Download - OFSAA - BIG DATA - IBANK
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Financial Services Global Business Unit Analytics and Big DataAmbreesh KhannaVP, OFSAA Product ManagementFSGBU
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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
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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OFSAAHigh Level Architecture
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Use Case – Customer Attrition
Customer Id: 12345
Name: Jane DoeMarital Status: SingleOwns house: NNo. of children: 0
CASA accountBi-weekly Direct depositAvg. Balance: $10K
Gold cardLimit: $10KBalance: $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 DoeMarital Status: SingleOwns house: NNo. of children: 0
CASA accountBi-weekly Direct depositAvg. Balance: $10K
Gold cardLimit: $10KBalance: $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 record2. 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 AttritionFunctional Flow
Weblogs, emails, call records
Cor
e B
anki
ng, C
RM
User or segment matched
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Use Case – Trader and Broker Compliance, Internal Fraud
1TC/BC/Fraud software monitors patterns of trading activity
2Additional data points to be provided to TC/BC/Fraud software• Emails, SMSs, IMs, weblogs, social updates
3Models 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
31. 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 detection2. 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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SO
AP
C+
+ P
ipe
s
Nat
ive
FraudTechnical Architecture
FSDF(DB 11.2.0.2+with ORE)
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)
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
Hiv
eQL
AAI
d
Behavior Detection
Inline-Processing Engine
OLTPSystems
I
I I I
I I
a I
MSG queues
OC
I / J
ND
I-JD
BC
ODBC
Endeca / OBIEE
AAIAAI
AAI
R-connector for Hadoop
ORE native connectivity
Collective-Intellect
HiveQL
or EID*M/R – Map Reduce
*M/R
*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
HiveQL
bI
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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 MetricsPD, 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 MetricsPD, LGD, EAD,
Default CorrelationsFront
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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Graphic Section Divider
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