Using Current Expected Credit Losses (CECL ) to Create a ...€¦ · He started his career as a...

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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 – PresidentDprice@ttadata.com

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

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

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

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

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

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

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

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

Thank You!

www.ttadata.com

Dan Pricedprice@ttadata.com

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