OFSAA - BIG DATA - IBANK

Financial Services Global Business Unit Analytics and Big DataAmbreesh KhannaVP, OFSAA Product ManagementFSGBU


Program Agenda

Big Data – what does it have to do with OFSAA?

Customer Analytics

Fraud

Default Correlation for Securitized Bond Prices


Oracle Financial Services Analytical Applications

FSDF


Relationship Pricing NBO Reputational Risk Fraud, AML, TC/BC Valuations for Credit Risk Payments Analytics Unified Data Model

OFSAA and Big Data

Use cases


OFSAA – Current Architecture


OFSAAHigh Level Architecture


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


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


Customer AttritionFunctional Flow

Weblogs, emails, call records

Cor

e B

anki

ng, C

RM

User or segment matched


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


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


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


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


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


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 Agricultural 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


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 Agricultural 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


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


Graphic Section Divider

OFSAA - BIG DATA - IBANK

Business

Transcript of OFSAA - BIG DATA - IBANK