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Successfully Transition to the New CECL Standard
Insights and Lessons Learned from
U.S. Federal Credit Agencies
August 8, 2017
©2017 FI Consulting. All rights reserved.
FI Consulting (FI) helps financial institutions address complex challenges across
risk, finance, and operations. We combine credit modeling, data, and technology
expertise with unique experience across the commercial and government sectors
to deliver solutions that apply leading practices from each.
ROBERT CHANG
Modeling Lead
ROMAN IWACHIW
CEO
MARK JORDAN
GSE Account Leader
INTRODUCTIONS
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AGENDA
BACKGROUND
LESSONS LEARNED
RECOMMENDATIONS Q&A
INTRODUCTIONSAGENDA
3
CECL VS INCURRED:
AN ILLUSTRATION
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BACKGROUND
FINANCIALS
AUDITS
STAKEHOLDERS
DATA
MODELS
TODAY 2020
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A template for CECL implementation exists in the Federal Government
▪ The Federal Credit Reform Act of 1990 (FCRA) mandated that US government credit
agencies model, financial report, and budget using a lifetime losses approach
▪ Today, more than $3.6 trillion in outstanding credit is reported under FCRA
▪ Estimates are audited and subject to high degree of scrutiny from external oversight
bodies
END OF YEAR 2016 TOTAL ($B)
Outstanding Credit 3,605
NPV of Future Costs 102
BACKGROUND
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Federal agencies who have transitioned to a lifetime credit losses approach
learned the following lessons:
INSIGHTS & LESSONS LEARNED
Financial impact is hard to predict but can be large
There are important stakeholders beyond finance
More data provides more capabilities
Audits are harder and more expensive
Level of model sophistication drives ability to control results
FINANCIAL
STAKEHOLDERS
DATA
AUDITS
MODELS
LESSONS LEARNED BY AGENCIESKEY LESSONS LEARNED
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Financial impact is hard to predict but can be large
FINANCIAL
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How will CECL impact the ALLL, earnings, and capital?
“30-50%
increase”
Source: ABA
“Potential high
volatility in credit
loss allowances
under CECL”
FINANCIAL STAKEHOLDERSDATA AUDITSMODELS
“May actually
lower allowances
in some portfolios”
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FINANCIAL STAKEHOLDERSDATA AUDITSMODELS
Impact is uncertain until you make substantial progress towards implementation
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The impact of the transition can be large
The US Government’s shift to lifetime losses
approach raised their credit loss provision by 50% in
year 1 (provision for credit losses increased from
approximately $20B to $30B on a $770B portfolio).
FINANCIAL STAKEHOLDERSDATA AUDITSMODELS
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IMPACT ON FINANCIAL RESULTS CAN BE LARGE, BUT IS UNCERTAINVolatility is not limited to upfront but can persist…
As views of future economic conditions change:
As models are enhanced and updated:
The $14.4 billion increase in liability can be mostly attributed to the
nationwide decrease in projected house price appreciation, which results
in increased claims and lower proceeds from the sale of foreclosed
properties (FHA 2009)
FINANCIAL STAKEHOLDERSDATA AUDITSMODELS
…Net upward reestimate of $209.4 million…due in large part to updated
model assumptions, which will allow for more accurate projections of future
cash flows
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Adjusted
Strategy
Managed
Expectations
Ran New
Process In
Advance
Started
Implementing
Early
The Best Organizations
FINANCIAL STAKEHOLDERSDATA AUDITSMODELS
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0.00%
1.00%
2.00%
3.00%
4.00%
5.00%
6.00%
1 2 3 4 5 6 7 8 9 10 11
Loan Age
Lifetime vs Incurred Allowance by Loan Age, Loan-Level Breakdown
FINANCIAL STAKEHOLDERSDATA AUDITSMODELS
*Based on Commercial Business Loans Originated from 1992 – 2017
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More data provides more capabilities
DATA
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Federal agencies dealt with serious data challenges
Data is the foundation for “reasonable and supportable”
loan performance estimates
FINANCIAL STAKEHOLDERSDATA AUDITSMODELS
What data do I have?
What is its quality?
How does my data impact my modeling options?
How much data is enough to support my estimates and pass audit?
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FINANCIAL STAKEHOLDERSDATA AUDITSMODELS
Data availability was the first challenge
▪ Organizations scrambled to understand what they had
▪ Federal standards provided a waterfall of options: 1) Use your own historical
data, 2) Use proxy data, 3) Use expert judgement
▪ Data availability challenges are ongoing for new programs and unique
credit products
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Assembling and Preparing Data Took Significant Time and
Resources
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FINANCIAL STAKEHOLDERSDATA AUDITSMODELS
▪ For most organizations, this became a focus after auditors made it one
▪ Typical efforts included profiling, outlier detection, quality business rules
▪ Regime changes were an important consideration
Data quality and suitability followed
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Assembling and Preparing Data Took Significant Time and
Resources
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Shared
Trusted Data
Across the
Organization
Evaluated Data
in Context of
Modeling Goals
Invested Time
to Understand
In-depth
The Best Organizations
FINANCIAL STAKEHOLDERSDATA AUDITSMODELS
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Built Data
Quality into
Modeling
Processes
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Level of model sophistication drives ability to control results
MODELS
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FINANCIAL STAKEHOLDERSDATA AUDITSMODELS
Assumptions-based
Credit Spread Default
Structural Model
Bottom-up Approach
Historical Roll-Rates
Vintage Analysis
Transition Matrix
Discounted Cash Flows
Dynamic Simulation
Many Modeling Options Exist
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INSIGHTS & LESSONS LEARNED MODEL SOPHISTICATION SPECTRUM
LESS COMPLEX MORE COMPLEX
Low Performance Volatility
Small Geographic Footprint
Low Macroeconomic Sensitivity
Small Material Impact on Financials
Wide Range of Performance Outcomes
Presence in Different States/MSAs
Sensitive to Macroeconomic Trends
Large Material Impact on Financials
FINANCIAL STAKEHOLDERSDATA AUDITSMODELS
Fewer resources needed
Less effort to maintain
Generates results quickly
More insight, diagnostics
Allows what-if analysis
Greater long-run benefit
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Monitored
Performance
and Made
Enhancements
Proactively
Used Model
Diagnostics
Calibrated
Model
Complexity to
Portfolio
The Best Organizations
FINANCIAL STAKEHOLDERSDATA AUDITSMODELS
Embedded
Model in Other
Business
Functions
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There are important stakeholders beyond finance
STAKEHOLDERS
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Stakeholders outside of Finance must be engaged to meet CECL’s requirements
FINANCIAL STAKEHOLDERSDATA AUDITSMODELS
INTERNAL
STAKEHOLDERS
EXTERNAL
STAKEHOLDERS
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INSIGHTS & LESSONS LEARNED
Formalized
Their
Engagement
Model
Maintained
Engagement
Throughout
the Transition
Proactively
Identified and
Reached Out
Understood
Early Impacts
Would Be
Broad
The Best Organizations
IDENTIFY & ENGAGE STAKEHOLDERSFINANCIAL STAKEHOLDERSDATA AUDITSMODELS
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Audits are harder and more expensive
AUDITS
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INSIGHTS & LESSONS LEARNED AUDIT COMPLEXITY AND COST
Audits will demand additional resources and close interaction with auditors
Data, models and processes more complex
Comprehensive documentation to justify and validate
Stronger governance and control data, models, and documentation
Longer beginning-to-end audit timeframe
Clean Audit
FINANCIAL STAKEHOLDERSDATA AUDITSMODELS
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INSIGHTS & LESSONS LEARNED
Planned for
an Extended,
Complex and
Costly Audit
Invested in
Building
Strong
Governance
Justified
Methods with
Clear
Documentation
Established
Better Control
Over Data
and Models
The Best Organizations
IDENTIFY & ENGAGE STAKEHOLDERSINSIGHTS & LESSONS LEARNED AUDIT COMPLEXITY AND COSTFINANCIAL STAKEHOLDERSDATA AUDITSMODELS
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RECOMMENDATIONSRECOMMENDATIONS
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▪ Start now
▪ Determine where on the modeling options spectrum is
best for your company
▪ Do your best and be able to show it
▪ Make stakeholder management a priority
▪ Recognize the opportunities that CECL can present
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CECL VS INCURRED
An illustration
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0.00%
1.00%
2.00%
3.00%
4.00%
5.00%
6.00%
2002 2004 2006 2008 2010 2012 2014 2016
Lifetime vs Incurred Allowance, FY 2002-2017
CECL Allowance Incurred Allowance Actuals
*Based on Commercial Business Loans Originated from 1992 – 2017
CECL VS INCURRED ILLUSTRATION
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The reserve for the lifetime allowance is already increasing a few years before the financial crisis. The incurred allowance has a 1-year loss emergence period and does not incorporate future information.
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0.00%
0.50%
1.00%
1.50%
2.00%
2.50%
3.00%
3.50%
4.00%
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16
Loan Age
Lifetime vs Incurred Allowance by Loan Age
CECL Allowance Incurred Allowance Actuals
*Based on Commercial Business Loans Originated from 1992 – 201731
CECL VS INCURRED ILLUSTRATION
Seasonality effects are smoother under the lifetime allowance, because lifetime losses are recognized at loan acquisition. The lifetime and incurred allowances start to converge after the first several years of loan life.
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0.00%
0.50%
1.00%
1.50%
2.00%
2.50%
3.00%
3.50%
4.00%
4.50%
1 2 3 4 5 6 7 8 9 10 11
Loan Age
Lifetime vs Incurred Allowance by Loan Age and Industry Segment
CECL - Construction Incurred - Construction Actuals - Construction
CECL - Professional Services Incurred - Professional Services Actuals - Professional Services
*Based on Commercial Business Loans Originated from 1992 – 2017 32
CECL VS INCURRED ILLUSTRATION
The level and shape of the allowance curve can be significantly different for different portfolio segments. Identifying each segment's contribution to the overall allowance provides more control and insight.
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0.00%
1.00%
2.00%
3.00%
4.00%
5.00%
6.00%
1 2 3 4 5 6 7 8 9 10 11
Loan Age
Lifetime vs Incurred Allowance by Loan Age, Loan-Level Breakdown
*Based on Commercial Business Loans Originated from 1992 – 2017 33
CECL VS INCURRED ILLUSTRATION
A loan level model will calculate an allowance curve for each loan in the portfolio depending on that unique borrower’s profile and product type. A loan level model provides powerful flexibility and control.
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Q&A
RECOMMENDATIONS
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©2017 FI Consulting. All rights reserved.
Roman Iwachiw, CEO | [email protected] Iwachiw is the co-founder and CEO of FI Consulting. He has led financial modeling efforts and developed data and analytics-focused solutions at clients that comprise US government agencies, financial regulators, the GSEs, banks, and non-profit sector lenders.
Mark Jordan, GSE Account Leader | [email protected] Jordan is a portfolio and project management expert with over 15 years of experience working with federal and commercial clients to create best practices for financial management and organizational effectiveness. At FI, he manages teams of modelers and technology experts at our commercial and GSE clients.
Robert Chang, Model Lead |[email protected] Chang has over 12 years experience in financial modeling, risk management, and large scale data analysis working for investment banks and hedge funds. He leads teams that build and validate CCAR, DFAST, and ALLL models for banks and other financial institutions.
ABOUT THE PRESENTERS
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