Transcript Title of Presentation

Non-Traditional Data Sources
for Analytic Models
Gautam Gupta
Senior Manager—Risk Analytics
Emirates NBD
© 2014 Fair Isaac Corporation. Confidential.
This presentation is provided for the recipient only and cannot be reproduced or shared without Fair Isaac Corporation’s express consent.
Traditional risk models can be improved by using Non-Traditional
data like CASA, debit card transactions, corporate data, paper trails
etc. especially in geographies where credit bureaus don't exist or for
segments with no credit history.
Data Enrichment Campaigns and Customer Level Aggregation can
improve and in most cases add to the use of Non-Traditional data in
credit models.
Models developed on Non-Traditional data can open credit access to
under served customer segments at affordable prices.
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© 2014 Fair Isaac Corporation. Confidential.
Agenda
►Deficiencies
in Traditional Risk Modeling
►Non-Traditional
►Data
►The
Data Sources
Enrichment Campaigns
Need for Customer Level Aggregation
►Regulatory
Issues with Non-Traditional Data
►Use
of Non-Traditional Data Sources in
Emirates NBD
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© 2014 Fair Isaac Corporation. Confidential.
Deficiencies in Traditional Risk Modeling
► In
this section we discuss the deficiencies associated with the traditional
data/processes in risk modeling
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© 2014 Fair Isaac Corporation. Confidential.
Risk Modeling Lifecycle
Lifecycle Stage
Data Depth
Risk Models
On boarding
Relationship
Loyalty
Application + Bureau
Product Behavior +
Bureau
Customer Behavior +
Bureau
Application Scores
Account Behavior scores
Customer Risk scores
Models become more predictive as data depth
on each customer improves—best at loyalty stage
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© 2014 Fair Isaac Corporation. Confidential.
Traditional Data Sources
Internal Data
Credit
Application
Internal Debt
Repayment
External Data
Credit Card
Transactions
Credit Bureau
Report
Bureau provides current debt performance but not future customer potential;
biased by prevailing economic conditions
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© 2014 Fair Isaac Corporation. Confidential.
Credit Modeling in Banks: Challenges
1. Credit Bureau
No Credit
Bureau
Developing
Countries
Expatriate
Population
Underserved
Segments
2. Missing Customer Dimensions
Career Growth
Industry/Employer Risk
Life Stage Burden
Lifestyle Risk
3. Regulatory Overload
Credit Scorecard
PD Model
Most of the innovation in credit modeling now comes from NBFC’s,
a result of their need for automation and lower regulatory burden
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© 2014 Fair Isaac Corporation. Confidential.
Non-Traditional Data Sources
► In
this section we discuss some of the non-traditional data sources that can
be used for risk models along with examples of modeling variables
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© 2014 Fair Isaac Corporation. Confidential.
The 360o Customer Profile
Employment/Business
E.g. Income, Designation
Safety Net
E.g. Spousal Support
Debt Repayment
E.g. Payment on due debts
Investment Profile
E.g. Deposits vs. Shares
Lifestyle
E.g. Frugal vs. Extravagant
Circumstances
E.g. Health issues
Customer Financials
E.g. Liquidity, Savings, Cash flow
Customer Life Stage
E.g. Had a baby
Bank Relationship
E.g. Product holding, Age on Books
Traditional data sources cover Employment and Debts in most cases, other dimensions
may be covered to a varying degree. In next few slides we describe a few non-traditional
data sources that cover these other dimensions.
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© 2014 Fair Isaac Corporation. Confidential.
Non-Traditional Data Sources
Checking/Saving/Deposit Account Data
CASA
CREDITS
Salary
Investment
returns
DEBITS
Inward
Remittances
Debt
Payments
Rent
Outward
Remittances
Bills
POS
Analyzing Customer CASA very similar to analysing Company Financials
Liquidity
► Low
Balances
► Deferred Salary
Debt Service
► Service
Ratios
► Interest Payments
Cash Flow
► Credits
vs. Debits
► Rate of Debits
Profitability
► Savings
Rate
► Interest Income
Activity
► Transaction
Volumes
► Volatility
CASA data is extremely useful for Income Indexation exercises—
both for salaried and self employed customers
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© 2014 Fair Isaac Corporation. Confidential.
Profile
► CASA
Vintage
► ATM vs. POS
Non-Traditional Data Sources
Transaction Data
► All
transactions including CASA, Debit card, Credit card, Loan , Prepaid card etc.
Dimension
What to look for
What can it help identify
Lifestyle
Summary merchant analysis
Can help describe if customer is frugal or extravagant—e.g. heavy
purchase at luxury stores vs. discount stores
Life stage
Merchant Analysis
Can help identify life stage like having a baby, buying a car etc
Cash flow
Variation in transaction amount
with specific merchant categories
Can help identify cash flow issues e.g. getting gasoline for smaller
amounts as compared to previous months when larger amounts would
be spend at one go
Payment
Habits
Bill payments
Late bill payments may indicate bad payment habits or cash flow issues
Safety Net
Insurance payments
E.g. Payments for earthquake insurance while residing in a earthquake
prone area indicates a risk averse profile
An Asian Bank was able to use transaction information from
Remittance Prepaid cards to offer small ticket personal loans
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© 2014 Fair Isaac Corporation. Confidential.
Non-Traditional Data Sources
Corporate Data
► Currently
employment variables limited to income, designation etc., obtained from
application form
► Bank can use corporate data to profile customer employer
Company Profile
Corporate Financials
Payroll Data
Some Asian banks identify fluctuations in payroll (salary decrease, deferments etc.)
and use this to project layoffs
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© 2014 Fair Isaac Corporation. Confidential.
Non-Traditional Data Sources
Offline Data
► This
includes all data that is not data-entered into systems and is available as a paper or
pdf/image document
► Most
of these collected as salary or address proofs
Other Bank Statements
Tax Returns
Utility Bills
A Bank in South America already using payment and usage characteristics
from postpaid mobile bills to extend credit
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© 2014 Fair Isaac Corporation. Confidential.
Non-Traditional Data Sources
Other Data Sources
► There
are other data sources that can be used although for most of them their use is limited
by the fact that they will need to be procured from third party sources
Call Center
Investment
Profiles
Point Of
Sale
Loyalty
Programs
Insurance
Companies
Healthcare
Services
Census Data
Social
Network
Government
Services
Telecommunication
Companies
In developing countries state lenders already have access to limited
amounts of census/municipality data for welfare payments
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© 2014 Fair Isaac Corporation. Confidential.
Data Enrichment Campaigns
► In
this section we discuss the need for data enrichment campaigns and an
example of one such campaign
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© 2014 Fair Isaac Corporation. Confidential.
Data Enrichment Campaigns
Current
Framework
New Scorecard
Request
Improved
Framework
Gather
Available Data
Develop
Scorecard
Identify Data Gaps
Primary
Data Enrichment
Secondary
Customer Touch Points
Third party sources
At each interaction ask
a question/confirm a belief
Search for customer information in
third party databases
Data enrichment campaigns are a cost effective way of creating
new or enriching existing non-traditional data sources
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© 2014 Fair Isaac Corporation. Confidential.
Data Enrichment Campaign—An Example
50$ Profit
Optional Marketing
information block
on application
Launch
3 months later
10%
FILL
RATE
In 6 months
Next Month
40%
FILL
RATE
60%
FILL
RATE
70$ Profit
75$ Profit
In 6 months
Next Month
Reward
Increase Reward
20$
40$
Enhanced Segmentation should decrease the ‘Cost of Risk’
which in theory should be greater than ‘Cost of Data Enrichment’
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© 2014 Fair Isaac Corporation. Confidential.
6 months later
The Need for Customer Level Aggregation
► In
this section we discuss the necessity of aggregating non-traditional data at
customer level and examples of complex variables that can result from such
aggregation
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© 2014 Fair Isaac Corporation. Confidential.
Customer Level Aggregation
Benefits
Special Case:
Family Level Aggregation
360o Profiling
►What?
► Aggregate at
Consistency
of Information
Customer
Data
Aggregation
Family Unit
►Why?
Complex
Variables
► Safety Net
► Aggregate Financial
Behavior
►How?
Improved
Predictive
Models
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© 2014 Fair Isaac Corporation. Confidential.
► Common Address
► Family Surname
Product specific models result
in inaccurate segmentation—
which in turn leads to suboptimal decision management
Complex Variables
Situations Where Customer Risk Low from Individual Viewpoint—
Together May Show Other Wise
Ex. 1
Tax declaration
shows customer
doesn’t have health
insurance
Transaction data
shows heavy spends
in baby stores
Ex. 2
Liability data shows
customer broke fixed
deposit
Transaction data
shows non-essential
spends have doubled
Create single repository of all customer transactions (debit, credit, branch) to
profile customer (e.g. Lifestyle using wholesome merchant analysis)
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© 2014 Fair Isaac Corporation. Confidential.
Regulatory Issues with Non-Traditional Data
► In
this section we discuss regulatory issues with using non-traditional data
and possible solutions for such issues
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Regulatory Issues
Customer Consent
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Fair Practices
Data Integrity
Regulatory Issues—Suggested Solutions
Incentivize Customer
Data Sharing Agreements
Government Partnerships
Check Proxy Discrimination*
Data Validation
Fraud Monitoring
*Implementing Anti-Discrimination Policies in Statistical Profiling Models, By Devin G. Pope and Justin R. Sydnor,
American Economic Journal: Economic Policy 3 (August 2011): 206–231
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© 2014 Fair Isaac Corporation. Confidential.
Use of Non-Traditional Data Sources in
Emirates NBD
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© 2014 Fair Isaac Corporation. Confidential.
Use of Non-Traditional Data in Emirates NBD
UAE Credit Market
► No Credit Bureau
► Over 30 banks
► ~2 million Credit eligible population
► ~80% Expatriates
► Central Bank mandated customer
lending limits
► Majority credit loss from layoffs
Credit Modeling in Emirates NBD
Transactions
Paper
Statements
CASA
Credit
Models
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© 2014 Fair Isaac Corporation. Confidential.
Employer
And the Benefits
Improve predictive power
of credit scorecards
Scorecard GINI’s have improved by
over ~20%
PREDICT
PRICE
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Automate underwriting
process to reduce costs
Automated decisions for majority
consumer lending
AUTOMATE
LAUNCH
Risk-reward models to
generate optimal pricing
Launch new products to cater
to a wider market
Risk based pricing has benefited
product take up
New products have increased eligible
base by ~50%
© 2014 Fair Isaac Corporation. Confidential.
Thank You!
Gautam Gupta
[email protected]
© 2014 Fair Isaac Corporation. Confidential.
This presentation is provided for the recipient only and cannot be reproduced or shared without Fair Isaac Corporation’s express consent.
Please rate this session online!
Gautam Gupta
[email protected]
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© 2014 Fair Isaac Corporation. Confidential.