Transcript Banking on Analytics - Bengal Chamber of Commerce and Industry

Banking on Analytics
Dr A S Ramasastri
Director, IDRBT
A few questions . . .
1. What is the impact on sales and profit by a new
product / service introduced by you?
2. What is the general opinion in the market on a
product / service introduced by you?
3. Who is the ideal customer to whom you can
make a personal offer of the product / service?
Is the particular customer worthy of the offer?
4. What would happen if you make a few changes
to the product / service?
5. Are there any demography-based linkages
among products, services, defaults and frauds?
. . . and approaches to answers
• Reports from data warehouse / data mart thru
OLAP tools – Business Intelligence
• Opinion Mining on Social Networks –Descriptive
Analytics
• Finding potential customer and her value based
on past behavior – Predictive Analytics
• Assessing the impact of an action on a result –
Prescriptive Analytics
• Exploring huge volume of data for discovering
hidden patterns – Data Mining
The need of the hour
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Relevant Quality Data
Qualified Data Scientists
Coordinated Efforts by Concerned Companies
Focused Applied Research by Institutions –
with support from companies and bodies
• In case of banks, IDRBT has initiated the
process with the support from stakeholders
IDRBT
• A unique institute established by Reserve
Bank of India for development and research in
banking technology
• Works closely with Reserve Bank of India,
banks and academicians on important areas of
application of technology in banks –
information security, payment systems,
networks, cloud computing and analytics
Analytics Center at IDRBT
• Lab exclusively for analytics has been set up at
IDRBT a few years back
• Banks have training programs and
experiments conducted at IDRBT lab – both at
individual bank level and bank group level
• The areas of focus are generally CRM, risk
management and fraud analytics
• Dedicated faculty and research scholars
CRM : Products and Services
• Customer Retention – customer behavior prior to
attrition, model to retain the customers
• Targeted Marketing – identify buying patterns,
finding associations among customer demographic
customers, predicting response to various types of
campaigns
• Credit Card – identifying loyal customers, predicting
customers likely to change their affiliation, determine
card using behavior, selecting appropriate product /
service
Assessment : Credit and Portfolio
• Credit Appraisal – based on the data on the
current customers, develop classes of riskworthiness and classify a new borrower into
one of the classes
• Portfolio Management – identifying trading
rules from historical data, selecting financial
assets to be included in the portfolio,
assessing impact of market changes on
portfolio; optimizing portfolio performance
Prediction : Defaults and Frauds
• Housing Loan Prepayment Prediction
• Mortgage Loan Delinquency Prediction
• Uncovering hidden correlations between
customer characteristics and behavior
• Detecting Patterns of Frauds – Credit Card,
ATM, Internet Banking Frauds
• Real Time Alerts on Online Frauds
Tools for Analytics / Data Mining
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Classification
Clustering
Correlation
Regression
Association Rule Learning
Pattern Recognition
Deviation Detection
Artificial Neural Networks
Some Open Source Software
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RapidMiner
OpenNN
Orange
Apache Mohout
KNIME
Weka
Further References
Google !!!
After all
Google MUST be using
several techniques
to analyze such large volumes of web data
Thanks
[email protected]