Using Current Expected Credit Losses (CECL ) to Create a Culture of Analytics at your Credit Union
About Us
Twenty Twenty Analytics provides full service Analytics Solutions
MissionTo combine data solutions with cooperative industry experts to assist our credit union clients in making informed business decisions
Solutions• Interactive Data Visualization Platform• Peer Analytics• CECL Readiness & Implementation• Collateral Valuation (Auto & Real Estate)• FICO Score & FICO Migration• Credit Line Increase Program
About UsDan Price, CPA, CFA – [email protected]
Dan is a CFA charterholder and certified public accountant. He started his career as a financial statement auditor and has experience in audits, compilations and reviews.
Dan is President of 2020 Analytics, specializing in creating customized loan portfolio analytic models for Credit Unions ranging from profitability analytics to Current Expected Credit Loss (CECL) forecasting. When he’s not modeling credit union data, he’s probably looking at fantasy football data.
Agenda
• An Overview of Overviews
• Mythbusting Common Concerns
• Common Implementation Methodologies
• Analytics Parallels
• Example Use Cases
Overview – MethodologiesDetermining
credit lossDiscounted cash flowVintage / Static Pool
Loss rateRoll-rate
Probability-of-defaultCohort
Start Gathering your Data
Ensu
re d
ata
usab
le
in fu
ture
per
iods
Automated (or scheduled) file creation
Data reliable & standardized
Ensured consistency going forward
Captures relevant data
Accessible data
Understanding your Data
• Where is my data?• Core System• LOS• Card Processor• Mortgage Processor
• How can I common-size it?• Unique IDs• Field names• Do common field names mean the same thing?
• Is historical information available?
Mythbusting Common Concerns
Account 12/31/2020 Call Report
1/1/2021 CECL Effective Date
9/30/2021 Call Report 12/31/2021 CallReport
ALLL (Current Method) $150,000 (A) $175,000
Allowance for credit (CECL) $200,000 (B) $235,000(C)
Adjustment to beginning balance of retained earnings
$0 $50,000(B – A)
(NOT C – A)
Net YTD Charge Offs $65,000 $85,000
Provision expense $90,000
Provision for credit losses $120,000
Concern: CECL implementation is going to destroy my provision expenseFact or Myth: Myth
Mythbusting Common Concerns
Consistent Portfolio
Current Balance ALLL - BoY Charge Offs
Provision Expense ALLL - EoY
Retained Earnings
12/31/2020 100,000 1,000 -1,000 1,000* 1,000 10,000
12/31/2021 100,000 1,500 -1,000 1,000 1,500 9,500
12/31/2022 100,000 1,500 -1,000 1,000 1,500 9,500
Growing Portfolio
Increased Risk
*Pre CECL
Mythbusting Common ConcernsConcern: CECL is going to destroy my capital positionFact or Myth: Myth
Concern: I can/should start building up my ALLLFact or Myth: Big Myth
Mythbusting Common Concerns
Concern: Calculating CECL effectively is going to require us to understand our loan and charge off dataFact or Myth: Fact
Why: Call report and GL information does not identify timing of charge off relative to loan origination
Reality: Examiners and auditors will have acceptable methods that don’t use loan data.
Benefits of Using Loan Data
DataAnalytics
Performance / Profitability
Less Volatile Systematic Calculation
FASB
The Time is Now
Going all-in with Data
• Who are my stakeholders?• CEO• CFO (and team)• CLO (and team)• IT team
• What questions need answering?• Underwriting Decisions• Marketing Initiatives• Risk / Compliance
• What data is required?
Lifetime Loss Probability
0.00%
0.50%
1.00%
1.50%
2.00%
2.50%
3.00%
3.50%
Mon
th o
n B
ook 1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41 43 45 47 49 51 53 55 57 59
12-Month Loss Probability
0.00%
0.20%
0.40%
0.60%
0.80%
1.00%
1.20%
1.40%
1.60%
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37
Historical 12-Month Probability
Historical 12-Month Probability
Data Analysis and Interpretation
Use Conclusions to Improve Performance
• Finding 1: A+ direct auto paper performing weaker relative to portfolio• Conclusion 1: A+ paper still profitable net of charge offs, and increasing pricing
will make CU less competitive• Action 1: No action
• Finding 2: B and C FICO paper charging off at comparable rate, but C priced over 2% higher
• Conclusion 2: C paper overpriced, and priced in excess of competition• Action 2: Reduce C paper by 1.25%, aligning the risk premium with the risk in
that portfolio segment
Excel works, too
Other Use Cases
• Fair Lending- Geographic Components Impacting both Fair Lending and CECL
• https://files.consumerfinance.gov/f/201409_cfpb_report_proxy-methodology.pdf
• Credit Score Migration (Change in Credit Quality)• Concentration Limits, and Stress Testing • https://www.ncua.gov/Resources/Documents/LCU2010-03Encl.pdf
Validate your Results
Expectation (Hypothesis)
Results (Conclusion)
Areas for Improvement
Actionable ItemsStreamline your data acquisition process
Make it easy
Know your dataBad data = Bad decisions
Use your data, and take actionStart with what seems most logical
Test for performanceUpdate your models to improve effectiveness
Appendix: Data Fields that may be RelevantLoan Identifier Current Risk Rating
Borrower Name Date of Risk Rating Change
Borrower Location Loan Type
Origination Date Collateral Type
Loan Balance as of Report Date Collateral Location
Unfunded Commitment Original Collateral Value
Renewal / Maturity Date Most Recent Collateral Value
Loan Term Date of Most Recent Valuation
Interest Rate Type of Collateral Valuation
Fixed / Variable Original Debt Service Coverage
Lien Position Original Credit Score
Original Risk Rating Current Credit Score
Appendix: Data Fields that may be RelevantDate of Most Recent Credit Score Co-Borrower / Guarantor Name
Past Due History Co-Borrower / Guarantor Location
Past Due Status at Report Date Co-Borrower / Guarantor Original Credit Score
SIC or NAICS Code Co-Borrower / Guarantor Current Credit Score
Charge-offs Co-Borrower / Guarantor Current Credit Score Date
Date of Charge-off Payment Frequency
Recoveries Payment Amount
Date of Recoveries Loan Branch