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China Forum Presentation v2.0
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Transcript of China Forum Presentation v2.0
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