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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
Henry Sampson Lead, Engineering & ResearchDreamOval Limited@henrysampson
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
EVOLUTION
DESIGN EVOLUTION OF THE CBS
Decentralized Branches
Centralized Branch
Network
SOA Based CORE
Banking
DECENTRALIZED BRANCHES
Each branch has it’s own serverTransactions take at least one day to reflect in account
Data sent to Head Office at EoD I.T. Operations Nightmare for EoD staffCustomer transaction oriented design
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
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
BUT IS SOA THE FUTURE?
WHAT HAS DRIVEN THE CHANGE?
EVOLUTION OF CUSTOMER DATA
Central Bank’s KYC Requirements What ‘s next?
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
HOW TO STORE AND USE CUSTOMER’S LIFE STORY
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
DREAMOVAL’S OPEN SOURCE BIG DATA STACK
OPEN SOURCE BIG DATA STACK
Hadoop
Mon
goD
B
Mahout Application Layer
Apache Pig
WHY OPEN SOURCE?
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
HOW TO USE CUSTOMER’S LIFE STORY
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
LEARNING TO 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?
WHERE TO GET LIFE STORIES
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
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
CONCLUSION
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.
QUESTIONS?