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How data analytics could boost financial industry

Yogendra (Yogi) SinghSolution Consulting Head - Oracle Financial Service Analytical Applications

Apr 27, 2018

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Adding Big Data Successfully to the mix is Hard

“Only 27% of respondents described their Big Data initiatives as ‘successful’ and only 8% of respondents described them as ‘verysuccessful.’ In fact, organizations were found to be struggling even with their Proof-of-Concepts (PoCs), with an average success rate of only 38%.”

Capgemini Consulting, Cracking the Data Conundrum.

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Too many answers not enough questions

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Difficult to deliver consistent risk-optimized metrics across the enterprise

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Managing Your Data is Increasingly Complex

KYC

AML

FATCA and GATCA

CCO

Enterprise Fraud

Real-time offerCMO

Relationship Banking

Customer retention

Customer 720°

CROCCAR

Basel

BCBS 239

LRM

CFO

Profitability

CDO

BCBS 239

Big Data

RegulatoryReporting

Financial Business Industry Ontology

(FIBO)

Significant overlap in data requirements with little or no data sharing

Expensive to satisfy regulators who are demanding granular, accurate and

standardized enterprise data in a timely fashion

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Data Commonality within Finance , Treasury and Risk

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Data CommonalityAsset Liability Management

Balance Sheet Planning

Funds Transfer Pricing

IFRS9 ProfitabilityBasel III -

Regulatory Capital

Liquidity Risk Management

Regulatory Reporting

Asset Liability Management 100% 100% 78% 50% 73% 19% 16% 26%

Balance Sheet Planning

92% 100% 71% 49% 67% 9% 13.4% 22%

Funds Transfer Pricing

94% 94% 100% 47% 93% 20% 17% 28%

Profitability

92% 91% 97% 48% 100% 20% 17% 31%

Regulatory Reporting

65% 61% 58% 88% 61% 71% 94% 100%

Based on Oracle Financial Services Analytical Applications Data Requirements

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Information Excellence Strategy

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Multi-Business &

Opti-ChannelAdvisor

Banking, Insurance and Wealth Services

Meta

data

, R

efe

ren

ce D

ata

, B

usi

ness

Ru

les,

Data

Qu

ality

Information as a Service

Info

rmati

on

G

overn

an

ce

Operational

Analytical

Structured data, curated Un-Structured , Semi Structured , Agile

Mobile OnlinePhone

Productized Reporting Self-Service Reporting

Analytics, Modelling

& Visualization Machine Learning

ATM Branch

Information leverageCustomer understanding

• Customer

• Account

• Collateral

• Payments

• Offer

• Interaction

• Channel BI

• Leads

• Pre-approval

• Next offer

• Pricing

• Decisioining

Enterprise Data Provisioning Platform (EDPP)

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Analytics in Action…

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Reduce Compliance cost

Optimizing Finance OperationsCustomer Insight is at the center of a great customer experience

Jennifer Morris

Current Financial Scenario

Demo Highlights

• Use of machine learning to determine Jenny’s Next Best Offer

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A 31 year old married mother of two. She earns $ 110,000 and is owner of a home in Washington DC.

• One last payment to make on her car loan• Has set up a modest travel goal• Has Airmiles Credit Card which she pays in full

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Initial Offer Set based on Collaborative Filtering

Forex CardHolidayLoan

BalanceTransfer

Buy to LetMortgage

529 PlanCASA feeWaiver

RewardsCard

Money Manager

CashbackCard

3.

A B C

D E END

F G

BEGIN

C1……C300……C5000….…..C100k….….C1 mn

User Activity Capture 1.

Next Best Offer : Initial Offer Palate

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Microsegmentation based on Nearest Neighbor

2.

Top Ranked offer goes out as Recommendation on preferred channel

Adjustments to backend rank based on bank strategy and offer performance

Next Best Offer : Final Offer Recommendation

Forex Card

HolidayLoan

BalanceTransfer

Mortgage529 Plan

CASA feeWaiver

MilesCard

Rewards Card Money

Manager

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Next Best Offer with Machine Learning

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Defining a New Saving Goal and Funding It

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Click icon to add picture

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