CECL - Understanding Data Requirements for Expected Losses

1. Sageworksanalyst.com 1 June 27, 2017 CECL - Understanding Data Sageworks ALLL Garver Moore Principal - Advisory Services

2. Sageworksanalyst.com About the Webinar • We will address as many questions as we can throughout the presentation or through direct communication via follow-up email • Ask questions throughout the session using the GoToWebinar control panel 2

3. Sageworksanalyst.com Disclaimer 3 This presentation may include statements that constitute “forward-looking statements” relative to publicly available industry data. Forward-looking statements often contain words such as “believe,” “expect,” “plans,” “project,” “target,” “anticipate,” “will,” “should,” “see,” “guidance,” “confident” and similar terms. There can be no assurance that any of the future events discussed will occur as anticipated, if at all, or that actual results on the industry will be as expected. Sageworks is not responsible for the accuracy or validity of this publicly available industry data, or the outcome of the use of this data relative to business or investment decisions made by the recipients of this data. Sageworks disclaims all representations and warranties, express or implied. Risks and uncertainties include risks related to the effect of economic conditions and financial market conditions; fluctuation in commodity prices, interest rates and foreign currency exchange rates. No Sageworks employee is authorized to make recommendations or give advice as to any course of action that should be made as an outcome of this data. The forward-looking statements and data speak only as of the date of this presentation and we undertake no obligation to update or revise this information as of a later date.

4. Sageworksanalyst.com Sageworks Solutions BUSINESS OUTCOMES Better customer experience Mitigate risk Grow profitably Increase defensibility

5. Sageworksanalyst.com Today’s Discussion 5 • Segmentation • Life-of-Loan • Data • Measurement • Q&A Agenda

6. Sageworksanalyst.com Poll Question 6

7. Sageworksanalyst.com Segmentation 7 • Segmentation • Life-of-Loan • Data • Measurement • Q&A Agenda Proper Segmentation (326-20-30-2) Segmentations or pools should have similar risk characteristics. These pools should be as granular as possible while maintaining statistical significance. Management will need to evaluate pools on an ongoing basis to ensure that the underlying assets continue to exhibit similar risk behavior.

8. Sageworksanalyst.com Segmentation 8 • Segmentation • Life-of-Loan • Data • Measurement • Q&A Agenda Proper Segmentation (326-20-30-2) Segmentations or pools should have similar risk characteristics. These pools should be as granular as possible while maintaining statistical significance. Management will need to evaluate pools on an ongoing basis to ensure that the underlying assets continue to exhibit similar risk behavior. Granular

9. Sageworksanalyst.com Segmentation 9 • Segmentation • Life-of-Loan • Data • Measurement • Q&A Agenda Proper Segmentation (326-20-30-2) Segmentations or pools should have similar risk characteristics. These pools should be as granular as possible while maintaining statistical significance. Management will need to evaluate pools on an ongoing basis to ensure that the underlying assets continue to exhibit similar risk behavior. Granular Statistical Significance

10. Sageworksanalyst.com Segmentation 10 • Segmentation • Life-of-Loan • Data • Measurement • Q&A Agenda Proper Segmentation (326-20-30-2) Segmentations or pools should have similar risk characteristics. These pools should be as granular as possible while maintaining statistical significance. Management will need to evaluate pools on an ongoing basis to ensure that the underlying assets continue to exhibit similar risk behavior. Granular Statistical Significance Continue to exhibit similar risk behavior

11. Sageworksanalyst.com Life-of-Loan 11 • Segmentation • Life-of-Loan • Data • Measurement • Q&A Agenda Determining the Expected Life of Each Segment (326-20-30-6) Entities are required to estimate expected credit losses over the contractual term of the financial asset(s). Prepayments will need to be considered as a separate input or embedded in the credit loss experience. Expected life is a critical component of all methodologies used to determine loss experience. Prepayment and/or mortality rates will provide for increased flexibility and defensibility.

12. Sageworksanalyst.com Life-of-Loan 12 • Segmentation • Life-of-Loan • Data • Measurement • Q&A Agenda Determining the Expected Life of Each Segment (326-20-30-6) Entities are required to estimate expected credit losses over the contractual term of the financial asset(s). Prepayments will need to be considered as a separate input or embedded in the credit loss experience. Expected life is a critical component of all methodologies used to determine loss experience. Prepayment and/or mortality rates will provide for increased flexibility and defensibility. contractual term

13. Sageworksanalyst.com Life-of-Loan 13 • Segmentation • Life-of-Loan • Data • Measurement • Q&A Agenda Determining the Expected Life of Each Segment (326-20-30-6) Entities are required to estimate expected credit losses over the contractual term of the financial asset(s). Prepayments will need to be considered as a separate input or embedded in the credit loss experience. Expected life is a critical component of all methodologies used to determine loss experience. Prepayment and/or mortality rates will provide for increased flexibility and defensibility. Prepayments contractual term

14. Sageworksanalyst.com Data Requirements 14 • Segmentation • Life-of-Loan • Data • Measurement • Q&A Agenda Now Historical Loss Rates • Charge-offs • Recoveries • Aggregate pool data • Beginning balance of pool • Ending balance of pool Future Expected Loss Rates • Charge-offs • Recoveries • Aggregate pool data • Beginning balance of pool • Ending balance of pool • Risk rating by individual loan • Loan duration • Individual loan balance • Individual loan charge-offs and recoveries (partial and full) • Individual loan segmentation New

15. Sageworksanalyst.com Data Requirements for Expected Losses 15 • Segmentation • Life-of-Loan • Data • Measurement • Q&A Agenda Now Historical Loss Rates • Charge-offs • Recoveries • Aggregate pool data • Beginning balance of pool • Ending balance of pool Future Expected Loss Rates • Charge-offs • Recoveries • Aggregate pool data • Beginning balance of pool • Ending balance of pool • Risk rating by individual loan • Loan duration • Individual loan balance • Individual loan charge-offs and recoveries (partial and full) • Individual loan segmentation New

16. Sageworksanalyst.com Ways to Capture Loan-Level Data 16 • Segmentation • Life-of-Loan • Data • Measurement • Q&A Agenda

17. Sageworksanalyst.com Ways to Capture Loan-Level Data 17 • Segmentation • Life-of-Loan • Data • Measurement • Q&A Agenda Considerations: • Not a viable approach for most core systems due to limited data storage.

18. Sageworksanalyst.com Ways to Capture Loan-Level Data 18 • Segmentation • Life-of-Loan • Data • Measurement • Q&A Agenda Considerations: • Preserves optionality later in the project. Consider consistency and coherency.

19. Sageworksanalyst.com Ways to Capture Loan-Level Data 19 • Segmentation • Life-of-Loan • Data • Measurement • Q&A Agenda Considerations: • Significantly reduced risk and offers most optionality for use.

22. Sageworksanalyst.com Data Adequacy 22 • Segmentation • Life-of-Loan • Data • Measurement • Q&A Agenda The data is labeled appropriately (headers consistently applied and are understandable) Data does not contain duplicates (fields, rows or entities) There are no inconsistencies in values (e.g., truncated by 000’s vs. not truncated) Data is stored in the right format (e.g., numbers stored as numbers, zip codes stored as text) The file extracted from the core system is stored as the right file type File creation is automated; not requiring manual file creation

23. Sageworksanalyst.com Data Adequacy 23 • Segmentation • Life-of-Loan • Data • Measurement • Q&A Agenda Data is reliable and standardized throughout the institution, across all departments Data fields are standardized and governed to ensure consistency going forward Data storage does not have an archiving time limit (e.g., 13 months) Data is accessible (usable format like exportable Excel files, integrates with other solutions) Archiving function captures data points required to perform range of robust methodologies Questions?

24. Sageworksanalyst.com Data Adequacy 24 • Segmentation • Life-of-Loan • Data • Measurement • Q&A Agenda

25. Sageworksanalyst.com Data Deep Dive: Data Adequacy 25 • Segmentation • Life-of-Loan • Data • Measurement • Q&A Agenda 100% 55% 37% 21% 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% 3 Years 4 Years 5 Years 6+ Years Years of Data by 2019 Sageworks Clients as of 11/10/16

26. Sageworksanalyst.com Data Deep Dive: Data Adequacy 26 100% 55% 37% 21% 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% 3 Years 4 Years 5 Years 6+ Years Years of Data by 2019 Sageworks Clients as of 11/10/16 • Of more than 1,000 Sageworks clients, how many have 12+ quarters of loan-level balance and loss information? • At EOY 2019, for clients with an integration, how many clients would have loan-level balance and loss data for:

27. Sageworksanalyst.com Data Deep Dive: Origination Date 27 • Segmentation • Life-of-Loan • Data • Measurement • Q&A Agenda 95%* Average *But as low as 65% at some institutions • Among clients, on average, what percentage of loans have true origination date information stored in Sageworks? • Has it changed during the life of the loan? • Was it changed at renewal? This should never change!

28. Sageworksanalyst.com Data Deep Dive: Renewal Date & Balance 28 • Segmentation • Life-of-Loan • Data • Measurement • Q&A Agenda • Impacts life of loan • Impacts vintage disclosures • What percentage of clients have accurate Renewal Date and Renewal Balance archived? START NOW 2.6% Renewal Date 0.86% Renewal Balance

29. Sageworksanalyst.com Data Deep Dive: Renewal Date & Balance 29 • Segmentation • Life-of-Loan • Data • Measurement • Q&A Agenda Your borrower is past due Bank adjusts for credit risk Bank Reports Delinquency to Agencies Credit agencies report a drop in credit score • Commercial Risk Ratings • Delinquency Data (consumer) • Consider FICO Consider Risk Rating alternatives

30. Sageworksanalyst.com Data Deep Dive: Customer/Contract vs. Book/GL Balance 30 • Segmentation • Life-of-Loan • Data • Measurement • Q&A Agenda Among clients, what percentage provide separate fields for Contract/Customer- Facing Balance and GL/Book Balance? 5% Important to have a time series to determine expected future cash flows against the book balance.

31. Sageworksanalyst.com Data Deep Dive: Codes 31 • Segmentation • Life-of-Loan • Data • Measurement • Q&A Agenda 1% 5% 13% 22% 33% 25% 0% 5% 10% 15% 20% 25% 30% 35% 40% 3 4 5 6 7 8 Number of Loan Codes Successfully Mapped (Out of 8 Possible)

32. Sageworksanalyst.com Data Deep Dive: Codes 32 1% 5% 13% 22% 33% 25% 0% 5% 10% 15% 20% 25% 30% 35% 40% 3 4 5 6 7 8 Number of Loan Codes Successfully Mapped (Out of 8 Possible) • Among clients, on average, how many loan “codes” are being populated? • E.g., Call Code, Collateral Code, Loan Type Code, Product Code, Purpose Code, MSA Code, Industry Code, Postal Code Segmentation is the highest-leverage decision in future guidance.

33. Sageworksanalyst.com Data Deep Dive: Amortization Structure 33 • Segmentation • Life-of-Loan • Data • Measurement • Q&A Agenda Lines of Credit Amortizing Loans • Revolving line? • Paydown / draw speeds? • Probability of funding? • Assumptions for principal wind-down? • When is payment amount calculated? • Balloon Dates and Payments? • Pre-payments? • Payment Amounts – P&I Only? A time-series of balances permits inference of key parameters

34. Sageworksanalyst.com Data Deep Dive: Available Credit 34 • Segmentation • Life-of-Loan • Data • Measurement • Q&A Agenda • Important for your lines • Two paths: • Compute a lifetime loss rate against funded balances and apply a probability of funding (extra lever) • Compute a lifetime loss rate against commitment and apply to commitment “Disclaimer” is severely applicable here, but archive this data.

35. Sageworksanalyst.com Specific Fields Checklist – CECL Takeaway 35 Not all of these items are required historically

36. Sageworksanalyst.com Specific Fields Checklist – CECL Takeaway 36

43. Sageworksanalyst.com Fixing Data Inadequacy 43 • Segmentation • Life-of-Loan • Data • Measurement • Q&A Agenda

44. Sageworksanalyst.com Measurement - Expected Losses 44 • Segmentation • Life-of-Loan • Data • Measurement • Q&A Agenda Unadjusted historical lifetime loss experience Adjustments for past events and current conditions Adjustments for reasonable and supportable forecasts Estimate of expected credit losses • Choice of methods include: • Loss-rate methods • PD/LGD • Migration analysis • Vintage analysis • Any reasonable approach may be used – guidance is not prescriptive Source: “Loss Data, Data Analysis, and the Current Expected Credit Loss (CECL) Model”, Fed Perspectives Webinar, 10/30/15

45. Sageworksanalyst.com Measurement - Implementation 45 • Segmentation • Life-of-Loan • Data • Measurement • Q&A Agenda • For a PBE that is NOT an SEC filer, the credit losses standard is effective for fiscal years beginning after December 15, 2020, including interim periods within those fiscal years. • Standard effective January 1, 2021 • First application reflected in financial statements and regulatory reports for the quarter ended March 31, 2021 Source: “FAQ on New Accounting Standard on Financial Instruments – Credit Losses” OCC 12/19/2016

46. Sageworksanalyst.com How Sageworks Can Help 46 Advisory Services CECL Transition Assistance Data Quality Audit Advanced Analytics Sageworks ALLL Automation to spend 80% less time Supported by risk management experts Dedicated integration project manager

47. Sageworksanalyst.com Sageworks Advisory Services 47 Utilize Sageworks’ Advisory Services Group as a partner and an extension of your team. Our consultants work with institutions to optimize processes to align with strategy, goals, and mission. Our services enable firms to proactively monitor trends and drive efficiencies in the lending cycle. P O R T F O L I O M A N A G E M E N T S E R V I C E S Services Include • Model Transition and Validation Services • CECL Transition Services • Prepayment, Curtailment, Funding, and Cash Flow modeling • Risk Rating Policies and Backtesting • Profitability Analytics O P T I M I Z A T I O N I N S T I T U T I O N D A T A S A G E W O R K S S O L U T I O N S • Valuation Services • Economic Modeling • Process Optimization • Professional Education • DFAST Support • ALM Support

48. Sageworksanalyst.com 48 Expert consultants will structure and lead a project to: • Perform a Readiness Fit-Gap analysis and remediate issues • Create and support execution of a Transition Project Plan • Review segmentation strategy and impact • Execute appropriate measurement scenarios and provide a Model Selection Impact Analysis • Execute preparatory and transitional measurements • Train users on model configuration and execution • Analyze portfolio data to provide strongly supported, bottom-up estimations for important model inputs Initial measurements & model selection Stabilization Parallel A D V I S O R Y S E R V I C E S C E C L T R A N S I T I O N A S S I S TA N C E TRANSITION 2 0 1 7 2 0 1 8 2 0 1 9 Monitor • Create peer/industry benchmarks for model inputs where institutional loss experience cannot be relied on • Create statistical models for economic forecasting Sageworks Advisory Services

50. Sageworksanalyst.com Question & Answer 50 Resources • ALLL.com – join to network, discuss and learn about the ALLL • SageworksAnalyst.com – access whitepapers and the webinar archive • Risk Management Summit 2017 – September 25-27 in Denver, CO • Q&A Garver Moore Principal - Advisory Services Garver.Moore@Sageworks.com Contact Us: Tim McPeak Senior Risk Management Consultant Tim.McPeak@Sageworks.com David Kistler Marketing Manager David.Kistler@Sageworks.com

CECL - Understanding Data Requirements for Expected Losses

CECL - Understanding Data Requirements for Expected Losses

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CECL - Understanding Data Requirements for Expected Losses