Big Data Banking: Customer vs. Accounting
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Core Banking Systems have evolved from treating customer data as a peripheral of transactions to more and more a central focus of the system. Thi s presentation explores how DreamOval is positioning Bank Nurse to meet this new reality of store more customer data than transactions
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Big Data Banking: Customer vs. Accounting
- 1. BIG DATA BANKING Customer vs. Accounting Henry Sampson Lead, Engineering & Research DreamOval Limited @henrysampson
- 2. CORE BANKING SYSTEM (CBS) CORE Banking: “a back-end system that processes daily banking transactions, and posts updates to accounts and other financial records” Gartner C.O.R.E Centralized Online Real-time Environment
- 3. EVOLUTION
- 4. DESIGN EVOLUTION OFTHE CBS Decentralized Branches Centralized Branch Network SOA Based CORE Banking
- 5. DECENTRALIZED BRANCHES Each branch has it’s own server Transactions take at least one day to reflect in account Data sent to Head Office at EoD I.T. Operations Nightmare for EoD staff Customer transaction oriented design
- 6. CENTRALIZED BRANCH NETWORK One datacentre and many “dumb-clients” at branches Branches access application via radio All transactions are real-time Less hustle at EoD Mostly concentrated on core banking functions like Deposit Accounts Loans Mortgages Payments Easier multi-channel integration Customer transaction oriented design
- 7. SOA BASED CORE BANKING New architecture to promote service modularity Easier to integrate existing banking application Give more flexibility to banks to use the best tool for each function Meets most current demands of banks of today Deploying services at the speed of thought SLA monitoring on services Creating impossible services Customer services oriented design
- 8. BUT IS SOA THE FUTURE?
- 9. WHAT HAS DRIVEN THE CHANGE?
- 10. EVOLUTION OF CUSTOMER DATA Central Bank’s KYC Requirements Service requirements What ‘s next?
- 11. WHAT’S NEXT? Understanding what’s next for the customer What additional data is required? Relationships Family, friends, co-workers, etc. Interests Music artiste, car brands, home décor, photography, etc. Events wedding, birthday, graduation, etc. Location What kind of places do s/he visit and how often? Aspirations Dream car, dream house, dream job, etc. CUSTOMER’S LIFE STORY
- 12. HOW TO STORE AND USE CUSTOMER’S LIFE STORY
- 13. WHY IS STORAGE AN ISSUE The data being stored has three distinct attributes Volume Velocity Variety Traditional Relational Databases reach their breaking point on commodity servers very fast Buying specialized hardware is not feasible for most businesses A solution that is widely accessible must use commodity servers to do what is reserved for mainframes and supercomputers For Africa, an extra requirement is that the software must be reasonably priced } BIG DATA
- 14. DREAMOVAL’S OPEN SOURCE BIG DATA STACK
- 15. OPEN SOURCE BIG DATA STACK Hadoop MongoDB Mahout Application Layer Apache Pig
- 16. WHY OPEN SOURCE?
- 17. OPEN SOURCE Better control over price Removes vendor lock-in Reduces barrier to entry Reputation of creators Wider community of users (Best alternative in the absence of standardization) Apache Licenses are Enterprise Friendly
- 18. HOW TO USE CUSTOMER’S LIFE STORY
- 19. REALTIME ANALYTICS Map customer’s upcoming events to financial products Map customer’s friend’s upcoming events to fianacial products Map upcoming social/professional events to customer preferences (may add financial products) Map customer shopping wishlist to financial product Map spending pattern to social data
- 20. LEARNINGTO BE BETTER Learn about the classifications that worked and those that didn’t Learn about patterns that may not be evident with clustering Use clustered to data to form new classifications Basic question being answered is: Given the demographic, professional, social and transaction data of a customer what service would be the best next sell?
- 21. WHERE TO GET LIFE STORIES
- 22. SOURCES OF LIFE STORIES Social Media Networks – Relationships, interests, events, check-ins, etc eCommerce websites – Shopping history and whislists Telcos – Mobile money and locations data Device Manufacturers – All mobile data BEST Strategic Partnership Device Manufacturers
- 23. POSSIBLE ISSUES Although regulations allow such data to be used with customer consent abuse may lead to tighter laws Customers may not trust bank with such data
- 24. CONCLUSION
- 25. The next frontier of banking is to use big data technologies to rightly predict customer needs.The competition will be who has the clearest crystal ball.
- 26. QUESTIONS?