Survey
* Your assessment is very important for improving the workof artificial intelligence, which forms the content of this project
* Your assessment is very important for improving the workof artificial intelligence, which forms the content of this project
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 • • • • 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 • • • • • • • • Classification Clustering Correlation Regression Association Rule Learning Pattern Recognition Deviation Detection Artificial Neural Networks Some Open Source Software • • • • • • • R 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]