cecl - understanding data requirements for expected losses

Sageworksanalyst.com 1

June 27, 2017

CECL - Understanding DataSageworks ALLL

Garver MoorePrincipal - Advisory Services

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

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Disclaimer

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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.


Sageworks Solutions

BUSINESS OUTCOMES

Better customer experience

Mitigate risk

Grow profitably

Increase defensibility


Today’s Discussion

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• Segmentation

• Life-of-Loan

• Data

• Measurement

• Q&A

Agenda


Poll Question

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Segmentation

7

• Segmentation

• Life-of-Loan

• Data

• Measurement

• Q&A

AgendaProper 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.


Segmentation

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• Segmentation

• Life-of-Loan

• Data

• Measurement

• Q&A






Granular


Segmentation

9

• Segmentation

• Life-of-Loan

• Data

• Measurement

• Q&A






Granular

Statistical Significance


Segmentation

10

• Segmentation

• Life-of-Loan

• Data

• Measurement

• Q&A






Granular

Statistical Significance

Continue to exhibit similar risk behavior


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.


Life-of-Loan

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• Segmentation

• Life-of-Loan

• Data

• Measurement

• Q&A









defensibility.

contractual term


Life-of-Loan

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• Segmentation

• Life-of-Loan

• Data

• Measurement

• Q&A









defensibility.

Prepayments

contractual term


Data Requirements

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• Segmentation

• Life-of-Loan

• Data

• Measurement

• Q&A

AgendaNowHistorical Loss Rates

• Charge-offs

• Recoveries

• Aggregate pool data

• Beginning balance of pool

• Ending balance of pool

FutureExpected Loss Rates

• Charge-offs

• Recoveries




• Risk rating by individual loan

• Loan duration• Individual loan

balance• Individual loan

charge-offs and recoveries (partial and full)

• Individual loan segmentation

New


Data Requirements for Expected Losses

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• Segmentation

• Life-of-Loan

• Data

• Measurement

• Q&A

AgendaNowHistorical Loss Rates

• Charge-offs

• Recoveries




FutureExpected Loss Rates

• Charge-offs

• Recoveries




• Risk rating by individual loan

• Loan duration• Individual loan

balance• Individual loan

charge-offs and recoveries (partial and full)

• Individual loan segmentation

New


Ways to Capture Loan-Level Data

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• Segmentation

• Life-of-Loan

• Data

• Measurement

• Q&A

Agenda



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• Segmentation

• Life-of-Loan

• Data

• Measurement

• Q&A

Agenda

Considerations:• Not a viable approach for

most core systems due to limited data storage.



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• Segmentation

• Life-of-Loan

• Data

• Measurement

• Q&A

Agenda

Considerations:• Preserves optionality later in

the project. Consider consistency and coherency.



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• Segmentation

• Life-of-Loan

• Data

• Measurement

• Q&A

Agenda

Considerations:• Significantly reduced risk and

offers most optionality for use.


Poll Question

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Poll Question

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Data Adequacy

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• Segmentation

• Life-of-Loan

• Data

• Measurement

• Q&A

AgendaThe 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


Data Adequacy

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• Segmentation

• Life-of-Loan

• Data

• Measurement

• Q&A

AgendaData 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?


Data Adequacy

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• Segmentation

• Life-of-Loan

• Data

• Measurement

• Q&A

Agenda


Data Deep Dive: Data Adequacy

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• 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


Data Deep Dive: Data Adequacy

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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:


Data Deep Dive: Origination Date

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• 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!


Data Deep Dive: Renewal Date & Balance

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• 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


Data Deep Dive: Renewal Date & Balance

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• 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


Data Deep Dive: Customer/Contract vs. Book/GL Balance

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• 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.


Data Deep Dive: Codes

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• 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)


Data Deep Dive: Codes

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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.


Data Deep Dive: Amortization Structure

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• 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


Data Deep Dive: Available Credit

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• 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.


Specific Fields Checklist – CECL Takeaway

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Not all of these items are required historically



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Fixing Data Inadequacy

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• Segmentation

• Life-of-Loan

• Data

• Measurement

• Q&A

Agenda


Measurement - Expected Losses

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• Segmentation

• Life-of-Loan

• Data

• Measurement

• Q&A

AgendaUnadjusted 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


Measurement - Implementation

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• 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

https://www.occ.treas.gov/topics/bank-operations/accounting/cecl/cecl-faqs.html#q4


How Sageworks Can Help

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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


Sageworks Advisory Services

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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 IO 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

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

TR

AN

SI

TI

ON2017 2018 2019 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


Poll Question

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Question & Answer

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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 MoorePrincipal - Advisory Services

[email protected]

Contact Us:

Tim McPeakSenior Risk Management Consultant

[email protected]

David KistlerMarketing Manager

[email protected]

cecl - understanding data requirements for expected losses

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