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Banking on Analytics

Banking on Analytics. Dr A S Ramasastri Director, IDRBT. A few questions. What is the impact on sales and profit by a new product / service introduced by you? What is the general opinion in the market on a product / service introduced by you?

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Banking on Analytics

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

  2. A few questions . . . • What is the impact on sales and profit by a new product / service introduced by you? • What is the general opinion in the market on a product / service introduced by you? • 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? • What would happen if you make a few changes to the product / service? • 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

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