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FNB Optimizes Retail Banking
Product Offers
Using Real-Time Propensity Models, Rules and Events
Avsharn Bachoo – FNB
Vincent Baruchello - IBM
First National Bank
• The oldest bank in South Africa formed in 1838
• Listed on the South African Stock Exchange and the Namibian Stock Exchange
• One of the largest financial institutions in South Africa
• Providing banking and insurance to retail, commercial, corporate and public
sector customers
1
Product Sales Initiatives
• Proactive sales, service and prospecting system.
• Used to assess the eligibly customer base for a variety of
– product offers,
– value adds, and
– service related messages.
• The process caters for both a fully automated eligibility process via
– mainframe leads process,
– adhoc load capability.
2
Integrating decisioning capabilities
Domain with engine
3
Mainframe
Legacy
Rule
Engine
Business rules
Policy Rules
JCL call to DA
Java API
JMS
SOA/Web Service call
Enterprise Service Bus
Mainframe
Limitations
• Legacy rule engine not integrated into mainframe and no direct link to
warehouse
• Insufficient computing power to process information daily
4
Developers Mainframe
Legacy
Engine
Data
warehouse
30 day data gathering
2 day-long batch scoring
Adding propensity scores
5
Complex analytical processing of billions of records requires
significant amounts of computing power
Limitations
• Customer needs change on a daily basis
• Updates to customer information takes place monthly
• Propensity scores derived monthly
• Missing the window of opportunity for getting offers to the customers at
the right time.
6
Business Expectations
• Enable decisions to be made
– real-time
– leveraging of
• internal models,
• advanced statistical models &
• predictive analytics
Right Product @ Right Place & Right Time
7
Technical expectations
• Aggregate large volumes of data and derive variables
• Adaptive to seamlessly fit into the existing complex FNB architecture
• Solution needed the capability for current and future integration into
FNB’s
– Warehouse’s
– Data mining systems
8
Selection Criteria
9
Product Technology environment Interface Seamless Integration with
FNB mainframe
FICO Blaze Advisor JVM or .NET Proprietary API or Web
Service
Message switch
IBM ODM zOS, JVM Cobol, XML and JAVA API Direct
Jboss Enterprise BRMS Jboss Middleware JAVA API Message switch
Apama JVM or .NET JAVA, C, C++, .NET Message switch
Experian Powercurve JVM JAVA, C Message switch
SAP NetWeaver JVM JAVA, ABAP Message switch
Oracle Business Rules Oracle Fusion Middleware XML, JAVA or Oracle Message switch
ODM and Netezza 2016-future
10
Find leads
using changes
in Customer
activity
(Events)
Optimise Leads
Right time
communication
to customer
Systems of Records
Systems of
Engagements
Delivering through an event-driven architecture
11
Web Social
Detect
Decide
Systems of Insights
Mobile IoT
ATM
Branch
Mail
Event
Situational
Awareness
Predictive
Models
Netezza
Analytics
Systems of Records
Operational Decisions
(z / cloud / distributed)
Combining rules and analytics
12
Instant decision
Predictive
Scores
Business
Interaction
event
transaction
Situational
Awareness
Automated
Services
Timely event
ODM and Netezza
Conceptual Architecture
13
Netezza
ODM
• Aggregate customer base information
• Cleans information
• Apply models to daily information
• Produces scores
• Dump scores
• Rules filter customers & products
• Makes recommendations, bundling
products
• Decide when & how (channels)
Seizing opportunities through situational awareness
14
Process
Rule
Servi
ce
Channels
High fidelity,
granular actions
Millions of Customers
 Loan Applicant
 …
Millions of
interactions
Hundreds of
Aggregates
Thousands
of Rules
Dozens
of Models
Applying Insights to simplify creating personalized,
customer-specific actions at the time of interaction
Decision Management in context
IBMDecisionServer Insights
Processes
System of Records
Social Media
Sensors
Data Warehouse
Business Events
Situation Detection & Action
Information Bus
Mobile Devices
System of Engagement
Four Steps toward decision making in context
DistributedMainframeAppliancesCloud
Running IBM ODM on a wide range of solutions
16
Expected Benefits
• Make offers at the right time
• Improve relationship with customers
• Increase likelihood of sales by offering tailored products geared to
specific customer needs
– personalized offers to the customer
• Continuous improvement by
– analyzing customer data,
– monitoring transactions,
– determining patterns
– to make the right offer at the right time in a dynamic fashion
17
Lessons Learnt
• Technical:
– No major issues experienced
– Compatibility issue: ODM and Netezza ran different versions of Java
• Business:
– No yardstick to plan effort
– More focus on Java skill set for new recruits
18
Notices and Disclaimers
19
Copyright © 2016 by International Business Machines Corporation (IBM). No part of this document may be reproduced or transmitted in any form without written permission
from IBM.
U.S. Government Users Restricted Rights - Use, duplication or disclosure restricted by GSA ADP Schedule Contract with IBM.
Information in these presentations (including information relating to products that have not yet been announced by IBM) has been reviewed for accuracy as of the date of
initial publication and could include unintentional technical or typographical errors. IBM shall have no responsibility to update this information. THIS DOCUMENT IS
DISTRIBUTED "AS IS" WITHOUT ANY WARRANTY, EITHER EXPRESS OR IMPLIED. IN NO EVENT SHALL IBM BE LIABLE FOR ANY DAMAGE ARISING FROM THE
USE OF THIS INFORMATION, INCLUDING BUT NOT LIMITED TO, LOSS OF DATA, BUSINESS INTERRUPTION, LOSS OF PROFIT OR LOSS OF OPPORTUNITY.
IBM products and services are warranted according to the terms and conditions of the agreements under which they are provided.
Any statements regarding IBM's future direction, intent or product plans are subject to change or withdrawal without notice.
Performance data contained herein was generally obtained in a controlled, isolated environments. Customer examples are presented as illustrations of how those customers
have used IBM products and the results they may have achieved. Actual performance, cost, savings or other results in other operating environments may vary.
References in this document to IBM products, programs, or services does not imply that IBM intends to make such products, programs or services available in all countries in
which IBM operates or does business.
Workshops, sessions and associated materials may have been prepared by independent session speakers, and do not necessarily reflect the views of IBM. All materials
and discussions are provided for informational purposes only, and are neither intended to, nor shall constitute legal or other guidance or advice to any individual participant or
their specific situation.
It is the customer’s responsibility to insure its own compliance with legal requirements and to obtain advice of competent legal counsel as to the identification and
interpretation of any relevant laws and regulatory requirements that may affect the customer’s business and any actions the customer may need to take to comply with such
laws. IBM does not provide legal advice or represent or warrant that its services or products will ensure that the customer is in compliance with any law
Notices and Disclaimers Con’t.
20
Information concerning non-IBM products was obtained from the suppliers of those products, their published announcements or other publicly available sources. IBM has not
tested those products in connection with this publication and cannot confirm the accuracy of performance, compatibility or any other claims related to non-IBM products.
Questions on the capabilities of non-IBM products should be addressed to the suppliers of those products. IBM does not warrant the quality of any third-party products, or the
ability of any such third-party products to interoperate with IBM’s products. IBM EXPRESSLY DISCLAIMS ALL WARRANTIES, EXPRESSED OR IMPLIED, INCLUDING BUT
NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE.
The provision of the information contained h erein is not intended to, and does not, grant any right or license under any IBM patents, copyrights, trademarks or other intellectual
property right.
IBM, the IBM logo, ibm.com, Aspera®, Bluemix, Blueworks Live, CICS, Clearcase, Cognos®, DOORS®, Emptoris®, Enterprise Document Management System™, FASP®,
FileNet®, Global Business Services ®, Global Technology Services ®, IBM ExperienceOne™, IBM SmartCloud®, IBM Social Business®, Information on Demand, ILOG,
Maximo®, MQIntegrator®, MQSeries®, Netcool®, OMEGAMON, OpenPower, PureAnalytics™, PureApplication®, pureCluster™, PureCoverage®, PureData®,
PureExperience®, PureFlex®, pureQuery®, pureScale®, PureSystems®, QRadar®, Rational®, Rhapsody®, Smarter Commerce®, SoDA, SPSS, Sterling Commerce®,
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Machines Corporation, registered in many jurisdictions worldwide. Other product and service names might be trademarks of IBM or other companies. A current list of IBM
trademarks is available on the Web at "Copyright and trademark information" at: www.ibm.com/legal/copytrade.shtml.
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Fnb optimizes retail banking product offers using real-time propensity models rules and events - Avsharn Bachoo

  • 1. FNB Optimizes Retail Banking Product Offers Using Real-Time Propensity Models, Rules and Events Avsharn Bachoo – FNB Vincent Baruchello - IBM
  • 2. First National Bank • The oldest bank in South Africa formed in 1838 • Listed on the South African Stock Exchange and the Namibian Stock Exchange • One of the largest financial institutions in South Africa • Providing banking and insurance to retail, commercial, corporate and public sector customers 1
  • 3. Product Sales Initiatives • Proactive sales, service and prospecting system. • Used to assess the eligibly customer base for a variety of – product offers, – value adds, and – service related messages. • The process caters for both a fully automated eligibility process via – mainframe leads process, – adhoc load capability. 2
  • 4. Integrating decisioning capabilities Domain with engine 3 Mainframe Legacy Rule Engine Business rules Policy Rules JCL call to DA Java API JMS SOA/Web Service call Enterprise Service Bus Mainframe
  • 5. Limitations • Legacy rule engine not integrated into mainframe and no direct link to warehouse • Insufficient computing power to process information daily 4 Developers Mainframe Legacy Engine Data warehouse 30 day data gathering 2 day-long batch scoring
  • 6. Adding propensity scores 5 Complex analytical processing of billions of records requires significant amounts of computing power
  • 7. Limitations • Customer needs change on a daily basis • Updates to customer information takes place monthly • Propensity scores derived monthly • Missing the window of opportunity for getting offers to the customers at the right time. 6
  • 8. Business Expectations • Enable decisions to be made – real-time – leveraging of • internal models, • advanced statistical models & • predictive analytics Right Product @ Right Place & Right Time 7
  • 9. Technical expectations • Aggregate large volumes of data and derive variables • Adaptive to seamlessly fit into the existing complex FNB architecture • Solution needed the capability for current and future integration into FNB’s – Warehouse’s – Data mining systems 8
  • 10. Selection Criteria 9 Product Technology environment Interface Seamless Integration with FNB mainframe FICO Blaze Advisor JVM or .NET Proprietary API or Web Service Message switch IBM ODM zOS, JVM Cobol, XML and JAVA API Direct Jboss Enterprise BRMS Jboss Middleware JAVA API Message switch Apama JVM or .NET JAVA, C, C++, .NET Message switch Experian Powercurve JVM JAVA, C Message switch SAP NetWeaver JVM JAVA, ABAP Message switch Oracle Business Rules Oracle Fusion Middleware XML, JAVA or Oracle Message switch
  • 11. ODM and Netezza 2016-future 10 Find leads using changes in Customer activity (Events) Optimise Leads Right time communication to customer
  • 12. Systems of Records Systems of Engagements Delivering through an event-driven architecture 11 Web Social Detect Decide Systems of Insights Mobile IoT ATM Branch Mail Event Situational Awareness Predictive Models
  • 13. Netezza Analytics Systems of Records Operational Decisions (z / cloud / distributed) Combining rules and analytics 12 Instant decision Predictive Scores Business Interaction event transaction Situational Awareness Automated Services Timely event
  • 14. ODM and Netezza Conceptual Architecture 13 Netezza ODM • Aggregate customer base information • Cleans information • Apply models to daily information • Produces scores • Dump scores • Rules filter customers & products • Makes recommendations, bundling products • Decide when & how (channels)
  • 15. Seizing opportunities through situational awareness 14 Process Rule Servi ce Channels High fidelity, granular actions Millions of Customers  Loan Applicant  … Millions of interactions Hundreds of Aggregates Thousands of Rules Dozens of Models Applying Insights to simplify creating personalized, customer-specific actions at the time of interaction Decision Management in context IBMDecisionServer Insights
  • 16. Processes System of Records Social Media Sensors Data Warehouse Business Events Situation Detection & Action Information Bus Mobile Devices System of Engagement Four Steps toward decision making in context
  • 17. DistributedMainframeAppliancesCloud Running IBM ODM on a wide range of solutions 16
  • 18. Expected Benefits • Make offers at the right time • Improve relationship with customers • Increase likelihood of sales by offering tailored products geared to specific customer needs – personalized offers to the customer • Continuous improvement by – analyzing customer data, – monitoring transactions, – determining patterns – to make the right offer at the right time in a dynamic fashion 17
  • 19. Lessons Learnt • Technical: – No major issues experienced – Compatibility issue: ODM and Netezza ran different versions of Java • Business: – No yardstick to plan effort – More focus on Java skill set for new recruits 18
  • 20. Notices and Disclaimers 19 Copyright © 2016 by International Business Machines Corporation (IBM). No part of this document may be reproduced or transmitted in any form without written permission from IBM. U.S. Government Users Restricted Rights - Use, duplication or disclosure restricted by GSA ADP Schedule Contract with IBM. Information in these presentations (including information relating to products that have not yet been announced by IBM) has been reviewed for accuracy as of the date of initial publication and could include unintentional technical or typographical errors. IBM shall have no responsibility to update this information. THIS DOCUMENT IS DISTRIBUTED "AS IS" WITHOUT ANY WARRANTY, EITHER EXPRESS OR IMPLIED. IN NO EVENT SHALL IBM BE LIABLE FOR ANY DAMAGE ARISING FROM THE USE OF THIS INFORMATION, INCLUDING BUT NOT LIMITED TO, LOSS OF DATA, BUSINESS INTERRUPTION, LOSS OF PROFIT OR LOSS OF OPPORTUNITY. IBM products and services are warranted according to the terms and conditions of the agreements under which they are provided. Any statements regarding IBM's future direction, intent or product plans are subject to change or withdrawal without notice. Performance data contained herein was generally obtained in a controlled, isolated environments. Customer examples are presented as illustrations of how those customers have used IBM products and the results they may have achieved. Actual performance, cost, savings or other results in other operating environments may vary. References in this document to IBM products, programs, or services does not imply that IBM intends to make such products, programs or services available in all countries in which IBM operates or does business. Workshops, sessions and associated materials may have been prepared by independent session speakers, and do not necessarily reflect the views of IBM. All materials and discussions are provided for informational purposes only, and are neither intended to, nor shall constitute legal or other guidance or advice to any individual participant or their specific situation. It is the customer’s responsibility to insure its own compliance with legal requirements and to obtain advice of competent legal counsel as to the identification and interpretation of any relevant laws and regulatory requirements that may affect the customer’s business and any actions the customer may need to take to comply with such laws. IBM does not provide legal advice or represent or warrant that its services or products will ensure that the customer is in compliance with any law
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Editor's Notes

  1. No computing power to process daily
  2. Added propensity models, next best offer, next best action
  3. IBM Decision Server Insights is new innovation we’re bringing into our ODM portfolio. It enables high fidelity, real-time granular and specific actions be taken given a very changing environment. Whether your customer is a patient, a loan applicant or an insurance policy holder, Decision Server Insights has the ability to make sense of the millions of interactions that happen across channels and take specific action in real-time. This is made possible by a really cool concept called Aggregates – it helps synthesize millions of interactions down into data that can be fed into scoring models in real-time. The result is a number of immediately actionable business rules that provide personalized, customer specific actions. This is a great example of a case where a truly complex environment is drastically simplified, yet leading to customer-centric results. Key points about Decision Server Insights: Provides incremental capability for our ODM customers; it builds on skills that customers acquire as they work with ODM. Addresses clients’ need for stateful real-time situational context for decision automation. Allows clients to sense changing business conditions and respond to them with predetermined actions.
  4. React at the right time and place to business opportunities and risks, such as detecting and preventing fraud, sending targeted marketing messages, threats to people and equipment, By aggregating events correlated with business entities across all channels And applying policy rules and predictive models to trigger an appropriate response, such as alerting people triggering processes opening cases logging data for subsequent analysis. Sense: Captures meaningful events across all channels, systems and devices Build: Put data and events into context to understand and evaluate how everything relates Decide: Apply the models, policies and best practices established by your subject matter experts Act: Initiates and consistently Automates the next best action