1. Banking on Analytics Dr A S Ramasastri Director, IDRBT

2. 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?

3. ... 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

4. The need of the hour 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

5. 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

6. 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

7. 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

8. Assessment : Credit and Portfolio Credit Appraisal – based on the data on the current customers, develop classes of risk- worthiness 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

9. 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

10. Tools for Analytics / Data Mining Classification Clustering Correlation Regression Association Rule Learning Pattern Recognition Deviation Detection Artificial Neural Networks

11. Some Open Source Software R RapidMiner OpenNN Orange Apache Mohout KNIME Weka

12. Further References Google !!! After all Google MUST be using several techniques to analyze such large volumes of web data

13. Thanks asramasastri@idrbt.ac.in