Introduction

The IBM Behavior Based Client Insight for Wealth Management solution gives you the information and insight that you need to provide proactive service to your customers.

Audience

This guide is intended for administrators and users of the Behavior Based Client Insight for Wealth Management solution. It provides information on installation, configuration, and use of the Behavior Based Client Insight for Wealth Management solution.

Finding information

To find product documentation on the web, including all translated documentation, access IBM® Knowledge Center (http://www.ibm.com/support/knowledgecenter).

Accessibility features

Accessibility features help users who have a physical disability, such as restricted mobility or limited vision, to use information technology products. Some of the components included in the Behavior Based Client Insight for Wealth Management have accessibility features. For more information, see Accessibility features.

Behavior Based Client Insight for Wealth Management HTML documentation has accessibility features. PDF documents are supplemental and, as such, include no added accessibility features.

Forward-looking statements

This documentation describes the current functionality of the product. References to items that are not currently available may be included. No implication of any future availability should be inferred. Any such references are not a commitment, promise, or legal obligation to deliver any material, code, or functionality. The development, release, and timing of features or functionality remain at the sole discretion of IBM.

Samples disclaimer

Sample files may contain fictional data manually or machine generated, factual data compiled from academic or public sources, or data used with permission of the copyright holder, for use as sample data to develop sample applications. Product names referenced may be the trademarks of their respective owners. Unauthorized duplication is prohibited.

Overview

The IBM Behavior Based Client Insight for Wealth Management solution is a prepackaged solution that works with IBM Predictive Customer Insight.

The solution benefits wealth management firms and financial advisors by providing insights into the key behaviors of their client base. For each client, historical data analysis is used to generate a life event model and transaction history. The solution analyzes each client’s financial position and also develops a psychographic profile, which defines such things as the customer’s willingness to accept risk for the potential of higher returns. The solution uses this information to divide the client base into segments.

The Behavior Based Client Insight for Wealth Management solution provides advisors with insights into how customers behave in terms of funding frequency, advisor interactions, trading frequency, and asset allocation, as well as their views on risk, wealth preservation, and involvement. It predicts life events by analyzing historical data and enables portfolio managers to identify which customers will be receptive to investment advice at a particular time. It then helps portfolio managers to quickly and confidently recommend the best investment options to those customers.

Published APIs can be used to embed the solution into a company's existing portals and desktops. This provides advisers and clients with insights through familiar interfaces.

Summary of benefits

Interactive dashboards for easy access to generated insights

Greater insight into the key behaviors of your client base

Personalized portfolio advice

Tailored offers for products and services

Targeted promotions for financial products or services

Advanced risk and rebalancing alerts

Greater inflows and client-advisor interaction

Increase in cross-sell and assets invested

Improved customer experience and greater customer loyalty

Demonstration

For a brief demonstration of the solution, watch the following video.

https://youtu.be/2B3ILmvB-78

Installation and configuration

The IBM Behavior Based Client Insight for Wealth Management solution is for use with IBM Predictive Customer Intelligence.

The installation and configuration of the solution involves going to Passport Advantage, downloading the solution, and then configuring the various parts of it within your environment.

Prerequisites
Before you install the Behavior Based Client Insight for Wealth Management solution, there are prerequisites that must be met.

Contents of the download package
The Behavior Based Client Insight for Wealth Management package, which you download from Passport Advantage, contains several parts.

Overview of the installation and configuration process
This is an overview of the general tasks that you must complete to install and configure the Behavior Based Client Insight for Wealth Management solution.

Planning the installation and configuration of the solution
Before you install the solution, you must decide which computers you want the various parts of the solution to reside on.

Downloading the solution
You must download the Behavior Based Client Insight for Wealth Management solution from IBM Passport Advantage.

Modifying the sudoers file for the user who runs the installation
To run the Behavior Based Client Insight for Wealth Management installation, you must be either root user or have sudo permission on each node computer, and you must disable requiretty during the installation.

Computer firewall settings required for the installation
The solutions installer requires bidirectional communication with each node when you install a server component or server artifact component, and your firewall can interfere with this communication.

The solution installer
The solution installer provides automation that will help you to install the solution.

Creating the database
Before you can use the IBM Behavior Based Client Insight for Wealth Management solution, you must create a database.

Creating and running stored procedures
If, when you created the database for the Behavior Based Client Insight for Wealth Management solution, you chose not to load the sample data, then you need to run additional commands to create and run stored procedures. If you loaded the sample data, then you do not need to complete this task.

Importing IBM SPSS project streams, models, and rules
IBM SPSS project streams, models, rules, and other artifacts are contained in a repository export file (.pes) for the IBM Behavior Based Client Insight for Wealth Management solution. If you want to modify or view these artifacts, you must copy the export file to the computer where IBM SPSS Collaboration and Deployment Services Deployment Manager is installed, and open the file.

Configuring the data view for IBM SPSS models
To configure the data view, IBM® SPSS® Modeler must be connected to the IBM Behavior Based Client Insight for Wealth Management database through an ODBC data source connection.

Configuring ODBC for IBM SPSS Modeler Server
To use an ODBC data source with IBM SPSS Modeler Server on a Linux operating system, you must configure the environment.

Deploying the IBM Cognos content
For IBM Cognos Business Intelligence, you must catalog the database, create a data source connection, and then deploy the content files for the IBM Behavior Based Client Insight for Wealth Management solution.

Installing the EAR file
The EAR file contains the REST APIs that with which you can embed the IBM Behavior Based Client Insight for Wealth Management solution in your existing system.

Prerequisites

Before you install the Behavior Based Client Insight for Wealth Management solution, there are prerequisites that must be met.

The following conditions must be met before the application is installed:

IBM Predictive Customer Intelligence is installed and configured.

You have a Linux system that you can install to. The application installer works only on Linux.

You understand your existing environment so that you can determine where you want the various components of the application to reside.

You have administration rights and the ability to copy files between computers.

Parent topic: Installation and configuration

Contents of the download package

The Behavior Based Client Insight for Wealth Management package, which you download from Passport Advantage, contains several parts.

The following parts are included in the package:

Solution installer

DB2 databases

SPSS project streams, models, and rules

Cognos BI reports, Framework Manager models, and Dynamic cube models

EAR file that contains the REST APIs

Parent topic: Installation and configuration

Overview of the installation and configuration process

This is an overview of the general tasks that you must complete to install and configure the Behavior Based Client Insight for Wealth Management solution.

The following diagram provides a high-level overview of the installation and configuration process.

Diagram that provides an overview of the installation process rect 116,28,231,53 Downloading the solution rect 120,57,226,84 Computer firewall settings required for the installation rect 38,108,169,145 The solution installer rect 242,348,298,382 Creating the database rect 313,350,366,391 Deploying the IBM Cognos content rect 373,351,410,399 Importing IBM SPSS project streams, models, and rules rect 423,350,471,391 Configuring ODBC for IBM SPSS Modeler Server rect 478,356,526,411 Installing the EAR file

You need to complete the following tasks:

On the server from which you want to install the solution, download the tar.gz file from IBM Passport Advantage.

Plan where you want the various parts of the solution to reside.

Open the ports that are required for the installation. You can do this by running the firewall.sh script, manually opening the ports on the installation node, or disabling the firewall. You also must open ports on the other node computers that are used for the solution.

On the installation computer, run the setup.sh file to install the solution installer, and then run the solution installer to deploy files to the locations that you specify.

Create the DB2 database on the data node computer.

Import the SPSS project streams, models, and rules on the Predictive Analytics node computer.

Configure the data view for SPSS models on the Predictive Analytics node computer.

Install the Cognos content on the BI node computer.

Copy the Framework Manager model to a Microsoft Windows computer.

Install the EAR file.

Parent topic: Installation and configuration

Planning the installation and configuration of the solution

Before you install the solution, you must decide which computers you want the various parts of the solution to reside on.

You must ensure that no application is listening on the ports that are used during the installation of the solution.

Parent topic: Installation and configuration

Downloading the solution

You must download the Behavior Based Client Insight for Wealth Management solution from IBM Passport Advantage.

Before you begin

You must have a Passport Advantage account and be entitled to the package.

About this task

Passport Advantage contains package repositories. Instead of downloading the package from Passport Advantage and creating a repository locally, you access the repository on Passport Advantage.

Procedure

Set the Passport Advantage preference to connect to Passport Advantage.

Download the tar.gz file.

Move the tar file to the Linux node from which you want to run the installation.

Decompress the tar file.

Parent topic: Installation and configuration

Modifying the sudoers file for the user who runs the installation

To run the Behavior Based Client Insight for Wealth Management installation, you must be either root user or have sudo permission on each node computer, and you must disable requiretty during the installation.

To install with sudo user permissions, you must also add the user to the sudoers file.

Procedure

Log in as root user.

Enter the following command to open the sudoers file for editing:

visudo -f /etc/sudoers

Locate the following line:

                        Defaults requiretty

Press the I key to insert text.

Type a number sign ( #) in front of Defaults requiretty to comment out the line. For example, the line will appear as

                        #Defaults requiretty

If you run the installer as a user with sudo user permissions, go to the end of the file, and add the following line for your user:

                           username ALL=(ALL) NOPASSWD: ALL

Press the Esc key, and enter :wq to save and close the file.

Repeat these steps on each computer on which you install a Behavior Based Client Insight for Wealth Management node.

Parent topic: Installation and configuration

Computer firewall settings required for the installation

The solutions installer requires bidirectional communication with each node when you install a server component or server artifact component, and your firewall can interfere with this communication.

To ensure this bidirectional communication, you must configure your firewall settings for the installation. Alternatively, you can temporarily disable your firewall during the installation.

A script is provided to open all necessary ports on the installation node. You can either use the firewall.sh script, open the nodes manually, or disable the firewall settings.

In addition to opening ports on the installation node computer, you must open some ports on each computer to which content from the solution will be copied.

Disabling firewall settings during installation (optional)
If you choose to temporarily disable your computer's firewall during the installation of the IBM Behavior Based Client Insight for Wealth Management application, ensure that you enable the firewall on each node after the installation is complete

Configuring your firewall settings on the installation node computer by using the Firewall script
You can run the firewall.sh script to open the ports on the installation node.

Manually configuring firewall settings on the installation node
If you prefer not to use the firewall.sh script, you can manually configure your firewall settings on the installation node.

Configuring firewall settings on other computers that will contain content from the solution
On all node computers to which content from the application will be copied, the ports 443, 9683, and 8080 must be opened for the installation process.

Parent topic: Installation and configuration

Disabling firewall settings during installation (optional)

If you choose to temporarily disable your computer's firewall during the installation of the IBM Behavior Based Client Insight for Wealth Management application, ensure that you enable the firewall on each node after the installation is complete

About this task

Important: If you disable your computer's firewall during the installation, you must still ensure that the required ports are opened on each node computer when you restart your firewall.

Procedure

Log on to the computer as the root user or as a user with sudo permissions.

Type the following command:

/etc/init.d/iptables stop

Repeat these steps on each computer on which you install a node and on the server from which you are running the installation.

What to do next

After the installation is complete, on each node, type /etc/init.d/iptables start to restart your firewall.

Parent topic: Computer firewall settings required for the installation

Configuring your firewall settings on the installation node computer by using the Firewall script

You can run the firewall.sh script to open the ports on the installation node.

About this task

The firewall.sh script opens the following ports on the solution installer node:

8080 incoming

443 incoming

9683 incoming

22 outgoing

Procedure

Log on to the computer that contains the solution installer node as the root user or as a user with sudo permissions.

Back up your existing firewall settings by typing the following command: /etc/init.d/iptables save.

Run the firewall.sh script.

Parent topic: Computer firewall settings required for the installation

Manually configuring firewall settings on the installation node

If you prefer not to use the firewall.sh script, you can manually configure your firewall settings on the installation node.

Procedure

As the root user or as a user with sudo permissions, log on to the computer that contains the solution installer node .

Back up your existing firewall settings by typing the following command: . /etc/init.d/iptables save

On the computer that contains the solution installer node, add rules for the ports that are required by the installer by typing the following commands:

                           iptables -I INPUT -p tcp --dport 8080 -j ACCEPT
                              iptables -I OUTPUT -p tcp --sport 8080 -m state --state
                              RELATED,ESTABLISHED -j ACCEPT

                           iptables -I INPUT -p tcp --dport 443 -j ACCEPT
                              iptables -I OUTPUT -p tcp --sport 443 -m state --state
                              RELATED,ESTABLISHED -j ACCEPT

                           iptables -I INPUT -p tcp --dport 9683 -j ACCEPT
                              iptables -I OUTPUT -p tcp --sport 9683 -m state --state
                              RELATED,ESTABLISHED -j ACCEPT

                           iptables -I OUTPUT -p tcp --dport 22 -j ACCEPT
                              iptables -I INPUT -p tcp --sport 22 -m state --state
                              RELATED,ESTABLISHED -j ACCEPT

Parent topic: Computer firewall settings required for the installation

Configuring firewall settings on other computers that will contain content from the solution

On all node computers to which content from the application will be copied, the ports 443, 9683, and 8080 must be opened for the installation process.

Procedure

On each of the computers to which content for the solution will be copied, add rules for the ports that are required by the installer by typing the following commands:

                           iptables -I OUTPUT -p tcp --dport 443 -j ACCEPT
                              iptables -I INPUT -p tcp --sport 443 -m state --state RELATED,ESTABLISHED -j ACCEPT

                           iptables -I OUTPUT -p tcp --dport 9683 -j ACCEPT
                              iptables -I INPUT -p tcp --sport 9683 -m state --state RELATED,ESTABLISHED -j ACCEPT

                           iptables -I OUTPUT -p tcp --dport 8080 -j ACCEPT
                              iptables -I INPUT -p tcp --sport 8080 -m state --state RELATED,ESTABLISHED -j ACCEPT

Type the following command to save your firewall settings:

/etc/init.d/iptables save

Type the following command to restart your firewall:

/etc/init.d/iptables restart

Verify that your changes are applied by using the following command:

iptables -L

The added port appears in the list.

Repeat these steps on each computer on which you will install an IBM Behavior Based Client Insight for Wealth Management node.

Parent topic: Computer firewall settings required for the installation

The solution installer

The solution installer provides automation that will help you to install the solution.

The Industry Solution Installer runs on the latest versions of Chrome and Firefox. It is not tested on Safari and does not run on Internet Explorer.

The Industry Solution provides the following functionality:

Deploys the required files to defined nodes.

You can create a new configuration or start from a configuration that you previously saved.

Automated validation.

Electronic license acceptance.

Automated clean-up of installed files if the license is declined.

The setup.sh script installs nodejs, chef client, and chef server. It extracts and uploads the cookbooks and sets up everything so that chef can be used to install the software.

The setup.sh script takes the following five arguments (the fifth argument is a password, and if omitted, it will be prompted for):

sh setup.sh <username> <first name> <last name> <e-mail> <password>

Where

username: The user name to create in chef server (can be used to log on to the chef server) Must be all lowercase characters.

first name: The first name of the user that is created in chef (for display purposes only)

last name: The last name of the user that is created in chef (for display purposes only)

e-mail: A valid email address for notifications from chef server

password: A password for the user that is created in chef (this is optional: a password will be prompted for if it is not included on the command line).

Running the industry solution installer
You can now run the Industry Solution Installer to deploy files to the correct nodes. Nodes are the target computers on which you deploy the software.

Parent topic: Installation and configuration

Running the industry solution installer

You can now run the Industry Solution Installer to deploy files to the correct nodes. Nodes are the target computers on which you deploy the software.

Before you begin

Ensure that you know the following information for each of the nodes that are used in your solution and that you open the necessary ports.

Host name or IP address

Password of the root user ID or a user ID and password that has sudo permissions

About this task

In this task, you create an installation configuration for your solution by dragging nodes onto a canvas and then dragging components into the nodes.

Important: The Industry Solution Installer runs on the latest versions of Chrome and Firefox. It does not run on Internet Explorer and it is untested on Safari.

Procedure

Run the setup shell script setup.sh.

After the solution installer finishes, a browser window opens. If a browser is not installed, a URL is displayed from which you can connect to the UI. The default URL is https:// solution installer node/UI/Index.html .

Click New Configuration

If you have a configuration that was previously saved, you can start from that saved configuration.

From the Mandatory Content List pane, select Node.

A visual representation of an empty node is displayed on the canvas.

In the Property Editor, type a name for the node, and in the Host Name box, specify the server on which the node will be placed.

For example, you might create a node that is named BI Node.

Enter a user name and password for a user that has sudo access.

If you click Validate before all the mandatory content is added, a message indicates that the node is not valid. For example, if you validate the BI Node before adding Cognos BI Content, then a message indicates that the node is not yet valid.

From the Optional Content list, drag any content that you want to add into the appropriate node. The reason that this content is optional is because you might already have some of the necessary components installed in your environment.

For every node that you want to add content to, define the node and then drag content objects onto it.

Click Validate.

After all nodes are defined, the contents are specified, and each node is validated, click Run.

Parent topic: The solution installer

Creating the database

Before you can use the IBM Behavior Based Client Insight for Wealth Management solution, you must create a database.

About this task

You run one script to create the database, and then run another script to populate the database. The installer will have copied the compressed file to your data node computer.

Procedure

Log on to the data node computer as root user.

On the data node computer, if the compressed file is not already decompressed, then decompress it.

Open a terminal window, and go to the directory where the content files were decompressed.

Type the following command to change the permissions for the files: chmod -R 755 *sh

Change to the database instance owner.

For example, su db2inst1

In the decompressed folder, run the ./Install_DB.sh script.

Do one of the following actions:

If you want to load the sample data that is provided with the solution, then run the ./Load_Data.sh script.

If you do not want to load the sample data, then run the following SQL commands:

                              INSERT INTO PCIWLTH.CUSTOMER_SEGMENT_MINI_DIMENSION (ANNUAL_INCOME,
                                 IMPORTANCE_RATING,LOYALTY_RATING,MARKET,NET_WORTH,PERFORMANCE_RATING,PERFORMANCE_STATUS,
                                 POPULATION_DATE,POPULATION_DESCRIPTION,POPULATION_TIME,PRIMARY_BRANCH_PROXIMITY,
                                 PROFITABILITY,RELATIONSHIP_AGE,RELATIONSHIP_VALUATION,RETENTION_RATING,
                                 SATISFACTION_RATING,SEGMENT_NAME,SOURCE_SYSTEM_CODE,SOURCE_SYSTEM_NAME,
                                 UNIQUE_ID_IN_SOURCE_SYSTEM) values(null,null,null,null,null,null,null,null,
                                 null,null,null,null,null,null,null,null,'Conserver',null,null,null);
                                 INSERT INTO PCIWLTH.CUSTOMER_SEGMENT_MINI_DIMENSION (ANNUAL_INCOME,IMPORTANCE_RATING,
                                 LOYALTY_RATING,MARKET,NET_WORTH,PERFORMANCE_RATING,PERFORMANCE_STATUS,POPULATION_DATE,
                                 POPULATION_DESCRIPTION,POPULATION_TIME,PRIMARY_BRANCH_PROXIMITY,PROFITABILITY,
                                 RELATIONSHIP_AGE,RELATIONSHIP_VALUATION,RETENTION_RATING,SATISFACTION_RATING,
                                 SEGMENT_NAME,SOURCE_SYSTEM_CODE,SOURCE_SYSTEM_NAME,UNIQUE_ID_IN_SOURCE_SYSTEM)
                                 values(null,null,null,null,null,null,null,null,null,null,null,null,null,null,
                                 null,null,'Indifferent',null,null,null);
                                 INSERT INTO PCIWLTH.CUSTOMER_SEGMENT_MINI_DIMENSION (ANNUAL_INCOME,IMPORTANCE_RATING,
                                 LOYALTY_RATING,MARKET,NET_WORTH,PERFORMANCE_RATING,PERFORMANCE_STATUS,POPULATION_DATE,
                                 POPULATION_DESCRIPTION,POPULATION_TIME,PRIMARY_BRANCH_PROXIMITY,PROFITABILITY,
                                 RELATIONSHIP_AGE,RELATIONSHIP_VALUATION,RETENTION_RATING,SATISFACTION_RATING,
                                 SEGMENT_NAME,SOURCE_SYSTEM_CODE,SOURCE_SYSTEM_NAME,UNIQUE_ID_IN_SOURCE_SYSTEM) values
                                 (null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,
                                 null,'Straight Arrow',null,null,null);
                                 INSERT INTO PCIWLTH.CUSTOMER_SEGMENT_MINI_DIMENSION (ANNUAL_INCOME,IMPORTANCE_RATING,
                                 LOYALTY_RATING,MARKET,NET_WORTH,PERFORMANCE_RATING,PERFORMANCE_STATUS,POPULATION_DATE,
                                 POPULATION_DESCRIPTION,POPULATION_TIME,PRIMARY_BRANCH_PROXIMITY,PROFITABILITY,
                                 RELATIONSHIP_AGE,RELATIONSHIP_VALUATION,RETENTION_RATING,SATISFACTION_RATING,
                                 SEGMENT_NAME,SOURCE_SYSTEM_CODE,SOURCE_SYSTEM_NAME,UNIQUE_ID_IN_SOURCE_SYSTEM) values
                                 (null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,
                                 null,'Individualistic',null,null,null);
                                 INSERT INTO PCIWLTH.CUSTOMER_SEGMENT_MINI_DIMENSION (ANNUAL_INCOME,IMPORTANCE_RATING,
                                 LOYALTY_RATING,MARKET,NET_WORTH,PERFORMANCE_RATING,PERFORMANCE_STATUS,POPULATION_DATE,
                                 POPULATION_DESCRIPTION,POPULATION_TIME,PRIMARY_BRANCH_PROXIMITY,PROFITABILITY,
                                 RELATIONSHIP_AGE,RELATIONSHIP_VALUATION,RETENTION_RATING,SATISFACTION_RATING,
                                 SEGMENT_NAME,SOURCE_SYSTEM_CODE,SOURCE_SYSTEM_NAME,UNIQUE_ID_IN_SOURCE_SYSTEM) values
                                 (null,null,null,null,null,null,null,null,null,null,null,null,null,null,
                                 null,null,'Hedgers',null,null,null);
                                 INSERT INTO PCIWLTH.CUSTOMER_SEGMENT_MINI_DIMENSION (ANNUAL_INCOME,IMPORTANCE_RATING,
                                 LOYALTY_RATING,MARKET,NET_WORTH,PERFORMANCE_RATING,PERFORMANCE_STATUS,POPULATION_DATE,
                                 POPULATION_DESCRIPTION,POPULATION_TIME,PRIMARY_BRANCH_PROXIMITY,PROFITABILITY,
                                 RELATIONSHIP_AGE,RELATIONSHIP_VALUATION,RETENTION_RATING,SATISFACTION_RATING,
                                 SEGMENT_NAME,SOURCE_SYSTEM_CODE,SOURCE_SYSTEM_NAME,UNIQUE_ID_IN_SOURCE_SYSTEM) values
                                 (null,null,null,null,null,null,null,null,null,null,null,null,null,null,
                                 null,null,'Others',null,null,null);

What to do next

Verify that the tables are created and the data is successfully loaded into the input tables by checking the out.log file, which is in the db2inst1 home directory. In the log file, search for rows were rejected. The value should be zero. If it is not, there are data load issues.

Parent topic: Installation and configuration

Creating and running stored procedures

If, when you created the database for the Behavior Based Client Insight for Wealth Management solution, you chose not to load the sample data, then you need to run additional commands to create and run stored procedures. If you loaded the sample data, then you do not need to complete this task.

Before you begin

Save the SEGMENT_SUMMARY_PATTERN procedure into the SEGMENT_SUMMARY_PATTERN.sql file.

Procedure

Go to the following location to download the code for creating the stored procedures: Industry Solutions techdoc

Parent topic: Installation and configuration

Importing IBM SPSS project streams, models, and rules

IBM SPSS project streams, models, rules, and other artifacts are contained in a repository export file (.pes) for the IBM Behavior Based Client Insight for Wealth Management solution. If you want to modify or view these artifacts, you must copy the export file to the computer where IBM SPSS Collaboration and Deployment Services Deployment Manager is installed, and open the file.

Procedure

Copy the .pes file to the computer where IBM SPSS Collaboration and Deployment Services Deployment Manager is installed.

In IBM SPSS Collaboration and Deployment Service Deployment Manager, right-click Content Repository, and click Import.

Browse to the .pes file

Select the following options:

Resolve conflicts globally

Add new version of target item or rename source item, Use labels from source.

Continue import even if some objects cannot be imported due to locking conflicts.

Resolve Invalid Version Conflicts, Import.

Resource Definitions, Recommended - Import if there are no Duplicate ID conflicts or Duplicate Name conflicts.

Click OK.

Under Resource definitions / Credentials, create a new credential named IBMPCIWEATH .

Under Resource definitions / Data Sources, create a new data source named IBMPCIBNK.

After the .pes file is imported to the CnDS, open the job and click the step listed in the job.

Make sure that Modeler Server and Modeler Login fields are set to the correct definitions.

Click the ODBC Data Sources tab, and make sure that ODBC DSN and credentials are set to the newly created data source and credential.

Results

Content folders and resource definitions are added to the repository alongside any existing content.

Parent topic: Installation and configuration

Configuring the data view for IBM SPSS models

To configure the data view, IBM® SPSS® Modeler must be connected to the IBM Behavior Based Client Insight for Wealth Management database through an ODBC data source connection.

If your IBM Predictive Customer Intelligence environment uses the IBM SPSS Modeler client logged in to a Modeler server, complete the steps on the Predictive Analytics node computer (where IBM SPSS Modeler Server is installed).

If your IBM Predictive Customer Intelligence environment uses the IBM SPSS Modeler Client in a stand-alone environment, complete the steps on the client computer where IBM SPSS Modeler client is installed.

The Behavior Based Client Insight for Wealth Management solution uses the Analytic Data View and JDBC source connections for accessing IBM Analytical Decision Manager. When the Behavior Based Client Insight for Wealth Management files are imported through IBM SPSS Collaboration and Deployment Services Deployment Manager, a new version of the Enterprise View is created that contains the tables for the application.

Procedure

Catalog the database on the client computer.

Click Start > IBM DB2 > DB2COPY1 (Default) > DB2 Command Window - Administrator .

Enter the following command to catalog the database node:

                              db2 catalog tcpip node
                                 NODE_NAME remote
                                 data_node_name server
                                 PORT_NUMBER

NODE_NAME can be any value. PORT_NUMBER is 50000 by default.

Enter the following command to catalog the PCI database:

                              db2 catalog database
                                 WEALTH at node
                                 NODE_NAME authentication server

You must use the same node_name that you used in the db2 catalog database command.

Create an ODBC DSN to point to the Behavior Based Client Insight for Wealth Management database. The database account that is provided in the ODBC connection must be the same user that was used for creating the tables.

In the Deployment Manager client, under Resource Definitions, Credentials, open the db2admin credentials and update the user ID and password to the DB2® administrator account of the data node computer.

Parent topic: Installation and configuration

Configuring ODBC for IBM SPSS Modeler Server

To use an ODBC data source with IBM® SPSS® Modeler Server on a Linux operating system, you must configure the environment.

Procedure

Stop the IBM SPSS Modeler Server.

Go to the /root/SDAP71 directory.

The driver files are installed as part of the Behavior Based Client Insight for Wealth Management Server deployment.

Run the setodbcpath.sh script to update the ODBC path in the scripts.

Edit the odbc.sh script to add the definition for ODBCINI to the bottom of the script. For example:

                        ODBCINI=/usr/spss/odbc/odbc.ini; export ODBCINI

ODBCINI must point to the full file path of the odbc.ini file for IBM SPSS Modeler. The odbc.ini file lists the ODBC data sources that you want to connect to. A default odbc.ini file is installed with the drivers.

In the odbc.ini file, add the data source and specify the driver in the [ODBC Data Sources] section of the file.

For example, add the data source as:

                        [ODBC Data Sources]

                              WEALTH
                           =IBM DB2 ODBC Driver

In the odbc.ini file, create an ODBC data source connection for the WEALTH database.

For example, include the following content:

                        [

                              WEALTH
                           ]
                           Driver=/opt/ibm/db2/V10.1/lib64/libdb2o.so
                           DriverUnicodeType=1
                           Description=IBM DB2 ODBC Driver
                           ApplicationUsingThreads=1
                           AuthenticationMethod=0
                           BulkBinaryThreshold=32
                           BulkCharacterThreshold=-1
                           BulkLoadBatchSize=1024
                           CharsetFor65535=0
                           #Database applies to DB2 UDB only
                           Database=

                              WEALTH

                           DefaultIsolationLevel=1
                           DynamicSections=200
                           EnableBulkLoad=0
                           EncryptionMethod=0
                           FailoverGranularity=0
                           FailoverMode=0
                           FailoverPreconnect=0
                           GrantAuthid=PUBLIC
                           GrantExecute=1
                           GSSClient=native
                           HostNameInCertificate=
                           IpAddress=
                           IP_Address_of_DB_server
                           KeyPassword=
                           KeyStore=
                           KeyStorePassword=
                           LoadBalanceTimeout=0
                           LoadBalancing=0
                           LogonID=db2inst1
                           MaxPoolSize=100
                           MinPoolSize=0
                           Password=
                           password
                           PackageCollection=NULLID
                           PackageNamePrefix=DD
                           PackageOwner=
                           Pooling=0
                           ProgramID=
                           QueryTimeout=0
                           ReportCodePageConversionErrors=0
                           TcpPort=50000
                           TrustStore=
                           TrustStorePassword=
                           UseCurrentSchema=0
                           ValidateServerCertificate=1
                           WithHold=1
                           XMLDescribeType=-10

Important: You must use the driver library libdb2o.so with IBM SPSS Modeler.

Ensure that you set DriverUnicodeType=1 to avoid buffer overflow errors when you connect to the database.

If you are using the 64-bit version of IBM SPSS Modeler Server, define and export LD_LIBRARY_PATH_64 in the odbc.sh script:

                        if [ "$LD_LIBRARY_PATH_64" = "" ]; then
                           LD_LIBRARY_PATH_64=<
                           library_path>
                           else
                           LD_LIBRARY_PATH_64=<
                           library_path>:$LD_LIBRARY_PATH_64
                           fi
                           export LD_LIBRARY_PATH_64

Where < library_path> is the same as for the LD_LIBRARY_PATH definition in the script that was initialized with the installation path. For example, /usr/spss/odbc/lib.

Tip: You can copy the if and export statements for LD_LIBRARY_PATH in the odbc.sh file, append them to the end of the file. Then, replace the LD_LIBRARY_PATH strings in the newly appended if and export statements with LD_LIBRARY_PATH_64. Here is an example of the odbc.sh file for a 64-bit IBM SPSS Modeler Server installation:

                           if [ "$LD_LIBRARY_PATH" = "" ]; then
                              		LD_LIBRARY_PATH=/usr/spss/odbc/lib
                              else
                              		LD_LIBRARY_PATH=/usr/spss/odbc/lib:$LD_LIBRARY_PATH
                              fi
                              export LD_LIBRARY_PATH
                              if [ "$LD_LIBRARY_PATH_64" = "" ]; then
                              		LD_LIBRARY_PATH_64=/usr/spss/odbc/lib
                              else
                              		LD_LIBRARY_PATH_64=/usr/spss/odbc/lib:$LD_LIBRARY_PATH_64
                              fi
                              export LD_LIBRARY_PATH_64
                              ODBCINI=/usr/spss/odbc/odbc.ini; export ODBCINI

Ensure that you export LD_LIBRARY_PATH_64, and define it with the if loop.

Configure IBM SPSS Modeler Server to use the driver. Edit modelersrv.sh and add the following line immediately below the line that defines SCLEMDNAME:

                        . <
                           odbc.sh_path>

Where odbc.sh_path is the full path to the odbc.sh file.

For example: . /usr/spss/odbc/odbc.sh

Ensure that you leave a space between the first period and the file path.

Save modelersrv.sh.

Configure the IBM SPSS Modeler Server to use the ODBC wrapper named libspssodbc_datadirect.so.

Go to the /usr/IBM/SPSS/ModelerServer/16.0/bin directory.

Remove the existing libspssodbc.so soft link by using the following command:

                              rm –fr libspssodbc.so

Link the new wrapper to libspssodbc.so by using the following command:

                              ln –s libspssodbc_datadirect_utf16.so libspssodbc.so

Configure the db2cli.ini file in <db2 instance home> /sqllib/cfg/db2cli.ini to add the sections for each database.

                        [

                              WEALTH
                           ]
                           Database=

                              WEALTH

                           Protocol=TCPIP
                           DriverUnicodeType=1
                           Port=50000
                           Hostname=
                           ip or
                           hostname
                           UID=
                           username
                           PWD=
                           password

Save the odbc.ini file.

What to do next

To test the connection, do the following steps:

Restart IBM SPSS Modeler Server.

Connect to IBM SPSS Modeler Server from a client.

Add a database source node to the canvas.

Open the node and verify that you can see the data source names that you defined in the odbc.ini file.

For more information and troubleshooting tips for connecting to data sources, see the SPSS Modeler documentation (www.ibm.com/support/knowledgecenter/SS3RA7_16.0.0)

Parent topic: Installation and configuration

Deploying the IBM Cognos content

For IBM Cognos Business Intelligence, you must catalog the database, create a data source connection, and then deploy the content files for the IBM Behavior Based Client Insight for Wealth Management solution.

Preparing the IBM Cognos content
You must move the IBM® Cognos® Framework Manager content for the IBM Behavior Based Client Insight for Wealth Management solution to the Cognos Deployment folder.

Creating a data source connection to the database
You must create a data source connection to the Behavior Based Client Insight for Wealth Management database.

Deploying dynamic cubes for the sample data
If you choose to use the sample data for the Behavior Based Client Insight for Wealth Management solution, you must deploy the dynamic cubes.

Deploying the IBM Cognos reports
You must deploy the Behavior Based Client Insight for Wealth Management reports by using IBM Cognos Administration.

Parent topic: Installation and configuration

Preparing the IBM Cognos content

You must move the IBM® Cognos® Framework Manager content for the IBM Behavior Based Client Insight for Wealth Management solution to the Cognos Deployment folder.

The Solution installer supports copying content only to Linux nodes. If you are modifying the Framework Manager model, it must be copied to a Windows computer. Instructions for doing this by using the browser’s download capabilities are included in the SolutionInstaller > NodeRoot > Downloads > IS_<Solution_Name>_1.0 folder, in a file that is called IS_<SolutionName>_1.0_ClientDeployment Files.txt.

Procedure

Copy the IBM Cognos Framework Manager model file from the computer where you deployed the application to the computer where you installed IBM Cognos Framework Manager, and decompress the file.

The Framework Manager file is named \BI\Client_ClientInsight_CD.fmd .

If you want to edit the Framework Manager models, you must catalog the Behavior Based Client Insight for Wealth Management database on the computer where Framework Manager is installed.

Click Start > IBM DB2 > DB2COPY1 (Default) > DB2 Command Window - Administrator .

Enter the following command to catalog the database node: db2 catalog tcpip node NODE_NAME remote data_node_name server PORT_NUMBER NODE_NAME can be any value. PORT_NUMBER is 50000 by default.

Enter the following command to catalog the PCI database:

db2 catalog database WEALTH at node NODE_NAME authentication server.

You must use the same node_name that you used in the DB2 catalog database command.

Download IBM Cognos Business Intelligence Cube Designer software from Passport Advantage. Cube Designer requires Framework manager to be installed on the same node as well.

Go to the location where the installation files were downloaded and extracted and then double-click the issetup.exe file.

Select the language to use for the installation. The language that you select determines the language of the user interface. All supported languages are installed. You can change the user interface to any of the installed languages after installation.

On the Installation Location panel, select the location where Framework Manager is installed.

Accept the defaults on the Component Selection page.

Follow the directions in the installation wizard to copy the required files to your computer.

If you are installing in a directory that contains other IBM Cognos Business Intelligence components, you are prompted to create backup copies of the files that will be replaced.

In the Finish page of the installation wizard, complete the following steps:

If you want to see the log files, click View for the appropriate log file.

If you want to see late-breaking information about the product, select the check box for IBM Cognos Release Notes.

Click Finish.

Parent topic: Deploying the IBM Cognos content

Creating a data source connection to the database

You must create a data source connection to the Behavior Based Client Insight for Wealth Management database.

Procedure

Open a web browser.

Go to the IBM® Cognos® BI portal URL.

For example, go to http://bi_node_name/ibmcognos/.

On the Welcome page, click Administer IBM Cognos Content .

Click the Configuration tab, and click Data Source Connections .

Click New Data Source.

In the Name box, type WEALTH, and then click Next.

In the connection page, select IBM DB2, ensure that Configure JDBC connection is selected, and click Next.

In the DB2 database name field, type WEALTH.

Leave DB2 connect string blank.

Under Signons, select both Password and Create a signon that the Everyone group can use, and then type the user ID and password for the DB2® instance owner user that you used to create the database, and click Next.

Tip: To test whether the parameters are correct, click Test the connection . After you test the connection, click OK to return to the connection page.

In the Server name box, enter the name or IP address of your data node computer.

In the Port number box, enter the DB2 port number. The default is 50000.

In Database name, type WEALTH.

Tip: To test whether the parameters are correct, click Test the connection . After you test the connection, click OK to return to the connection page.

Click Finish.

Parent topic: Deploying the IBM Cognos content

Deploying dynamic cubes for the sample data

If you choose to use the sample data for the Behavior Based Client Insight for Wealth Management solution, you must deploy the dynamic cubes.

Before you begin

You must create a data source connection to the database that contains the data by completing the steps in the following link:

Creating a data source in the Cognos Administration portal (http://www.ibm.com/support/knowledgecenter/SSEPGG_10.5.0/com.ibm.dwe.migrate.doc/data_source_cs_cognos.html)

Procedure

Add the dispatcher and gateway information of the computer in which the data source is created to the Cognos configuration of the local system from which cubes are published. This is the computer on which the cube designer is installed.

The dispatcher and gateway URI are shown below:

Gateway URI: http://localhost:80/ibmcognos/cgi-bin/cognos.cgi

Dispatcher URI: http://localhost:9080/p2pd/servlet/dispatch

Open IBM Cognos Cube Designer and click Open Existing to load the .fmd file.

In Cognos Cube Designer, click the data source to display the Properties window.

Change the Content Manager data source to the name of the data source connection that is created in Cognos Server, and change the schema name to the name under which data objects are created.

Right-click the data source and refresh the metadata.

To publish the cubes, right-click a package and click Publish. The following message appears.

Click OK.

Cubes are published to the Cognos server. A message appears to indicate that the cubes are published successfully.

Check the status of the cube in Cognos Administration under Data Sources . Initially all cubes will have a status of Unknown.

Select all the cubes that you published, right-click the drop-down menu next to the cube, select Add data store to server group, and click OK. Now the cubes move to an Unavailable status.

From the same drop-down menu, and click Start. If an error message is encountered when you publish the cube, check the log file in C:\Program Files\IBM\cognos\c10_64\logs\XQE on the computer in which IBM Cognos BI is installed, and open the latest log file with a file name that starts with xqelog followed by a date and time stamp. The error message in the log will look similar to the following message:

Parent topic: Deploying the IBM Cognos content

Deploying the IBM Cognos reports

You must deploy the Behavior Based Client Insight for Wealth Management reports by using IBM® Cognos® Administration.

Procedure

Open a web browser.

Go to the IBM Cognos BI portal URL.

On the Welcome page, click Administer IBM Cognos Content .

On the Configuration tab, click Content Administration .

On the toolbar, click the New Import button.

In the Deployment Archive pane, select the deployment archive, and click Next.

The deployment archive is named Wealth_CustomerInsight_BIContent.zip .

In the Specify a name and description pane, accept the default or enter a new name, and click Next.

In the Select the public folders and directory content pane, select all of the packages in the table, leave the Options as default, and click Next.

Note: Ensure that the Disable after import option is cleared.

In the Specify the general options pane, accept the defaults, and click Next.

On the Review the summary page, click Next.

On the Select an action page, select Save and run once , and click Finish.

On the Run with options page, accept the defaults, and click Run, and then click OK.

Select View the details of this import after closing this dialog and click OK.

In IBM Cognos Administration, click the Home button.

The Behavior Based Client Insight for Wealth Management content is available in Public Folders in the Wealth_CustomerInsight_BIContent.zip file.

Rave libraries are also available as part of the solution. They are packaged in the Wealth_CustomerInsight_Rave.zip file.

Go to the IBM Cognos BI portal URL.

For example, go to http://bi_node_name/ibmcognos/.

On the Welcome page, click Administer IBM Cognos Content.

On the libraries tab, click the Import button.

Select the Wealth_CustomerInsight_Rave.zip file, and click Import.

Parent topic: Deploying the IBM Cognos content

Installing the EAR file

The EAR file contains the REST APIs that with which you can embed the IBM Behavior Based Client Insight for Wealth Management solution in your existing system.

Procedure

Log in to the WebSphere Application Server Network Deployment administrative console.

Load the IS_Wealth_Solution_EAR.ear file into one of the WebSphere Application Server nodes, either the BI node or the Analytics node.

Install the application through WebSphere Application Server.

Click the Applications link, and then click Install New Application .

From the Specify the EAR, WAR, JAR, or SAR module to upload and install page, select Local file system, and then browse to the location on your system of the EAR file.

Start the installed application, IS_Wealth .

Parent topic: Installation and configuration

Reports

The Behavior Based Client Insight for Wealth Management solution provides reports with dashboard templates, sample predictive models, and application interfaces for integration.

The reports provide information about clients, including financial positions and psychographic profiles based on life event models and transaction history.

The following analytics reports are provided:

Executive Dashboard

Customer Summary

Customer Details

Executive Dashboard
When you log in to the solution, the Executive Dashboard provides analysis for each segment. Customers are divided into segments based on their investment characteristics. You can view each segment's total net worth and its percentage of the total net worth of your customer base.

Customer Summary report
The Customer Summary report provides a list of customers from the segment and net worth that you select.

Customer Details report
The Customer Details report provides detailed information for the selected customer.

Executive Dashboard

When you log in to the solution, the Executive Dashboard provides analysis for each segment. Customers are divided into segments based on their investment characteristics. You can view each segment's total net worth and its percentage of the total net worth of your customer base.

To view the analysis for a segment, click the name of the segment. The following screen capture shows the executive dashboard with the Conserver segment.

The Change Point Analysis charts provide the average cost and age of life events, and the average net worth, for the selected segment. You can view age and cost of the life event when you hover your mouse pointer over a bubble. You can use the Networth box to filter for customer groups that have different net worths.

The Segment Board section of the dashboard provides investment characteristics of each segment. It shows how customers of the selected segment distribute their assets, fund their portfolios, interact with the company, and trade securities. Specific information is provided when you hover your mouse pointer over each pie slice.

Parent topic: Reports

Customer Summary report

The Customer Summary report provides a list of customers from the segment and net worth that you select.

The Life Event table indicates life events that have already taken place for each client. The current or most recent life event is listed in the chart and each life event that has occurred is indicated by a check mark. You can click a life event in the Life Event list to filter the clients. For example, if you choose Job Change as the life event, only those customers whose current or most recent life event is job change are shown on the page. Suggested actions related to the selected life event are also shown.

The following screen capture shows the report when the Conserver segment and Mass Affluent net worth are selected.

Parent topic: Reports

Customer Details report

The Customer Details report provides detailed information for the selected customer.

To access the Customer Details report, on the Customer Summary report, click the name of the customer who you want to get further information on.

In the Customer Details section of the report, the Last Met field indicates the date of the most recent meeting with the client. The Follow Up field indicates the recent life event to address at the next meeting with the client.

The Channel Usage section shows the number of times each channel has been used for communication with the company.

The Social Interaction section shows the number of times the customer mentioned your wealth management firm in social interactions.

The Change Point Analysis indicates life events that the customer has experienced.

The following screen capture shows the report for a selected customer.

Parent topic: Reports

Using the Behavior Based Client Insight for Wealth Management solution

The solution provides reporting and dashboard templates and sample predictive models.

For each client, historical data analysis is used to generate a life event model and transaction history. The solution analyzes each client’s financial position and also develops a psychographic profile, which defines such things as the customer’s willingness to accept risk for the potential of higher returns. The solution uses this information to divide the client base into segments and provide predictions about life events. It provides wealth advisors with suggestions for the best actions to take for each client.

An example of the Behavior Based Client Insight for Wealth Management solution in action
This fictitious example shows how the application provides insight that helps an investment advisor to provide the best possible recommendations for a client.

An example of the Behavior Based Client Insight for Wealth Management solution in action

This fictitious example shows how the application provides insight that helps an investment advisor to provide the best possible recommendations for a client.

Christina Sizemore is a financial advisor with 60 clients under management. Her employer, ABC Finance, a traditional full-service wealth management firm, has recently experienced flat revenue growth and is losing clients to the new internet-based robo-advisor firms. ABC Finance has been in the wealth management business for 50 years and has accumulated a large amount of historical client data, including client profile, interaction, and transaction data. The company recognizes that the accumulated information is a valuable resource that can provide them with competitive advantage if they can use it to gain insight into their clients’ behaviors. ABC Finance decides to invest in the IBM Behavior Based Client Insight for Wealth Management solution.

Christina arrives at the office and looks at her calendar to view her customer appointments for the day. To prepare for the customer appointments, Christina starts the IBM wealth management portlet on her desktop and sees the following view.

The Executive Dashboard shows the various behavioral segments: Conserver, Indifferent, Straight Arrow, and so on, followed by a change point analysis and average net worth for the segment that is selected. For the chosen segment, this view shows the distribution of four types of data – Assets, Funding, Interactions, and Trading.

Christina clicks the various behavioral segments to view the segment-specific information. Christina’s first customer of the day is Anika Paul. Christina has been helping Anka manage her investments for the past five years and is familiar with the account. She looks at Anika’s profile and sees that Anika is in the Conserver behavioral segment, which is shown in the following image. Conservers invest in assets that have very little risk of losing value. Consequently, their investments tend to grow slowly. They are comfortable investing in government bonds, money market funds, guaranteed investment certificates, and sometimes high-quality dividend-paying stocks.

To prepare for her appointment with Anika, Christina wants to understand the Conserver segment better. She drills down into the segment to display the following dashboard.

The dashboard displays the names of customers in this segment who are nearing life events. Christina selects Anika's name in the list of customers to display the following view that suggests specific actions to discuss with Anika.

When Anika arrives for her appointment, Christina is able to discuss a financial plan that Anika is comfortable with and provides options for investments, retirement savings, and health insurance.

Parent topic: Using the Behavior Based Client Insight for Wealth Management solution

Accessibility features

Accessibility features help users who have a physical disability, such as restricted mobility or limited vision, to use information technology products.

For information about the commitment that IBM has to accessibility, see the IBM Accessibility Center (www.ibm.com/able).

IBM Cognos HTML documentation has accessibility features. PDF documents are supplemental and, as such, include no added accessibility features.

Report output

In IBM Cognos Administration, you can enable system-wide settings to create accessible report output. For more information, see the IBM Cognos Business Intelligence Administration and Security Guide . In IBM Cognos Report Studio, you can enable settings to create accessible output for individual reports. For more information, see the IBM Cognos Report Studio User Guide . You can access the previously mentioned documents at IBM Knowledge Center (http://www.ibm.com/support/knowledgecenter).

Troubleshooting

This section contains troubleshooting information for the Behavior Based Client Insight for Wealth Management installation.

Troubleshooting a problem
Troubleshooting is a systematic approach to solving a problem. The goal of troubleshooting is to determine why something does not work as expected and how to resolve the problem.

After running the solution installer, Chef processes are still running after executing the cleanup.sh script
After running the cleanup.sh script on the solution installer node, all chef processes should be terminated. However, in some cases chef processes are still running.

Troubleshooting a problem

Troubleshooting is a systematic approach to solving a problem. The goal of troubleshooting is to determine why something does not work as expected and how to resolve the problem.

Review the following table to help you or customer support resolve a problem.

Table 1. Troubleshooting actions and descriptions .

Actions | Description
A product fix might be available to resolve your problem. | Apply all known fix packs, or service levels, or program temporary fixes (PTF).
Look up error messages by selecting the product from the IBM Support Portal, and then typing the error message code into the Search support box (http://www.ibm.com/ support/entry/portal/). | Error messages give important information to help you identify the component that is causing the problem.
Reproduce the problem to ensure that it is not just a simple error. | If samples are available with the product, you might try to reproduce the problem by using the sample data.
Ensure that the installation successfully finished. | The installation location must contain the appropriate file structure and the file permissions. For example, if the product requires write access to log files, ensure that the directory has the correct permission.
Review all relevant documentation, including release notes, technotes, and proven practices documentation. | Search the IBM Knowledge Center to determine whether your problem is known, has a workaround, or if it is already resolved and documented.
Review recent changes in your computing environment. | Sometimes installing new software might cause compatibility issues.

If the items in the table did not guide you to a resolution, you might need to collect diagnostic data. This data is necessary for an IBM® technical-support representative to effectively troubleshoot and assist you in resolving the problem. You can also collect diagnostic data and analyze it yourself.

Troubleshooting resources
Troubleshooting resources are sources of information that can help you resolve a problem that you are having with an IBM product.

Parent topic: Troubleshooting

Troubleshooting resources

Troubleshooting resources are sources of information that can help you resolve a problem that you are having with an IBM® product.

Support Portal

The IBM Support Portal is a unified, centralized view of all technical support tools and information for all IBM systems, software, and services.

The IBM Support Portal lets you access all the IBM support resources from one place. You can tailor the pages to focus on the information and resources that you need for problem prevention and faster problem resolution. Familiarize yourself with the IBM Support Portal by viewing the demo videos (https://www.ibm.com/blogs/SPNA/entry/the_ibm_support_portal_videos).

Find the content that you need by selecting your products from the IBM Support Portal (http://www.ibm.com/support/entry/portal).

Before contacting IBM Support, you will need to collect diagnostic data (system information, symptoms, log files, traces, and so on) that is required to resolve a problem. Gathering this information will help to familiarize you with the troubleshooting process and save you time.

Service request

Service requests are also known as Problem Management Reports (PMRs). Several methods exist to submit diagnostic information to IBM Software Technical Support.

To open a PMR or to exchange information with technical support, view the IBM Software Support Exchanging information with Technical Support page (http://www.ibm.com/software/support/exchangeinfo.html).

Fix Central

Fix Central provides fixes and updates for your system's software, hardware, and operating system.

Use the pull-down menu to navigate to your product fixes on Fix Central (http://www.ibm.com/systems/support/fixes/en/fixcentral/help/getstarted.html). You may also want to view Fix Central help.

IBM developerWorks

IBM developerWorks® provides verified technical information in specific technology environments.

As a troubleshooting resource, developerWorks provides easy access to the most popular practices, in addition to videos and other information: developerWorks (http://www.ibm.com/developerworks).

IBM Redbooks

IBM Redbooks® are developed and published by the IBM International Technical Support Organization, the ITSO.

IBM Redbooks (http://www.redbooks.ibm.com) provide in-depth guidance about such topics as installation and configuration and solution implementation.

Software support and RSS feeds

IBM Software Support RSS feeds are a quick, easy, and lightweight format for monitoring new content added to websites.

After you download an RSS reader or browser plug-in, you can subscribe to IBM product feeds at IBM Software Support RSS feeds (https://www.ibm.com/software/support/rss).

Log files

Log files can help you troubleshoot problems by recording the activities that take place when you work with a product.

Error messages

The first indication of a problem is often an error message. Error messages contain information that can be helpful in determining the cause of a problem.

Parent topic: Troubleshooting a problem

After running the solution installer, Chef processes are still running after executing the cleanup.sh script

After running the cleanup.sh script on the solution installer node, all chef processes should be terminated. However, in some cases chef processes are still running.

If you try to run the solution installer setup.sh file again, you may receive a message indicating that required ports are in use, or creation of the chef user might fail.

The solution to this issue is to reboot the installation node after having run "cleanup.sh".

Note: This only applies to the solution installer node and not the client/target nodes.

Parent topic: Troubleshooting

Client data ingestion: SPSS streams

The IBM solution works with IBM Predictive Customer Insight.

Two SPSS streams are provided that enable the client to ingest data into the solution database. The client data in a predefined Comma Separated Value (CSV) file format is provided to the stream as input, and the stream extracts the data from the CSV file and inserts the records into relevant tables in the solution database. The client data can be of the following types:

Customer demographic details (including contact information)

Customer transactions data

There are two separate streams for each of the above types of data.

Purpose of the SPSS streams
The SPSS streams act as an important ETL process between the client data and the solution database.

CSV formats
This section provides the CSV formats for both customers and transactions.

Prerequisites for the ETL streams
There are no prerequisites for the Wealth Customer ETL stream. However, there are prerequisites for the Wealth Transaction ETL stream.

End result of using the ETL streams
This page illustrates the results of the ETL streams.

Purpose of the SPSS streams

The SPSS streams act as an important ETL process between the client data and the solution database.

They perform the following operations:

Extract raw client data from the CSV files

Transform data according to the solution needs (such as eliminating the unwanted spaces)

Filter the transformed data and load it into relevant tables in the solution database

By using the streams, clients can easily ingest large sets of data related to customers and their transactions.

The following two streams are provided:

Wealth Customer ETL – Customer demographic details (including contact information)

Wealth Transaction ETL - Customer transactions data

Parent topic: Client data ingestion: SPSS streams

CSV formats

This section provides the CSV formats for both customers and transactions.

Important: In the following CSV files, you must replace the NULL values by default values for the streams to work correctly.

Format for the CSV file input to the Wealth Customer ETL

                     "CITY","COUNTRY","EMAIL","POSTCODE_AREA","STATE","STREET","FULL_TELEPHONE_NUMBER",
                        "BIRTH_DATE","FULL_NAME","PHOTO","MARITAL_STATUS","EDUCATION"

                     CITY > city where the customer lives
                        COUNTRY > country where the customer lives
                        EMAIL > customer's email address
                        POSTCODE_AREA > postal code of the area where the customer lives
                        STATE > state where the customer lives
                        STREET > street address where the customer lives
                        FULL_TELEPHONE_NUMBER > customer's telephone number
                        BIRTH_DATE > customer's date of birth
                        FULL_NAME > customer's full name
                        PHOTO > Link to the customer's photo (URL)
                        MARITAL_STATUS > customer's marital status
                        EDUCATION > customer's most recently completed education

Sample CSV file for customers

                     "CITY","COUNTRY","EMAIL","POSTCODE_AREA","STATE","STREET",
                        "FULL_TELEPHONE_NUMBER","BIRTH_DATE","FULL_NAME","PHOTO","MARITAL_STATUS","EDUCATION"

                     "Jamaica","US","vereen@yahoo.com","04017","MA","11 smith street","8572055850", "1986-06-17",
                        "Alfonso Osborne","http://hostname/ibmcognos/samples/images/Customer/3.png","Married","Graduate"

Format for the CSV file input to the Wealth Transaction ETL

                     "CALENDAR_DATE","NAME","FULL_NAME","BIRTH_DATE","PHOTO",
                        "MARITAL_STATUS","EDUCATION","DESCRIPTION","NAME","NET_CASH_FLOW_AMOUNT"

Notice that in the above format, there are two instances of "NAME".

The first instance of “NAME” relates to the channel name.

The second instance of “NAME” relates to the transaction type name. In the stream this is referred to as NAME(1).

                     CALENDAR_DATE > date of the transaction
                        NET_CASH_FLOW_AMOUNT > transaction amount
                        BIRTH_DATE > customer's date of birth
                        FULL_NAME > customer's full name
                        PHOTO > Link to the customer's photo(URL)
                        MARITAL_STATUS > customer's marital status
                        EDUCATION > customer's most recently completed education.
                        DESCRIPTION > relates to geographic area description

Sample CSV file (transactions)

In the following sample there are three transactions related to the customer provided in the customer's csv file above.

                     "CALENDAR_DATE","NAME","FULL_NAME","BIRTH_DATE","PHOTO",
                        "MARITAL_STATUS","EDUCATION","DESCRIPTION","NAME","NET_CASH_FLOW_AMOUNT"

                     "1980-04-07","In Person","Alfonso Osborne","1986-06-17",
                        "http://hostname/ibmcognos/samples/images/Customer/3.png",
                        "Married","Graduate","Remote","Credits","90937.90674"

                     "1985-12-07","In Person","Alfonso Osborne","1986-06-17",
                        "http://hostname/ibmcognos/samples/images/Customer/3.png",
                        "Married","Graduate","Remote","Credits","124841.22253"

                     "1985-12-07","In Person","Alfonso Osborne","1986-06-17",
                        "http://hostname/ibmcognos/samples/images/Customer/3.png",
                        "Married","Graduate","Remote","Money Market","-4815.30429"

Parent topic: Client data ingestion: SPSS streams

Prerequisites for the ETL streams

There are no prerequisites for the Wealth Customer ETL stream. However, there are prerequisites for the Wealth Transaction ETL stream.

The following perquisites are required for the Wealth Transaction ETL to work:

The Wealth Customer ETL stream must be run first to load the customer details into the INDIVIDUAL_CUSTOMER_DIMENSION table. This is because the Wealth Transaction ETL stream uses this table to establish relationship between the customer and his transactions.

Each customer is identified by a distinct key: (FULL_NAME, BIRTH_DATE, PHOTO, MARITAL_STATUS, EDUCATION) . A combination of all these values would uniquely identify a customer. Using this key as a reference, only transactions related to the customers that are loaded through the Wealth Customer ETL stream must be provided in the CSV input file for the Wealth Transaction ETL stream.

The CALENDAR_DATE provided in the CSV input file must be present in the CALENDAR_DATE_DIMENSION table of the Wealth solution database.

The channel name (NAME) provided in the CSV input file must be present in the CHANNEL_DIMENSION table of the Wealth solution database.

The transaction type name (NAME(1))provided in the CSV input file must be present in the TRANSACTION_DIMENSION table of the Wealth solution database.

The geographic area description (DESCRIPTION) provided in the CSV input file must be present in the GEOGRAPHIC_AREA_DIMENSION table of the Wealth solution database.

Parent topic: Client data ingestion: SPSS streams

End result of using the ETL streams

This page illustrates the results of the ETL streams.

Wealth Customer ETL

The stream, shown in the following image, filters and loads the input data from the CSV file.

"BIRTH_DATE","FULL_NAME","PHOTO","MARITAL_STATUS","EDUCATION" are inserted into the INDIVIDUAL_CUSTOMER_DIMENSION table. The combination of all these columns forms a distinct key that identifies a particular customer.

"CITY","COUNTRY","EMAIL","POSTCODE_AREA","STATE","STREET","FULL_TELEPHONE_NUMBER" are inserted into the CONTACT_INFORMATION_DIMENSION table. The combination of all these columns forms a distinct key that identifies a particular contact.

After that, the stream establishes a relationship between a customer and his contacts. This relationship information is stored in the INVOLVED_PARTY_CONTACT_INFORMATION_BRIDGE table.

In total, three tables ( INDIVIDUAL_CUSTOMER_DIMENSION, CONTACT_INFORMATION_DIMENSION and INVOLVED_PARTY_CONTACT_INFORMATION_BRIDGE) are populated as a result of running this stream.

Wealth Transaction ETL

The stream, shown in the following image, filters and loads the input data from the CSV file.

The stream performs the following steps:

Merges the data from the transactions csv file with the CALENDAR_DATE_DIMENSION table using the CALENDAR_DATE field and picks up the corresponding CALENDAR_DATE_DIMENSION_IDs.

Merges the data from the transactions csv file with the CHANNEL_DIMENSION table using the NAME field and picks up the corresponding CHANNEL_DIMENSION_IDs.

Merges the data from the transactions csv file with the TRANSACTION_DIMENSION table using the NAME field and picks up the corresponding TRANSACTION_DIMENSION_IDs.

Merges the data from the transactions csv file with the INDIVIDUAL_CUSTOMER_DIMENSION table using the key: ( FULL_NAME, BIRTH_DATE, PHOTO, MARITAL_STATUS, EDUCATION ) and picks up the corresponding INDIVIDUAL_CUSTOMER_DIMENSION_IDs.

Finally, it inserts the above primary keys along with the corresponding NET_CASH_FLOW_AMOUNT field into the TRANSACTION_FACT table as a result of running this stream.

Parent topic: Client data ingestion: SPSS streams

JDBC connection to WebSphere Application Server

The IBM® Behavior Based Client Insight for Wealth Management solution provides a set of RESTful APIs to allow you to create applications to programmatically execute operations. If you're using Websphere Application Server as your database, you must set up a JDBC connection to the server. The provided instructions and procedures are based on WAS v8.5.5.

To set up your JDBC connection, you must complete the following tasks, in order:

Configure the JDBC provider

Create an authentication alias

Configure the JDBC data source

Configure the JDBC provider

In this section, you'll set up the DB2 using IBM J2CC Driver (XA) for your JDBC connection.

Log in to the WebSphere Application Server console.

Navigate to the JDBC providers section.

If you already have the DB2 Using IBM J2CC Driver (XA) setup, proceed to Create an authentication alias. Otherwise, create a new JDBC provider by clicking New..., as highlighted in the image.

Enter the JDBC provider details as per the following image, then click Next.

Use the default Class path settings, as shown in the following image, and click Apply.

Specify the directory location where the db2jcc4.jar and db2jcc_license_cisuz.jar files are present on the server. The jar files are included in the WebSphere installation package.

Click Next.

Review the summary page and click Finish.

Create an authentication alias

In this section, you'll specify the authorized users (IDs and passwords) who have access to the database.

From the WAS console, go to the Global Security section and click J2C authentication data.

Click New...

In the next page, enter an arbitrary Alias name. Enter the User ID and Password that you use to connect to your database. The Description field is optional.

Click Apply and then OK.

Configure the JDBC data source

In this section, you'll configure your JDBC data source and test the connection.

From the WAS console, go to the Data Sources section and click New...

In the next page, enter an arbitrary Data source name.

Enter "jdbc/pciwlth" in the JNDI name field. The JNDI name is used by the REST API to connect to the database.

Click Next.

Choose Select an existing JDBC provider. Select the JDBC provider that you created in the first task. Click Next.

Provide the Database name that you wish to access. Provide the Server name that you wish to connect to and the Port number . The remaining details can retain their default settings. Click Next.

Set the authentication alias that you created earlier, then click Next.

Verify the summary information and click Finish.

When you're prompted to save the server configuration changes, click Save.

Select the JDBC data source that you have created and click Test connection . If successful, a message will appear indicating that a successful connection was established.

REST API reference

The IBM® Behavior Based Client Insight for Wealth Management solution provides a set of RESTful APIs to allow you to create applications to programmatically invoke operations and extend industry solutions capabilities.

The representational state transfer (REST) API provides a lightweight interface that uses HTTP requests to communicate to the server. The APIs can be used to extract and aggregate data for further processing in other systems. All supported resources are listed in the following sections and are actionable using a GET operation.

If a request returns an empty JSON response, this indicates that there were no records in the database that matched the parameters specified in the request. If an error occurred, one of the following error codes may be returned:

IS_WEALTH_ERR00

Unknown error

IS_WEALTH_ERR01

A customerid is required.

IS_WEALTH_ERR02

The specified customer_id parameter is invalid.

IS_WEALTH_ERR03

The specified pagestart parameter is invalid.

IS_WEALTH_ERR04

The specified pagesize is invalid.

IS_WEALTH_ERR05

Database error

IS_WEALTH_ERR06

A segment parameter is required.

IS_WEALTH_ERR07

A networth parameter is required.

The Behavioral Segmentation REST API allows you to create your own segments and associated segment definitions. This is a generic API that applies to Behavior Based Customer Insight for Banking, Behavior Based Client Insight for Wealth Management, and Behavior Based Customer Insight for Insurance. The following error codes apply to the Behavioral Segmentation REST API.

IS_SEGMENTATION_ERR00

Unknown error

IS_SEGMENTATION_ERR01

Could not parse the JSON input. Please check the JSON input and try again.

IS_SEGMENTATION_ERR02

Failed to insert the segment into the database.

IS_SEGMENTATION_ERR03

Category does not exist in the data dictionary.

IS_SEGMENTATION_ERR06

Database error.

IS_SEGMENTATION_ERR15

No records found.

IS_SEGMENTATION_ERR16

Failed to create batch statements for insert.

IS_SEGMENTATION_ERR17

Failed to insert the segment definitions into the database.

IS_SEGMENTATION_ERR18

The industry parameter is required.

IS_SEGMENTATION_ERR19

The industry parameter is incorrect. Supported values are banking, wealth, or insurance.

IS_SEGMENTATION_ERR20

The segmented parameter is required.

Errors will include a "flag: false" indicator in the response body.

The URLs used to issue REST commands must adhere to the US-ASCII character set, including reserved and restricted characters. For example, a space must be URL-encoded as "%20".

Note: For additional information on the API, please reference the provided Javadocs that are included in the installation package. Information on the data model is posted at the following location:

Industry Solutions techdoc http://www.ibm.com/support/docview.wss?uid=swg27046047

Customizing segments
The Behavioral Segmentation REST API allows you to create and delete custom segments and their associated segment definitions.

/iswealth/customer/bi/{customerid}/peergroup
This resource returns the peer group information for a specific customer.

/iswealth/customer/bi/segments
This resource provides the details of each segment and their distribution. Examples of segments include Accumulators, Preservers, Independents, Followers, or Straight Arrows.

/iswealth/customer/bi/{segment_name}/assets
This resource provides the assets distribution information for particular segments. In addition to segments, the JSON response can be further filtered by networth and customer id..

/iswealth/customer/bi/{segment_name}/trading
This resource provides the trading distribution information for particular segments. In addition to segments, the JSON response can be further filtered by networth and customer id..

/iswealth/customer/bi/{segment_name}/funding
This resource provides the funding distribution information for particular segments. In addition to segments, the JSON response can be further filtered by networth and customer id..

/iswealth/customer/bi/{segment_name}/interactions
This resource provides the interactions distribution information for particular segments. In addition to segments, the JSON response can be further filtered by networth and customer id..

/iswealth/customer/bi/activities
This resource provides the insights into activities that were performed by customers, filtered by segment or net worth, and related to particular life events, such as getting married or buying a new house.

/issegmentation/bs/{industry}/segment/create
This resource allows you to create a new segment with an associated segment definition; defined in JSON format. If the specified segment name already exists, then the segment definition will be updated by the provided JSON.

/issegmentation/bs/{industry}/segment/delete/{segment_id}
This resource allows you to create a new segment with an associated segment definition; defined in JSON format. If the specified segment name already exists, then the segment definition will be updated by the provided JSON.

Customizing segments

The Behavioral Segmentation REST API allows you to create and delete custom segments and their associated segment definitions.

Segment data dictionary

Segment definitions must conform to the behavioral categories defined in the segment data dictionary. All categories in the data dictionary are top-level and have a corresponding list of sub-categories. When creating a segment, you must provide conditions that define a segment at the sub-category level. The segment data dictionary for your industry solution is specified in the following table.

Table 2. Wealth data dictionary

Categories | Sub-categories
ASSETS | Bonds, Commodities, Credits, Derivatives, Estate Funds, Forex, Money Market, Stocks, Others
TRADING | Bonds, Commodities, Credits, Derivatives, Estate Funds, Forex, Money Market, Stocks, Others
FUNDING | Regular, Rare, Lumpy
INTERACTION | Rare, Frequent, Less Frequent

The above categories are defined based on the customer transactions data.

ASSETS

Assets owned by the user in any of the listed sub-categories.

TRADING

Trading activity performed by the user in any of the listed sub-categories.

FUNDING

Funding pattern for the user.

INTERACTION

Interaction pattern for the user. Implies interaction with the wealth manager.

Segment definition

A segment definition is created by using any combination of the above categories and the relevant sub-categories. As mentioned, conditions that define a segment are specified at the sub-category level. Some predefined segments are included in your industry solution. The REST API accepts the segment definition in the form of a JSON input string and persists it to the database. The JSON format for the segment definition is as follows:

                     {
                        "segment": "segment_name",
                        "categories": [
                        {
                        "name": "category_name",
                        "subcategories": [
                        {"name": "subcat_name", "operator": "value", "deviation": value}, ...
                        ]
                        }, ...
                        ]
                        }

segment

This attribute is a string that holds the segment name.

categories

This is an array of categories.

Within the categories array for each category, you have:

name

This attribute is a string that holds the category name.

subcategories

This is an array of subcategories.

Within the subcategories array for each subcategory, you have:

name

This attribute is a string that holds the subcategory name.

operator

This attribute is a string that holds the operator value. Possible values are listed below.

deviation

This is a float value that is used to specify "the number of times the standard deviation" .

The rules for operator values are determined by the following conditions.

greater, if condition used is > (M + deviation*SD).

upper, if condition used is > (M - deviation*SD).

lower, if condition used is < (M + deviation*SD).

less, if condition used is < (M - deviation*SD).

equal, if condition used is = value.

greater num, if condition used is > value.

less num, if condition used is < value.

range, if condition used is > (M - deviation*SD) AND (M + deviation*SD).

Note: Mean (M) and Standard Deviation (SD) values are calculated at the category level, for each category. These values are calculated by the Segmentation SPSS model using the customer transactions data. As a user, you supply the deviation value (i.e., the number of times the standard deviation).

The following example illustrates the segment definition structure.

Wealth segment name: Indifferent

The following sample JSON input defines these conditions:

ASSETS: Bonds > (M – 1.0*SD) ; Stocks < (M + 1.0*SD). The operator value for Bonds is upper. The operator value for Stocks is lower.

TRADING: Bonds > (M + 0*SD); Money Market > (M – 0.5*SD); Stocks < (M + 0.5*SD); and Others < (M - 0.5*SD). The operator value Bonds is greater. The operator value Money Market is upper. The operator value Stocks is lower. The operator value Others is less.

FUNDING:

Funding pattern is Not Lumpy (i.e. Lumpy = 0). There would not be any Mean or Standard Deviation calculated in case of FUNDING category. The operator value is "equal" and deviation value set to 0.

Funding pattern is Irregular (i.e. Regular = 0). There would not be any Mean or Standard Deviation calculated in case of FUNDING category. The operator value is "equal" and deviation value set to 0.

INTERACTION: Interaction pattern is "Rare" (i.e. Rare = 1). There would not be any Mean or Standard Deviation calculated in case of FUNDING category. The operator value is "equal" and deviation value set to 1.

                        {
                           "segment": "Indifferent",
                           "categories": [
                           {
                           "name": "ASSETS",
                           "subcategories": [
                           {"name": "Bonds", "operator": "upper", "deviation": 1},
                           {"name": "Stocks","operator": "lower", "deviation": 1}
                           ]
                           },
                           {
                           "name": "TRADING",
                           "subcategories": [
                           	         {"name": "Bonds", "operator": "greater", "deviation": 0},
                           {"name": "Money Market", "operator": "upper","deviation": 0.5},
                           {"name": "Stocks","operator": "lower", "deviation": 0.5},
                           	         {"name": "Others","operator": "less", "deviation": 0.5}
                           ]
                           },
                           {
                           "name": "FUNDING",
                           "subcategories": [
                           { "name": "Lumpy", "operator": "equal", "deviation": 0},
                           	         { "name": "Regular", "operator": "equal", "deviation": 0}
                           ]
                           },
                           {
                           "name": "INTERACTION",
                           "subcategories": [
                           {"name": "rare","operator": "equal", "deviation": 1}
                           ]
                           }
                           ]
                           }

Parent topic: REST API reference

/iswealth/customer/bi/{customerid}/peergroup

This resource returns the peer group information for a specific customer.

/iswealth/customer/bi/{customerid}/peergroup

Returns the peer group (segment and networth) to which a particular customer belongs to.

Sample JSON response:

                     {
                        "networth":{
                        "id":2,
                        "group":"Mass Affluent"
                        },
                        "segment":{
                        "name":"Others",
                        "id":6
                        },
                        "customer_id":12
                        }

Example:

<baseurl>iswealth/customer/bi/12/peergroup

The above example returns the peer group that the customer (with id "12") belongs to.

Parent topic: REST API reference

/iswealth/customer/bi/segments

This resource provides the details of each segment and their distribution. Examples of segments include Accumulators, Preservers, Independents, Followers, or Straight Arrows.

/iswealth/customer/bi/segments

Returns the following information for each segment:

Number of customers in the segment

Segment name

Net worth of the segment

The number of customers can be compared against the total, across all segments, to determine the percentage of customer within each segment.

Sample JSON response:

                     {
                        "segments":
                        {
                        "networth":899286832.00,
                        "segment":"Others",
                        "num_customers":341
                        },
                        {
                        "networth":19934364798.00,
                        "segment":"Preservers",
                        "num_customers":3416
                        },
                        "flag": true
                        }

Parent topic: REST API reference

/iswealth/customer/bi/{segment_name}/assets

This resource provides the assets distribution information for particular segments. In addition to segments, the JSON response can be further filtered by networth and customer id..

/iswealth/customer/bi/{segment_name}/assets

The {segment_name} parameter in the URL is required. Supported values include:

Conserver

Indifferent

Straight Arrow

Individualistic

Hedgers

Others

Supported query parameters:

networth

(Optional) Specifies the net worth category to which the customer belongs to, such as "Mass Affluent" .

customerid

(Optional) Specifies ID of a particular customer.

The JSON response contains distribution percentages for the corresponding section within the category.

Sample JSON response:

                     {
                        "networth":"Mass Affulent",
                        "segment":"conserver",
                        "customer_id":6,
                        "assets":[
                        {
                        "Bonds":{"percentage":"65.21"}
                        },
                        {
                        "Money Market":{"percentage":"21.69"}
                        },
                        {
                        "Others":{"percentage":"6.54"}
                        },
                        {
                        "Stocks":{"percentage":"6.57"}
                        }
                        ]
                        }

Example:

<baseurl>/iswealth/customer/bi/straight arrow/assets?networth=Mass Affulent&customerid=6

This example returns the distribution information for assets related to a customer (with id 6) and falling under the "straight arrow" segment and the "Mass Affulent" networth group.

Parent topic: REST API reference

/iswealth/customer/bi/{segment_name}/trading

This resource provides the trading distribution information for particular segments. In addition to segments, the JSON response can be further filtered by networth and customer id..

/iswealth/customer/bi/{segment_name}/trading

The {segment_name} parameter in the URL is required. Supported values include:

Conserver

Indifferent

Straight Arrow

Individualistic

Hedgers

Others

Supported query parameters:

networth

(Optional) Specifies the net worth category to which the customer belongs to, such as "Mass Affluent" .

customerid

(Optional) Specifies ID of a particular customer.

The JSON response contains distribution percentages for the corresponding section within the category.

Sample JSON response:

                     {
                        "networth":"Mass Affulent",
                        "segment":"straight arrow",
                        "customer_id":6,
                        "trading":[
                        {
                        "Bonds":{"percentage":"33.92"}
                        },
                        {
                        "Money Market":{"percentage":"3.22"}
                        },
                        {
                        "Others":{"percentage":"16.29"}
                        },
                        {
                        "Stocks":{"percentage":"33.92"}
                        }
                        ]
                        }

Example:

<baseurl>/iswealth/customer/bi/straight arrow/trading?networth=Mass Affulent&customerid=6

This example returns the distribution information for trading related to a customer (with id 6) and falling under the "straight arrow" segment and the "Mass Affulent" networth group.

Parent topic: REST API reference

/iswealth/customer/bi/{segment_name}/funding

This resource provides the funding distribution information for particular segments. In addition to segments, the JSON response can be further filtered by networth and customer id..

/iswealth/customer/bi/{segment_name}/funding

The {segment_name} parameter in the URL is required. Supported values include:

Conserver

Indifferent

Straight Arrow

Individualistic

Hedgers

Others

Supported query parameters:

networth

(Optional) Specifies the net worth category to which the customer belongs to, such as "Mass Affluent" .

customerid

(Optional) Specifies ID of a particular customer.

The JSON response contains distribution percentages for the corresponding section within the category.

Sample JSON response:

                     {
                        "networth":"Mass Affulent",
                        "segment":"straight arrow",
                        "flag": true,
                        "customer_id":8,
                        "funding":[
                        {
                        "Lumpy":{"percentage":"3.33"}
                        },
                        {
                        "Regular":{"percentage":"96.67"}
                        }
                        ]
                        }

Example:

<baseurl>/iswealth/customer/bi/straight arrow/funding?networth=Mass Affulent&customerid=8

This example returns the distribution information for funding related to a customer (with id 8) and falling under the "straight arrow" segment and the "Mass Affulent" networth group.

Parent topic: REST API reference

/iswealth/customer/bi/{segment_name}/interactions

This resource provides the interactions distribution information for particular segments. In addition to segments, the JSON response can be further filtered by networth and customer id..

/iswealth/customer/bi/{segment_name}/interactions

The {segment_name} parameter in the URL is required. Supported values include:

Conserver

Indifferent

Straight Arrow

Individualistic

Hedgers

Others

Supported query parameters:

networth

(Optional) Specifies the net worth category to which the customer belongs to, such as "Mass Affluent" .

customerid

(Optional) Specifies ID of a particular customer.

The JSON response contains distribution percentages for the corresponding section within the category.

Sample JSON response:

                     {
                        "networth":"Mass Affulent",
                        "segment":"straight arrow",
                        "flag": true,
                        "customer_id":9,
                        "interactions":[
                        {
                        "Frequent":{"percentage":"52.81"}
                        },
                        {
                        "Less Frequent":{"percentage":"47.19"}
                        }
                        ]
                        }

Example:

<baseurl>/iswealth/customer/bi/straight arrow/interactions?networth=Mass Affulent&customerid=9

This example returns the distribution information for interactions related to a customer (with id 9) and falling under the "straight arrow" segment and the "Mass Affulent" networth group.

Parent topic: REST API reference

/iswealth/customer/bi/activities

This resource provides the insights into activities that were performed by customers, filtered by segment or net worth, and related to particular life events, such as getting married or buying a new house.

/iswealth/customer/bi/activities

Returns the activities performed by customers.

Supported query parameters:

segment

(Required) Specifies the segment to which the customer belongs to, such as "Straight Arrow" .

networth

(Required) Specifies the net worth category to which the customer belongs to, such as "Mass Affluent" .

lifeevent

(Required) Specifies the activity of a customer, such as "Marriage".

customerid

(Optional) Specifies ID of a particular customer.

Sample JSON response:

                     {
                        "networth":"Mass Affulent",
                        "segment":"straight arrow",
                        "flag": true,
                        "lifeevent":"first job",
                        "customerid":500,
                        "activities": [
                        {
                        "id":6,
                        "description":"Get Life Insurance"
                        },
                        {
                        "id":15,
                        "description":"Open Retirement Account"
                        }
                        ]
                        }

Example 1:

<baseurl>/iswealth/customer/bi/activities?segment=straight arrow&networth=Mass Affulent&lifeevent=first job

This example returns the activities performed by all customers that belong to a specific peer group (segment: straight arrow, networth: Mass Affluent) and related to life event "first job".

Example 2:

<baseurl>/iswealth/customer/bi/activities?segment=straight arrow&networth=Mass Affulent&lifeevent=first job&customerid=500

This example returns the activities performed by a customer (with id "500") that belongs to a specific peer group (segment: straight arrow, networth: Mass Affluent) and related to life event "first job" .

Parent topic: REST API reference

/issegmentation/bs/{industry}/segment/create

This resource allows you to create a new segment with an associated segment definition; defined in JSON format. If the specified segment name already exists, then the segment definition will be updated by the provided JSON.

/issegmentation/bs/{industry}/segment/create

This resource is issued as a POST request. The "industry" parameter must be specified as one of the following values:

banking

wealth

insurance

Sample JSON response:

                     Content-Type: application/json
                        HTTP status code: 200
                        Body: {
                        	"segment_name":"name",
                        	"segment_id":id,
                        	"flag":true
                        }

Example:

<baseurl>/issegmentation/bs/banking/segment/create

This example creates a new segment in the Behavior Based Customer Insight for Banking database. The segment name and definition are defined in the JSON body of the request. For information on the proper JSON structure, see Customizing segments.

Parent topic: REST API reference

/issegmentation/bs/{industry}/segment/delete/{segment_id}

This resource allows you to create a new segment with an associated segment definition; defined in JSON format. If the specified segment name already exists, then the segment definition will be updated by the provided JSON.

/issegmentation/bs/{industry}/segment/delete/{segment_id}

This resource is issued as a PUT request. The "industry" parameter must be specified as one of the following values:

banking

wealth

insurance

The "segment_id" represents the ID of the segment to be deleted from the database.

Sample JSON response:

                     Content-Type: application/json
                        HTTP status code: 200
                        Body: {
                        	"flag":true
                        }

Example:

<baseurl>/issegmentation/bs/banking/segment/create/1010

This example deletes a segment, with ID 1010, from the Behavior Based Customer Insight for Banking database. For information on the proper JSON structure, see Customizing segments.

Parent topic: REST API reference

BI model

Multiple cubes are provided to support the PCI Wealth Client Insight report. This section gives a brief description of each cube along with the metadata model (the facts and dimensions involved in each cube).

LIFE_EVENT_HISTORY_FACT cube

This cube contains customer details, contact information, the net worth group that the customer belongs to, and life events that have happened along with the customer's age at the time of each life event.

ACTIVITY_PEER_GRP_FACT cube

This cube contains information on activities performed by a customer for each life event along with the activities performed by his peer group for the same life event.

ASSETS_FACT cube

This cube contains information on a customer's assets, net worth, and segment.

FUNDING_FACT cube

This cube contains information on a customer's funding patterns, net worth, and segment.

INTERACTION_FACT cube

This cube contains information on a customer's interaction pattern, net worth, and segment.

TRADING_FACT cube

This cube contains information on a customer's trading percentages, net worth, and segment..

INDIVIDUAL_SNAPSHOT_CURRENT cube

This cube contains a snapshot of the current details for the customer, including annual income, assets, segment, and net worth.

NETWORTH_FACT cube

This cube contains the net worth of a customer across the age dimension.

CUSTOMER_LASTMET_FACT cube

This cube contains the most recent date on which the customer interacted with the financial adviser or the firm.

WEALTH_CHANNEL_USAGE_FACT cube

This cube contains the channel usage of the customer. Channel usage shows the channels that the customer is using to perform his transactions.

WEALTH_SOCIAL_INTERACTION_CHANNEL_USAGE_FACT cube

This cube contains the social interaction channel usage of the customer. It includes the number of interactions through each channel.

Notices

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