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Unlocking the power of commercial bureau and regional economic data in forecasting loan performance
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Cris deRitis Moody’s Analytics
Dan Meder Experian
Introducing:
@ExperianVision | #vision2015
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Small business as growth engine
Capital adequacy requirements and small business lending
Correlating macro economic factors and small business performance
Best practices in loss forecasting and stress testing
Summary
Agenda
Data
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Firms with less than
100 employees make up
98% of all businesses
in the U.S.
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Growth Firms with less than
100 employees
showed substantial
employment growth
in H1 2014
Insights
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Over 50% of Small
and Medium Enterprise
(SME) lending
is conducted by
banks with assets
over $1 billion
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Capital adequacy
requirements and
small business
lending
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WHAT IS REQUIRED? The aim of the annual reviews is to ensure that
large financial institutions have robust,
forward-looking capital planning processes
that account for their unique risks, and to help
ensure that they have sufficient capital to
continue operations throughout times of
economic and financial stress.
– Federal Reserve Board
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Economic
Instability in the
economic environment
calls for controlled
growth that balances
portfolio growth with risk
Technology
Potential for shifts in
regulatory, industry,
market and business
dynamics are
demanding more agile
and flexible technology
to minimize business
disruptions and support
real-time decisioning
Regulatory
Increased regulatory
requirements demand
sound development,
implementation and use
of credit models, a
stronger focus on risk
governance and
rigorous management of
liquidity and capital
Social
The Consumer
Protection Bureau
(CFPB) is mandating
more education,
transparency and
inclusive lending
practices to ensure
equal access to credit
and to protect
consumers from unfair
lending practices
Moving beyond the burden of compliance to uncover opportunity
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Correlating macro
economic factors
and small business
performance
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Analytics
11
Best practice combines
business performance
and macroeconomic
factors in determining
capital adequacy
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Macro economic factors influence on small business performance
Small business
performance
Economic growth
Employment Personal income
Consumer price index
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Payment and risk performance
► Region
► Industry
Economic
► Employment
► Personal income
► Retail sales
► Gross domestic product
► Personal consumption expenditures
► Industrial production
The combined power of economic and performance data
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Macro economic factors can affect different types of businesses in different ways
► Example: unemployment impact on retail performance
Correlating macro economic factors with the unique characteristics of your portfolio can increase predictiveness
► Geographic
► Business size
► Industry
Identifying higher risk segments of the portfolio will likely result in better identification of true reserves
Obtaining a well-rounded view of your portfolio
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How payment performance can vary by region…
Percent of balances past due (firms with fewer than 100 workers)
Sources: Experian, Moody’s Analytics
0.80 to 5.43
11.57 to 29.50
5.44 to 11.56
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…and by industry
Best performing industries by delinquency percent
Source: Business Information Map
1%
2%
3%
4%
5%
6%
7%
Q1 2013 Q2 2013 Q4 2013 Q1 2014 Q2 2014 Q3 2014 Q4 2014
Public Services Utilities
Health Services Education Services
Hospitality
0%
5%
10%
15%
20%
Q1 2013 Q2 2013 Q4 2013 Q1 2014 Q2 2014 Q3 2014 Q4 2014
Business Services Real EstateTransportation FinanceConstruction
Worst performing industries by delinquency percent
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Best practices in
loss forecasting
and stress testing
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SEEING THE FUTURE
Prediction is very difficult,
especially if it's about
the future.
– Niels Bohr
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Credit loss models for regulatory stress testing requirements:
► Sound, transparent, well-understood and owned by their users
Models need to be sensitive to economic conditions
Documentation is critical
► Conceptual underpinnings/assumptions and forecast results
Objectivity
► Let the models speak
Consider the alternatives
► Data
► Techniques
► Opinions
Best practices in loss forecasting and stress testing
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MOONSTRUCK
MOMENT There are three kinds of pipe. There's what you
have, which is garbage - and you can see where
that's gotten you. There's bronze, which is pretty
good, unless something goes wrong. And
something always goes wrong. Then, there's
copper, which is the only pipe I use. It costs
money. It costs money because it saves money.
— Cosmo Castorini
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Scorecards
► Rank-ordering originations, acquisitions and servicing queue
Roll rate models
► Moving average 30-to-60, 60-to-90, etc., delinquency rates
► Easy to calculate but assumes stability of performance
► Moving averages are short-lived
Expected loss models
► Estimate losses as a function of the probability of default (PD), loss given default (LGD) and exposure at default (EAD)
EL = EAD * PD * LGD
► Focus on economic sensitivity as well as backtesting
Alternative modeling approaches for loss forecasting and stress testing
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The complex life of a loan
O months delinquent
1 month delinquent
2 months delinquent
3 months delinquent
4 months delinquent
Charge-off (Gross $)
Recovery (=1-LGD)
Net Loss $
Originations/ Sales
Prepayment
Receivable Income
• Interest Income
• Fees
• Collection Expenses
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Micro approach: Loan/account level models
► Incorporate borrower and loan level detail (Big Data)
► Longer processing and simulation times
► May be less sensitive to broader economic trends and feedback effects
● Attenuation bias
Macro approach: Cohort level models
► Collapse borrower and loan level information to cohorts
► Shorter processing and simulation times
► Better suited to aggregate economic data
Multiple approaches are ideal
Expected loss model strengths and weaknesses
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Microeconomic model used to forecast account or loan level performance
Competing risk framework is common
Estimate discrete-time hazard
X has account-specific and time-varying economic factors
Aggregate economic information is correlated with individuals but may introduce noise
► Metro-level unemployment rate is an imprecise approximation of individual-level unemployment probability
Expected loss models Micro approach
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Macroeconomic model used to forecast macro-level performance like portfolio loss
► Capture broader trends, interdependencies and cycles
Portfolio losses especially stress losses are macro-dependent
Credit performance by regional geography may offer better match with regional economic data
► Higher correlation = lower noise
► Greater precision = Less excess capital
Expected loss models Macro approach
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Fed favors an aggregated approach for small business loans
Fed favors an aggregated approach for small business loans due to limited data
The other retail lending product portfolios include the small business loan portfolio, the other consumer loan portfolio, the student loan portfolio, the corporate credit card portfolio, and international retail portfolios. Due to data limitations and the relative small size of these portfolios, loan level models of default are not feasible.
Comprehensive Capital Analysis and Review 2012: Methodology for Stress Scenario Projections March 12, 2012
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Cohort loss models
Segment portfolio by product, geography, risk segment and vintage
Compute default rates each reporting month by cohort
Estimate PD = f(credit quality, age/maturation, the economy)
Leverage panel data and regional heterogeneity
Economic data is consistent with outcome providing appropriate sensitivity for stress testing:
► Default rate in Philadelphia directly related to unemployment rate in Philadelphia
Expected loss models Macro approach
Age V2011Q1 V2011Q2 V2011Q3 V2011Q4 V2012Q1
1 0.0% 0.0% 0.0% 0.2% 0.0%
2 0.5% 0.1% 1.0% 0.5% 0.3%
3 1.0% 0.1% 1.5% 1.0% 1.0%
4 1.5% 0.4% 1.7% 2.0% 2.4%
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Fed provides national scenarios
2015Q1 Severely Adverse Scenario for Unemployment Rate
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Local economics matter… Idiosyncratic scenarios stress geographic and industry concentrations
2015Q1 Severely Adverse Scenario
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Champion/challenger models give a broader view
► Over-reliance on single model technologies during US recession
► Leverage strengths of multiple approaches
► Fully transparent, back-tested and documented econometric loss forecasting models customized to specific portfolios
Benchmarking
► Experian industry data exist across multiple segments
► Industry data can fill in portfolio data deficiencies for modeling
► For small portfolios, no choice BUT an industry model
Supporting forecasts with external data
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Some final
thoughts…
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If you don’t have a loss forecast policy for your small business portfolio… get one
If you do, strongly consider what we’ve discussed today
But either way, we’re here to help. Come talk to us!
Some final thoughts…
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For additional information,
please contact:
@ExperianVision | #vision2015
Follow us on Twitter:
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