OFSAA, BIGDATA, EPM, BPM, BI, OBIEE, IBANK

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2. Financial Services Global
Business Unit Analytics and
Big Data
Ambreesh Khanna
VP, OFSAA Product Management
FSGBU

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Program Agenda
 Big Data – what does it have to do with OFSAA?
 Customer Analytics
 Fraud
 Default Correlation for Securitized Bond Prices

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Oracle Financial Services Analytical Applications
Performance Management & Finance
Model Risk
V2 061912
Performance
Management
Customer
Insight
Governance
& Compliance
Risk
Management
Hedge Management IFRS 9
– IAS 32/39
ICAAP/Risk Appetite
Customer Profitability
Stress Testing
Loan Loss Forecasting Pricing Management
Risk Adjusted Performance
Know Your Customer
Risk Management
Operational Risk & Compliance Mgt.Regulatory Compliance (Financial Crime)
Customer Insight
Anti-Money Laundering
Trading ComplianceBroker Compliance
Fraud Detection Operational Risk
Credit Risk
Institutional Performance
Retail Performance
Marketing
Customer Segmentation
Capital Management
Liquidity Risk
Economic Capital Advanced
(Credit Risk)
Operational Risk
Economic Capital
Balance Sheet Planning
Profitability
Asset Liability Management
Market Risk
Basel Regulatory Capital
Retail Portfolio Models and
Pooling
Funds Transfer Pricing
Reconciliation
Channel Insight
Compliance Risk
Business
Continuity Risk
Counterparty Risk
Audit
FSDF

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 Relationship Pricing
 NBO
 Reputational Risk
 Fraud, AML, TC/BC
 Valuations for Credit Risk
 Payments Analytics
 Unified Data Model
OFSAA and Big Data
Use cases

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OFSAA – Current Architecture

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OFSAA
High Level Architecture

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Use Case – Customer Attrition
Customer
Id: 12345
Name: Jane Doe
Marital Status: Single
Owns house: N
No. of children: 0
CASA account
Bi-weekly Direct deposit
Avg. Balance: $10K
Gold card
Limit: $10K
Balance: $7K
1
Event
• Customer gets married
2
Event
• Customer has a baby
• Opens 529K with competing bank
Event
• Customer buys a house
• Gets mortgage from competing bank
3
4
Event
• Customer consolidates accounts
• Moves all accounts to competing bank
5

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Use Case – Customer Retained with Better
Insights
Customer
Id: 12345
Name: Jane Doe
Marital Status: Single
Owns house: N
No. of children: 0
CASA account
Bi-weekly Direct deposit
Avg. Balance: $10K
Gold card
Limit: $10K
Balance: $7K
1Event
• Customer socially announces intent to
get married
2
Event
• Customer announces pregnancy and
eventually birth of child
6
Event
• Customer searches for mortgage on bank
website
4
1. Bank updates customer record
2. Runs propensity models for NBO
and makes time-bound loan offer
for $50K for wedding at next point
of customer interaction
3
1. Bank preapproves customer for
mortgage
2. Makes offer at next point of customer
interaction due to high propensity score
5
1. Bank analyzes purchase pattern and
predicts change in status; Augments score
with data from social networks
2. Makes 529K offer at next point of customer
interaction as per propensity score
7

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Customer Attrition
Functional Flow
Weblogs, emails, call records
CoreBanking,CRM
User or segment matched

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Use Case – Trader and Broker Compliance,
Internal Fraud
1
TC/BC/Fraud software monitors patterns of trading
activity
2
Additional data points to be provided to TC/BC/Fraud
software
• Emails, SMSs, IMs, weblogs, social updates
3
Models to find co-relation between events such as
large institutional trades and personal calls, or
employee accessing a articular customer activity on a
regular basis

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Use Case – Payments Fraud
1
Transaction persisted for detailed analytics
5
Real time fraud detection engine does rule
matching and machine learning models try to
enhance patterns
2
Additional data points
• User, address, geo-location previously known?
• Any known information from outside the bank
about originator or destination?
4
Approval/Denial response
Wire Transfer transaction
through Bank
• Enhanced user profiles and history kept on HDFS
• Behavior detection models run on Map Reduce
3

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Use Case – Anti Money Laundering
Monetary transactions
1
AML software monitors
• Large cash transactions (CTR)
• Patterns to identify money laundering (SARs)
• KYC (checks against negative lists)
2
Additional data points to be provided to AML
• External information about the customer
3
1. Graph analysis to detect patterns (vertices are
entities, edges are transactions)
2. Co-relation between SARs
4
1. Graph analysis is extremely relevant to fraud detection
2. Extremely large graphs cannot be analyzed with
traditional means – order of complexity is likely non-
probabilistic in time and space
3. Some of these problems are hadoop-able

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SOAP
C++Pipes
Native
Fraud
Technical Architecture
FSDF
(DB 11.2.0.2+
with ORE)
BDA
(HDFS/
Cloudera )
Hive/NoSQL
Discovery / adhoc
Analytical Reporting
Source
Systems
Trxns
a
Stochastic
Modeling subsystem
(with „R‟ support &
ORE connectivity)
Scenario Definitions
(metadata)
Post-Processing
(pluggable services
framework)
Batch
c
b
b
b
CI
d“Sqoop”
Batch process
c
HiveQL
AAI
d
Behavior Detection
Inline-Processing
Engine
OLTP
Systems
I
I I I
I I
a I
MSG queues
OCI/JNDI-JDBC
ODBC
Endeca / OBIEE
AAI
AAI
AAI
R-connector for Hadoop
ORE native
connectivity
Collective-Intellect
HiveQL
or EID
*M/R – Map Reduce
*M/R
*M/R
Endeca Information Discovery
Web-services interfaces
included (WSDL)
move to
structured
store
additional
/enriched
attributes
Unstructured Data
Blogs
Newsfeeds
Watch List Scans
Financial / Marketing /Trade
data providers/channels
b
I

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Using Big Data to Estimate Default Correlation
Rating Agencies
Players involved in securitization transactions and their roles
Evaluate credit risk and deal structure, assess third parties, interact with investors, and issues ratings
Asset Manager Financial Guarantor
Servicer TrusteeOriginator
Arranger
Senior
Mezzaine
Junior
Investors
SPV
Assets Liabilities
Monitors complianceCollects & makes payments
Pay outsFunds
Funds
Pay outs Pay outs
Funds
Trades assets Insures tranches
Funds Pay outs
Loans to
Energy firms
Loans to
Agricultural
firms
Loans to
Textile firms
• Prices of Bonds (i.e. tranches) are very sensitive to default
correlation of loans
• We propose to use Big Data comprising of public and private
information, Bloomberg and Reuters feeds, commercial transactions,
analyst meets, and research reports to estimate default correlation
Bonds with
different ratings

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Estimating Default Correlation and Securitized Bond
Prices – Current State
Analytical Applications Infrastructure
(Data Quality, Metadata, Stress Testing, Modeling, Execution, Big Data Connectors etc)
Staging Area
Common Input
area for
analytical
processing
Data Quality Checks,
GL Reconciliations,
Manual Data
Adjustments
Application-specific
Processing Area
Valuations Engine
Stochastic Models to
estimate default
metrics
Results Area
Dashboards
and Reports
Bond and Tranche
Prices,
Attachment and
Detachment Points,
Regulatory Reserves
Credit Risk Engine
Market Risk Engine
Default Metrics
PD, LGD, EAD,
Default Correlations
Front
Office
Systems
(like CRM,
RTD etc)
Core
Banking
Systems
Treasury
Systems
Loans to Energy
firms
Loans to
Agricultural firms
Loans to Textile
firms
Basel Engine
Company
Specific Metrics
• Demographic,
Geographic and
Industry
information
• Company
Ratings
• Risky Bond
prices floated by
firms
• CDS spreads of
the firms
• Balance Sheet
structure and
information
OBIEE
• Currently the estimation of default metrics like PD, LGD and Default Correlation only considers structured information
• Unstructured but rich information contained in Big Data sources like Bloomberg and Reuters feeds and news reports,
Analyst comments and Research reports, News on commercial transactions etc. is completely ignored
• This results in poor default metrics and hence very poor and inaccurate Securitized Bond Prices
• Securitized Bond Prices are extremely sensitive to Default Correlation, and incorrect estimates of which was one of
the main causes of 2008 market crash

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Estimating Default Correlation and Securitized Bond Prices – Future State
Using Big Data Sources
Analytical Applications Infrastructure
(Data Quality, Metadata, Stress Testing, Modeling, Execution, Big Data Connectors etc)
Staging Area
Common Input
area for
analytical
processing
Data Quality Checks,
GL Reconciliations,
Manual Data
Adjustments
Application-specific
Processing Area
Valuations Engine
Stochastic Models to
estimate default
metrics
Results Area
Dashboards
and Reports
Bond and Tranche
Prices,
Attachment and
Detachment Points,
Regulatory Reserves
Credit Risk Engine
Market Risk Engine
Default Metrics
PD, LGD, EAD,
Default Correlations
Front
Office
Systems
(like CRM,
RTD etc)
Core
Banking
Systems
Treasury
Systems
Loans to Energy
firms
Loans to
Agricultural firms
Loans to Textile
firms
Basel Engine
Company
Specific Metrics
OBIEE
Big Data
Sources
• Bloomberg &
Reuters feeds
and news
• Analysts
comments and
Research
reports
• Commercial
Transactions
• Quarterly
Investor meets,
notes and public
announcements
• Augmenting traditional structured information with the new unstructured information from Big
Data sources will result in better estimates of default correlation and PD, LGD, EAD
• Better estimates of default will result in more accurate prices of Bonds offered to investors via
Securitization of assets
• Estimates of default can be updated quickly as new unstructured information becomes available

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OFSAA at OpenWorld
 Monday, September 23
– 2:30-3:30 Making Sense of the Regulatory Challenges Facing Banks Today & Tomorrow
 Tuesday, September 24
– 10:30-11:30 Driving Business Growth by Unlocking Rich Customer Insights
– 5:15-6:15 Advanced Analytics for Insurance
 Wednesday, September 25
– 10:15-11:45 Big Data in Financial Services
– 4:15-5:15 Use-Case Driven Approach to Using OFS Data Foundation for Data
Management Needs

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