This document discusses how data science can be used to enhance customer experiences and business opportunities for banks. It provides examples of problems banks face around dormant accounts, cross-selling, and irrelevant offers. It then describes how various data analysis techniques like clustering, recommendations engines, and outlier detection can be applied to transaction data to better understand customers and their needs in order to increase engagement and sales. The key applications of data science highlighted are personalized offers, reviving dormant accounts, identifying new opportunities, and enhancing cross-selling capabilities.

1. Data Science In Action
http://rpubs.com/anuattesco/
https://github.com/anukaushalsingh
By, Anurag Singh

2. Problem-ABankwantstoeliminateunpleasantexperienceof
beingbombardedwith irrelevantcashbackoffersforits
customers?
Why do we do that?
• Personalized offers strike a better chord with customers.
• Cost Effective for Banks with probable income opportunities and association with best brands across
industries.
• Banks is alleviated of any tight integrations with merchants and even negotiations since sourcing of
offers is handled at the Banks end.
*Yes Bank did it in October 2017/Recommender Engine Algorithm

3. DataAnalysisandScience
• Use past transaction data
• Find the spending habits and the location
Current Deals/Create Deals with the Ice-cream Stores:
Store 1-Bank Profit 2 Customer Expense 10-Propensity 25%
Store2-Bank Profit 2 Customer Expense 10-Propensity 25%
Store 3-Bank Profit 5 Customer Expense 12-Propensity 25%
Store 4-Bank Profit 6 Customer Expense 12-Propensity 25%
Banks aim should be to increase the propensity of Customer to go to Store 3 or 4.
Can we really do this ? Can we provide proportional discount on store 3 and 4 based on Domain
Experience.

4. Problem-ABankhas .4milliondormantaccountwhatto do?
Actions and Tradeoff
• Shall we close all of them? Potentially a wasteful strategy..
• Cost of reviving a dormant account 7X cheaper..
• Reach out all the dormant customers still a non productive approach.
*Kotak Mahindra Bank did it in April 2014/Kmeans Clustering

5. DataAnalysisandScience
• Use past data from core banking data ware house.
• Divide the Customers into Buckets.
Get an Insight into wallet size
Loan Liability
Address Pin code
Banks aim should be to gauge exact potential of the customer
Can we really do this ? Can we make corrections to previously sold products?

6. Problem-Canweenhancecrosssellingcapability?Snifffresh
investmentopportunities?
Why do we do that?
• Convert customer conversation to business opportunities
• Not letting your customers be easy target for someone in competition.
• Channelize relationship channels efforts, optimize time, results in targeted engagement.
*Kotak Mahindra Bank did it in June 2016/Supervised&UnsupervisedTechniques-Detect Outliers

7. DataAnalysisandScience
• Use past data from core banking and relationship management system.
• Place Algorithms working on certain threshold.
360 view of customer
Needs
Wallet Size
Transaction Behavior
Demographic profile
Account History
Right Customer conversation--Customer Conversion
SLR approach to Customer Engagement rather than stain gun approach possible?

8. 8

9. BigData=CrudeOil[notnewoil]
Think data as ‘crude oil’
Big Data is about extracting the crude oil
Transporting it in mega tankers siphoning it through pipelines and storing in massive slos
All this is about IT BIG Data …..fine and well ..
BUT

10. You need to refine the ‘crude oil’
Enter Data Science…..

11. TheScience[andArt]of…..
Discovering what we don’t know from Data.
Obtaining Predictive Actionable insight from Data.
Creating Data Products that have business impact now.
Communicating relevant business stories from data.
Building confidence in decision that drive business value.

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13. Thank You.
https://www.linkedin.com/in/anuragsinghdatasciencebigdata/