Leveraging Big Data and Cognitive Technologies for Credit Risk Analytics and Credit Management

Dr. Chris Marshall

Agenda

• Pressures on Bank Risk Management

• Priorities in Credit Management

• Traditional Approaches to Credit Risk

• Linking Reputation Risk and Credit Risk

• Case Studies - Big Data Tools for Credit Risk

• Technology and Data Architecture

• Agile Approaches for Unstructured Data

• Implementation Challenges

© 2016 International Business Machines Corporation

Pressures on the Bank Risk Management

Trends in Banking Implications for Risk

Management

Greater Customer

expectations

Consistent and Easy Online and Mobile

experience

Regulatory Demands New regulations – BCBS239, AML, KYC

Increased enforcement

Internal Management

demands

Higher quality and more timely reports

Better customer segmentation, risk based

pricing, capital allocation, early warning

systems

Pressures from Fintech

companies

Low Bank RoE < Cost of Capital

Risk and Value based customer targeting,

Fintech pressures in banking

McKinsey Banking 2016

Innovation starts with

retail and leverages

their massive data sets

Existing Fintech

priorities -Facilitating

Payments and

supporting

lending/financing

Opportunities – SMEs,

account management,

Corporates, capital

markets

Big Data related Priorities in the credit process

Sales Analysis Contracts Issues EWS Report

Generation

Workout

strategies

Onboarding Scoring &

Ratings

Collateral Responses Business

Intelligence

Restructuring

Pricing Application Collections

Decision

REVENUESRISK COSTS

Analytics

Portfolio

Mgmt

Macro factors

Portfolio effects

Network effects

Portfolio

Analytics

LTV, CrossSell,

NBA,Collections

Profitability

Analytics

Obligor Models

• Scores

• Ratings

• PDs

• LGDs

• EADs

Credit Risk

Analytics

Portfolio

Mgmt

Credit

Mgmt

Relation-

ship Mgmt

Customers’

Reputational

Data

IDs

Exposures

Products

Collaterals

Credit

Data

Events e.g.

NPLs, change in

status, new

products, new lines

Actions, e.g., new execs,

LOCs

KRIs

Traditional Approach to Credit RiskCredit Risk models based on internal structured data (events/actions)

Str

uctu

red

Info

rmation

Segments,

Financials,

Transactions, Inte

gra

tion

What makes your best relationship managers

or senior credit officers successful?

Highly skilled individuals with vast experience who

leverage insights and understanding of their clients

thrive at

• Developing & maintaining relationships

• Local insights into Structuring deals

• Sensitivity in reviewing and underwriting credit

requests

• Understanding linkages in credit portfolio

• Interpretation of soft information about long term

future prospects- macro factors, management

skills, company strategy or industry market

share

• In short they assess Reputational Risks – the

potential for negative news at some future date will

effect customer base, costs, or revenues.

• RepRisk is a predictor of future credit risk

and future profitability.

Handle more information

Quicker Credit Decisions

Greater consistency

With formal audit trail

Continuous improvement

Better Pricing

EWS - More responsive risk

monitoring

Collateral Monitoring

More complete risk estimates

based on unstructured data

e.g. payments, behaviors,

locations, networks

Big Data enables Reputational

Risk monitoring

The New World of Risk –Linking Credit Risk and Customers’ Reputations

The New World of Risk –Linking Credit Risk and Customers’ Reputations

Str

uctu

red

Info

rmation

Inte

rnal

UnS

tructu

red

Info

rmation

Exte

rnal

Un

Str

uctu

red

Info

rmation

Assessment

Extraction

Events

Actions &

Behaviors

Intentions

Inte

gra

tion

KRIs/KPIs

Segments,

Financials,

Transactions,

• Text

Analytics

• Data

Mining

• Analytics

Models

Mappin

g &

Routing

Analytics

Sentiments

& Habits

Context

Portfolio

Mgmt

Macro factors

Portfolio effects

Network effects

Portfolio

Analytics

LTV, CrossSell,

NBA,Collections

Profitability

Analytics

Obligor Models

• Scores

• Ratings

• PDs

• LGDs

• EADs

Credit Risk

Analytics Credit

Mgmt

Relation-

ship Mgmt

Linkages

Trends

Sources

Locations

Customers’

Reputational

Data

IDs

Exposures

Products

Collaterals

Credit

Data

Portfolio

Mgmt

Use of Sentiment AnalysisTools and Techniques for Credit Risk – Simple Example

Credit Management Dashboard Augmented with SentimentsTools and Techniques for Credit Risk – Simple Example

Linking unstructured data with Financial Credit DataTools and Techniques for Credit Risk

Target Entity Target from Text Sentiment Level

Personal Computers PC Shipments High Negative

Personal Computers PC market Medium Negative

Microsoft MSFT Low Negative

Target Entity Target from Text Sentiment Level

Tablets Tablet High Positive

Tablets WiFi only tablet Medium Positive

Case Study: Extracting and Mapping Sentiments from Analyst reports for Large Corporate Clients at Large French Bank

Case Study –Extracting and Analyzing Industry Blogs for large commercial companies at large US Bank

Real-time tracking of

consumer feedback

Aggregate consumer

feedback by location

Real-time tracking of

consumer feedback

Real-time Complaint & Sentiment Tracking

Real-time Competitive Intelligence

Feedback by Location

Object of sentiment is crucial.

Products are mapped to company

entity

Automatically discover which accounts

are businesses

Link different accounts belonging to the

same business (different departments,

local branches, etc)

Case Study – Extracting, Mapping and Analyzing sentiment from Social media comments about companies at large US Bank

position history

committee

membership

Who Is James Dimon?Do these filings refer to the same person ?

variability in the person’s name

lack of a key identifier

supporting attributes vary depending on the context (form type)

Case Study – Mapping and Integrating Credit Risk Data by Individuals within Corps at large US Bank

Comprehensive view of publicly traded companies and related people based on regulatory filings

Annual Report Loan Agreement

Proxy Statement Insider Transaction

Counterparty Relationships

Loan Exposure

Company

Person

Extract Integrate

Over 2200 financial companies

Over 32000 key officials

in financial companies

Over 1 Million documents

2005 2010

Filing

timelineSEC/FDIC Filings of

Financial Companies

(SIC Codes 6000-

6799)(Forms 10-K,8-k, 10-Q, DEF 14A,

3/4/5, 13F, SC 13D SC 13 G

FDIC Call Reports)

Case Study - Integrating Credit Risk Data by Institutions at Large US Bank

Implications for Chinese State Owned Enterprises

19

Case Study – Extracting, Mapping, Integrating and Analyzing comments about Competitors at large US Bank

Key Reputation Issues

New Events and their

effects on stock prices

Media response on

French bank IT

vulnerabilities

Competitive Analysis – Who is known to have what issues

A lot of buzz around

• Operational risks

and exposures

• Legal issues and

compliance

• Industry issues

such as mortgage

and credit risks

• Customer service

Overall Customer Sentiment Comparison

AMEX is viewed

positively in general,

especially in customer

services and fraud

areas.

Citigroup in general

has negative

sentiment associated

with it. Suntrust is

viewed as neutral.

Examples of blog

postings on AMEX

customer services and

fraud handling.

Case Study – Extracting, Mapping, Integrating and Analyzing comments about Competitors at large US Bank

New architectures to leverage big data & analytics

Data inMotion

Data atRest

Data inMany Forms

Information

Ingestion and

Operational

Information

BI & Performance

Management

Predictive Analytics

& Modeling

Exploration &

Discovery

Intelligence

Analysis

Data Lake

Landing Area,

Analytics Zone

& Active Archive

Raw Data

Structured Data

Text Analytics

Data Mining

Entity Analytics

Machine Learning

Real-time

Analytics

Video/Audio

Network/Sensor

Entity Analytics

Predictive

Enterprise Warehouse

& Mart Zones

Reporting

Structured &

Governed

Multiple LOBs

Stream Processing

Data Integration

Master Data

Streams

Information Governance, Security and Business Continuity

Analytic Appliances

Dedicated analytics

processing

High volume, high

complexity

Predictive

Agile Approaches to deal with Unstructured Data

Identify Topics and Issues

to monitor

User definesanalytical models

(Rule Editor)Sources

Issues

“Only selected segments

“snippets” of an article which

discussed the intersection of

the topic are selectedCo-occurrence

Analytics

Trend by monthTopic vs Entity

Topic Classification

SentimentClustering

User interacts with data to

discover insight

new topics

Dashboard Analysis Reporting

Extract Blogs, Boards, News

sources, Forums, Complaints, NGO’s,

CRM, and Internal structured data

Strong, Weak, Emerging Signal Alerts

Trending and SentimentAnalysis

Companies

Now, it isn’t necessarily all easy:

• Getting off the ground

• Defining the Business Case

• Integration across silo systems

• Data Privacy

• Access to Unstructured Data Sources

• Analysis of sentiment and behaviors

rather than transactions

Implementation Challenges

• Access to Big data and Data mining skills

• Tolerance of iterative model building and

backtesting

• Separating Sentiment (signal) from

textual documents (noise)

• Integrating insights with existing

structured sales, financial and risk data

typically stored in traditional relational

databases

© 2016 International Business Machines Corporation