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Big Data Use Cases for Banks

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Large Banks are realizing that there exists a potential in this data deluge.
It would be really interesting to see which bank utilizes this the first and uses it to gain a stronger foothold in the market.
The deck discusses some use cases through which you can utilize data generated from a variety of systems you have invested in in the last many years.
Through experience and technology you can now discover insights which lie hidden in this treasure trove of data held up in a variety of formats.

Published in: Business, Economy & Finance
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Big Data Use Cases for Banks

  1. 1. Big Data Use Cases for banks Executive summary
  2. 2. Challenge 1: Customers 360° Needs Assessment “The more you engage with customers the clearer things become and the easier it is to determine what you should be doing.” John Russell, President, Harley Davidson This document is a partial preview. Full document download can be found on Flevy: http://flevy.com/browse/document/big-data-use-cases-for-banks-135
  3. 3. Challenge 2: Optimization of marketing spend "Half the money I spend on advertising is wasted; the trouble is I don't know which half." ~John Wanamaker, department store magnate This document is a partial preview. Full document download can be found on Flevy: http://flevy.com/browse/document/big-data-use-cases-for-banks-135
  4. 4. Output: Attribution by Channel This document is a partial preview. Full document download can be found on Flevy: http://flevy.com/browse/document/big-data-use-cases-for-banks-135
  5. 5. ∗ Direct financial benefits ∗ Create customer clusters and open them up for targeted promotions ∗ Act as a direct marketing agency to target and reach out to customers based on their credit card spend profile ∗ Indirect financial benefits ∗ Sharing customer insights with merchants ∗ More revenues for merchant ∗ Better commissions to the card provider 13 > 2/25/2014 Benefits from analyzing cards data This document is a partial preview. Full document download can be found on Flevy: http://flevy.com/browse/document/big-data-use-cases-for-banks-135
  6. 6. ∗ Based on the analysis of customers’ purchasing patterns across different stores, it is possible to identify which brands have the highest correlation (due to their complementary nature or similar positioning). 16 > 2/25/2014 Solution: Cross brand promotions and partnerships Customer buys shoes Malono Blahnik Customer buys LVMH Bag Partnership opportunity identified between the two vendors + Core/ Internet Banking Mobile Banking ATM/POSSocial Media Cards Data Data required for the analysis This document is a partial preview. Full document download can be found on Flevy: http://flevy.com/browse/document/big-data-use-cases-for-banks-135
  7. 7. ∗ Fraud is traditionally modeled with ∗ Features around ‘profiles’ ∗ Bank Tenure, GEO, Demographics, Bill Pay, Average Balance etc., ∗ Web data not considered (big data issue) ∗ Dealing with high false positive and negative rates ∗ Ex - If the model identifies 100 users who might commit fraud ∗ 80 users turn out innocent (low precision) ∗ 50 users who actually commit fraud escape model (low recall) ∗ Effort to fight fraud is ongoing and painful and not accurate ∗ Speed of iterations ACROSS channels is key – no easy way to “attempt” this now Data Challenges in meeting Business Objectives Core/ Internet Banking Mobile Banking ATM/POS BranchCall center Cards Data Weblogs Data required for the analysis This document is a partial preview. Full document download can be found on Flevy: http://flevy.com/browse/document/big-data-use-cases-for-banks-135
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