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Big Data World presentation - Sep. 2014

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Big Data World presentation in Kuala Lumpur - 9th. Sep., 2014

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Big Data World presentation - Sep. 2014

  1. 1. Case Study in Banking and Finance: The Real-World Use of Big Data in Financial Services Big Data World Show, Malaysia Loon Wing Yuen Director, Innovation Group Information Services, Group Information and Operations Division Shangri-La Hotel, Kuala Lumpur 9-10 September, 2014
  2. 2. The Opportunity in CIMB (circa 2012) CIMB had the largest Facebook fan-base (over 600k) among banks in Malaysia CIMB also had the largest Facebook fan-base (over 1m) among banks in ASEAN CIMB also had a huge Twitter following CIMB had launched OctoPay (Facebook Banking) and many Facebook-related marketing campaigns, several which targeted debit card usage – we built a database of matching CustomerIDs and FBIDs with associated FB structured and unstructured data With this data asset, there were potential debit card revenue opportunities leveraging this asset 2 FB Marketing Campaigns (2011 & 2012) ASEAN’s 1st banking service on Facebook (2012)
  3. 3. Application Use Case – Leveraging the value of our customer’s FB Likes The Debit Card business was new and so the active card base was low, resulting in low transactional volumes. Hence, this did not give the business a good idea on the types of merchant spend. We decided to check if there was a correlation between FB Likes and merchant spend. Assuming there was a good correlation – then the hypothesis was that we could take the wider range of merchant categories in FB Likes and use them for marketing campaign interventions. 3 Big Data Platform (FB Likes) With Debit Card With Spend Regular Spender Irregular* Spender Without Spend Without Debit Card Traditional Big Data New Big Data 2012
  4. 4. Distribution of Customers’ FB Likes ►Total identified distinct users who generated FB Like*: 27,614 (out of a total of 53,482) ►Out of the 53k total, we linked 12,925 users as active CIMB customers ►Approximately 6.5% of users liked more than 500 pages in their Facebook profile. ►These 6.5% of “heavy FB Likes users” accounted for approximately 36% of the total FB Likes captured. We discovered that the FB Likes generated by customers were very unevenly distributed. * -- Excluding an FB Like for CIMB. 14544 9006 2277 1787 0 2000 4000 6000 8000 10000 12000 14000 16000 <=100 >100 and <=300 >300 and <=500 >500 Total Number of User Count versus Total Number of Likes per User 567047, 12% 1584913, 34% 874962, 18% 1696692, 36% Total Number of User Like Pages versus Total Number of Likes per User <=100 >100 and <=300 >300 and <=500 >500 2012 4
  5. 5. Correlation of FB Likes with Merchant Spend ►Significant amounts of data cleansing and transformations required ►Correlations stronger in certain merchant categories/brands ►Not every FB Like is correlated ►Statistical testing required to determine the strength of correlations We discovered good correlations among certain merchant categories and brands. •The matching of the merchant_name and fblike_name is based on the simple “Like” SQL statement which does not guarantee the full match between the merchant_name and fblike_name. •More powerful data cleaning is needed to match the merchant_name and fblike_name more accurately. Debit Card Txn FB data 2012 5
  6. 6. Distinct Count of FBLikes for Starbucks by Micro Segment Targeted Interventions by Merchant Brand ►Size of bubble represents Total FB Likes from Credit Card Prospect Base* FILTERED BDPP DATASET AS AT 11 MAR 2013: 1.6 FB CAMPAIGN DATA FROM GMCD - I LOVE NEW YORK, DEBIT CARD RESKIN, MY DEBIT CARD, FOOTBALL FANTASY, YOUTH PEEK BUY, YOUTH VIDEO VOTING 2.FB CIMB_ASSISTS DATA FROM GMCD 3.CUSTOMER TAGGING DATA FROM BIU We can then partner with a selected existing merchant (eg. Starbucks) and design a very targeted campaign – or on-board a promising new merchant partnership. 6 Note*: Prospect Base is based on active customers aged > 21 yrs old without a credit card. Micro Segment Distinct Count of FBLikes for Starbucks Facebook User Base 4,747 Active Customer Base 1,778 Credit Card Base 96 Credit Card Prospect Base* 1,216 Debit Card Base 946 Distinct Count of FBLikes for Starbucks by Business Segment Distinct Count of FBLikes for Starbucks by Macro Segment FBLikes Comparison between Credit Card Base & Debit Card Base for Starbucks by Macro Segment 2013
  7. 7. Application Use Case – Moving on to the Credit Card base The results from the work on the Debit Card base was promising enough to gain buy-in to next work on the Credit Card base as the next phase. 7 2013 The scope was to create actionable insights to: Increase credit card usage Reactivate inactive credit card users The approach was to: Focus on influencing usage behavior – hence the focus on analyzing customer behaviors Influence usage behavior by offering targeted merchant offers Increased usage will generally lead to increased balances The deliverables were: Decile analysis of the card user base by card spend, merchant category and merchant brand spend  A range of actionable propositions that can drive card usage A fully sized segmentation model for targeted offers
  8. 8. Credit Card Usage Analysis The business goal at high level is to maximise both usage and balances for each credit card customer. 8 Usage Balance High Usage Medium Balance High Usage Low Balance (Transactors) High Usage High Balance (Core Revolvers) Medium Usage Medium Balance Medium Usage Low Balance Medium Usage High Balance Low Usage Medium Balance Low Usage Low Balance Low Usage High Balance 1 2 3 4 5 6 7 8 9 ‘Occasionals’ Profitable group 2013
  9. 9. From Analysis to Actionable Insights An example of crafting a marketing proposition for the ‘Occasionals’ cohort. 1. Understand customer purchases by merchant categories 2. Understand merchant product features 3. Plan campaign and create offers 4. Generate the customer list for each offer and execute according to campaign plan Which product to offer Choo- sing who to target 2013 9
  10. 10. Big Data Analytics Platform for Business In reality though, this is how the business is analyzed – by deciles. The new Big Data Exploration Portal allows “speed of thought” analyses as compared to the traditional multi-week report turnarounds from the data-warehouse – a key metric is now “Time to Actionable Insights”. 10 2014 Entire Customer Base with > 30 months of transactional data 500+ different metrics calculated in < 2 seconds
  11. 11. The problem is that at least two-thirds of our effort and time is spent with data cleansing, filtering, transformation, enrichment, etc. instead of extracting business value from the data. Our Biggest Challenge though is.. 11
  12. 12. Big Data requires familiarity with Statistical/Machine Learning and NoSQL approaches The Statistical/Machine Learning approaches used were: ► Principal Component Analysis (a Statistical dimensionality reduction approach) was used to reveal key behaviors among the credit card base ► K-Means clustering (a Machine Learning approach) was used to identify and segment “Low to High (Y1  Y2) Usage” spend behavior ► Neural Networks (a Machine Learning approach) was used to predict spend behavior ► Support Vector Machines (a Machine Learning approach) was used to predict customer inactivity The NoSQL approaches used were: ► De-normalisation/nesting of the transactional data ► Modeling the data for optimal access for the purposes of supporting long-term customer analytics and near-realtime customer intervention systems Some of the statistical/machine learning and NoSQL approaches used were: 2013 - 2014 12
  13. 13. Enhancing Business Capabilities with Big Data analytics Big Data analytics can enhance all business dimensions of “Analytics” and “Management Information” Compliance & Regulatory Analytics Basel II & III FATCA Sarbanes Oxley Act (SOX) Fraud / AML Suspicious Activity Compliance Reporting Regulatory Reporting Risk Management Analytics Credit Risk Market Risk Operations Risk Liquidity Risk Capital Analysis Collection Analysis Exposure Analysis Sales Analytics Event/Campaign Analytics Behavior Analytics Market Analytics Transaction Analytics Customer Analytics Targeted Marketing / Sales Lead Analytics Management Analytics Income Analytics Cost Analytics Profitability Analytics Sales Performance Payment Analytics Capital Allocation Analytics Position Analytics Balance Sheet Analytics Weighted Average Analytics Structured Finance Analytics Liquidity Analytics Corporate Action Analytics Performance Analytics Financial Market Analytics Foreign Exchange Analytics Settlement Analytics Performance vs Benchmark Asset Allocation Analytics Product Analytics Portfolio Performance Portfolio Risk Analytics 13
  14. 14. Rebuilding our Big Data and Machine Learning Platform There is an incredible opportunity to leverage Big Data and Machine Learning technologies to add advanced capabilities to our digital channels as well as to dramatically reduce “time to actionable insights” for our business stakeholders. ElasticSearch Indexing Map - Reduce Pig Hive Tez HBase Storm Spark* Yarn HDFS Exploration Portal Enterprise Data Warehouse Customer NoSQL Repository (Cassandra) 2014 Analytics REST-API layer Business Analyst 14
  15. 15. Focus on the business priorities first, start with an engaged business stakeholder and manageable pilot Identify a business opportunity to address and prove the viability/business case, let the next business use case build upon this success and expand Focus on people and skills, lesser on the technologies The technologies are new, so be prepared to experiment; use, discard and replace technology components as required (many are open-source, fortunately) Data cleansing/preparation/ management is a big issue, not to be underestimated If the existing EDW is not primarily built for customer centricity and insight, don’t retrofit this into the EDW – instead build something akin to the Customer NoSQL Repository outside using new Big Data technologies Approach Capabilities There is an incredible opportunity to leverage Big Data and Machine Learning technologies to add advanced capabilities to an organisation’s digital channels and supporting the business need of significantly reducing “time to actionable insights” There is significant business opportunity in leveraging external data such as FB and Twitter But rethink approach on leveraging this FB and Twitter data – start with working on the issue of reliably linking external ids with internal customer ids Opportunity Summary and our learnings along the journey so far 2012 – 2014 15
  16. 16. Thank You

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