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Opportunities of Big Data in Banking Risk Management

Sumit Dhar

Presentation at Interactive Enterprise Big Data SummitOctober 31st – November 1st, Worldwide Online Event

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Today’s Focus

Risk functions in banks are entrusted to manage variability in business ecosystem to ensure any adverse impact on capital or financial results are mitigated

Focus is on recent advances in 'Big Data' ecosystem that Banking Risk function can leverage

The focus is on the big data ecosystem and opportunities for Risk organizations in Banks

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Understanding Banking Risk

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In general the types of risks faced by a Bank and agreed upon Internationally are identified by Basel Accord and are classified as the following categories:

•Risk arising from the changes in interest rates, FOREX, equity and commodity prices

•E.g. Thrift failure in the US during 1980-90s, dot com bubble burst in early 2000s

Market Risk•Risk of loss resulting from inadequate or failed internal

processes, people and systems or from external events•E.g. 1995, Barings Brothers Company Authorizing his

own lossy trades in SingaporeOperational

Risk

•Potential loss a bank would suffer if a borrower fails to pay interest or repay principal

•E.g. A homeowner stops making mortgage payments

Credit Risk

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Leveraging Big Data for Risk Management

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Discover Measure Monitor Manage

Risk Management RhythmKn

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Risk

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Signal

New insights from shifting behaviorUsage of internal and external data Advanced Techniques- Simulations, Ensemble Models, Semantic Analysis Big Data- click streams, web logs, 3rd party transactions

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The Banking Big Data Situation

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With a shifting paradigm in Business Models, Banks are now exposed to different types of data sources that have high volume, velocity and vary in structure

Payments

Social Media

Data Services

Infrastructure as a Service

Channels

India: 600M

Prospects

India M-Bank Txn: 2012 85%

growth

30 m fb pieces/m

400 tweets/d

ay

4 B/ day

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Examples of Big Data Opportunity areas in Risk Analytics

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1. Enhanced ‘Big Data’ enabled Visualization

2. SoLoMo Credit Scoring

3. Crowd sourcing Advanced Analytics

4. Unstructured Data Integration & Relationship visualization

5. Extensible Big Data Discovery Infrastructure

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1. Opportunity: Enhanced ‘Big Data’ enabled Visualization

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Adopted from AT Kearney- Big Data and the Creative Destruction of Today's Business Models

Acknowledged with thanks Tableau Software + Teknion data solutionshttp://www.tableausoftware.com/solutions/banking-analytics

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2. Opportunity: SoLoMo Credit Scoring

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Premise- digital life is a veracious footprint of actual life and weight of the social relationship and sentiment can be used to underwrite individuals

Poor Below Avg Fair Good Excellent

The key to getting a successful loan is having a handful of highly trusted individuals in your social networks

Looks at social media activity to ensure that factual data provided on the online application matches

Consider up to 8,000 pieces of web site interaction data and perform credit checks for loan decision- TAT 30 mins

“Scoring as a service," looks at 8,000 indicators, such as "location data, social graph, behavioral analytics, people’s e-commerce shopping behavior and device data

Collects on average 7000 data points for each of your applicants from Facebook and scores them real time

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3. Opportunity: Crowd sourcing Advanced analytical Solutions

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…Finding these new risks may require a new mindset focused on discovery. Those charged with finding these new risks have been too focused on measuring and managing the known risks.. E. Paul Rowady, Jr, TABBB Group

1

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4

5

0.86320.8634

0.86360.8638

0.864

AUC

Open: 3.5 months Entries: ~ 1000Data: 150K + 110K [Train + Test]

Crowd sourci

ng Competition

Best solutions crowd sourced ensemble solution

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4. Opportunity: Unstructured Data Integration & Relationship visualization

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Public + Private Data sources Advanced Data Integration- structured/ unstructured etc Search & Discovery- space, time & Relationship, NLP MPP of Data in NoSQL

SG Electronics

VISA: 5001522241113111

SKU: 5215429

Knowledge Graph

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5. Opportunity: Extensible Infrastructure for Big Data Analytics in the cloud

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Utility Computing: pay-as-you-go computing High Data Volume work MapReduce implementation supporting upto 20

PB of data per day Large RDBMS implementation support e.g. MySQL Analytics Application providers migrating to cloud

infrastructure

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Barriers to adoption of Big Data Environments in Banking Risk

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1. Lack of clarity of new advanced technologies amongst senior Business Leaders

2. Organization culture, sponsorship and accountability of Big Data programs

3. Skills shortages- Credit Risk Organization with considerable compliance overhead find it difficult to repurpose staff

4. Regulatory environment that is averse to cloud implementation

5. Regulatory environment that restricts banks to act as Data Service providers

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Parting Thoughts on Opportunities

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Thank You