data quality considerations for cecl measurement

Garver MooreSageworks Advisory Services

CECL Measurement

P R E S E N T E D B Y

Danny SharmanSageworks Integration Services

About the Webinar

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• Ask questions throughout the session using the GoToWebinar control panel

• We will answer as many questions as we can at the end of the presentation

About Sageworks

• Risk management thought leader for institutions and examiners

• Regularly featured in national and trade media

• Loan portfolio and risk management solutions

• More than 1,000 financial institution clients

• Founded in 1998

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Disclaimer

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.

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About Today’s Presenters

Director, Special Research

Sageworks Advisory Services

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G A RV E R M O O R E

Project Manager

Sageworks Integration Services

D A N N Y S H A R M A N

Agenda

• Information Quality and Information Quantity

» Grading an economic cycle on a curve

• Specific Fields and Specific Actions

• Market Studies – Client Data

• Quantifying Information Risk

Beyond CECL: Transitioning to Data-Driven

World

• Global headwind (tailwind?) affecting all industries

• Each industry is creating its own interpretation of best practices and uses for information

• Large institutions are already taking advantage

• Alt-lending and emerging players are, too – niche players (for now)

• “Big data” isn’t meaningful information

• Data = Information: not just bits on a server

• Intelligence is *actionable* insight

Information Analysis Insights

Concept from Information Security: The

CIA Triangle

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• Confidentiality: Can you protect it from disclosure (beyond the scope of this webinar)

• Integrity: Can you rely on it (Data Quality)

• Availability: Do you have access to it (Data Quantity)

Concept from Information Security: The

CIA Triangle

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• Confidentiality: Can you protect it from disclosure (beyond the scope of this webinar)

• Integrity: Can you rely on it (Data Quality)

• Availability: Do you have access to it (Data Quantity)

The consequences of poor data planning now are indistinguishable from the consequences of a data

breach in 3 years.

Information Quantity: How Much To

Perform a CECL Measurement

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• START NOW

» START YESTERDAY

• START THREE YEARS AGO

• The Three, Five, Ten, Fifteen Years Myth:

» “Reasonably available”

» More is better

» Compositional assumptions

» Order your own house

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Example: Non-SEC Filing PBE

2020? It’s 2016!

Dec 2012FASB Proposes CECL Model

July 2014IASB’s IFRS 9 Financial Instruments

Feb 2015Basel ECL guidance released

June 2016Release of FASB’s CECL model

Scenarios & modelingFinal model & validation

Dec 2020Implementation

Dec 2018Early Adoption

Refine & monitor

Early 2009IFRS 9 / Convergence Introduction

2010-2012Convergence/Three-Bucket Wrangling

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Example: Non-SEC Filing PBE

2020? It’s 2016!

Scenarios & modelingFinal model & validation

Refine & monitor

May 1961“Time for a great new American enterprise”

Crewed Apollo Flights

The adjustments to historical loss information may be

qualitative in nature and should reflect changes related

to relevant data (such as changes in unemployment

rates, property values, commodity values, delinquency,

or other factors that are associated with credit losses on

the financial asset or in the group of financial assets).

Application: Forecasting

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Forecasting

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should reflect changes to related

relevant data







Forecasting

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relevant data unemployment







Forecasting

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relevant data unemployment

other factors associated with losses

An entity shall not rely solely on past events to estimate

expected credit losses. When an entity uses historical

loss information, it shall consider the need to adjust

historical information to reflect the extent to which

management expects current conditions and reasonable

and supportable forecasts to differ from the conditions

that existed for the period over which historical

information was evaluated.

Forecasting

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Forecasting

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adjust historical information









Forecasting

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adjust historical information

differ from the conditions that

existed

Forecasting - Application

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Commercial RE Loan Count Loan Balance Loss Rate Estimated Losses

Total 1,150 499,500,000 1.35% 6,752,500

Pass 975 485,000,000 1.20% 5,820,000

Special Mention 25 8,500,000 2.50% 212,500

Substandard 150 6,000,000 12.00% 720,000


Total 1,150 499,500,000 0.82% 4,115,950

Pass 975 485,000,000 0.70% 3,395,000

Special Mention 25 8,500,000 1.07% 90,950

Substandard 150 6,000,000 10.50% 630,000


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Include Static Date Balance Charge-offs Recoveries Loss Rate

Yes 12/31/2010 270,000,000 3,000,000 150,000 1.06%

Yes 3/31/2011 275,000,000 2,750,000 145,000 0.95%

Yes 6/30/2011 300,000,000 3,500,000 160,000 1.11%

Yes 9/30/2011 309,000,000 2,700,000 145,000 0.83%

Yes 12/31/2011 320,000,000 2,300,000 130,000 0.68%

Yes 3/31/2012 324,000,000 1,850,000 130,000 0.53%

Yes 6/30/2012 343,000,000 1,850,000 130,000 0.50%

Yes 9/30/2012 365,000,000 1,700,000 130,000 0.43%

Yes 12/31/2012 400,000,000 1,400,000 55,000 0.34%


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Yes 12/31/2010 270,000,000 3,000,000 150,000 1.06%

Yes 3/31/2011 275,000,000 2,750,000 145,000 0.95%

Yes 6/30/2011 300,000,000 3,500,000 160,000 1.11%

Yes 9/30/2011 309,000,000 2,700,000 145,000 0.83%

Yes 12/31/2011 320,000,000 2,300,000 130,000 0.68%

Yes 3/31/2012 324,000,000 1,850,000 130,000 0.53%

Yes 6/30/2012 343,000,000 1,850,000 130,000 0.50%

Yes 9/30/2012 365,000,000 1,700,000 130,000 0.43%

Yes 12/31/2012 400,000,000 1,400,000 55,000 0.34%


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Yes 12/31/2010 270,000,000 3,000,000 150,000 1.06%

Yes 3/31/2011 275,000,000 2,750,000 145,000 0.95%

Yes 6/30/2011 300,000,000 3,500,000 160,000 1.11%

Yes 9/30/2011 309,000,000 2,700,000 145,000 0.83%

Yes 12/31/2011 320,000,000 2,300,000 130,000 0.68%

Yes 3/31/2012 324,000,000 1,850,000 130,000 0.53%

Yes 6/30/2012 343,000,000 1,850,000 130,000 0.50%

Yes 9/30/2012 365,000,000 1,700,000 130,000 0.43%

Yes 12/31/2012 400,000,000 1,400,000 55,000 0.34%


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No 12/31/2010 270,000,000 3,000,000 150,000 1.06%

No 3/31/2011 275,000,000 2,750,000 145,000 0.95%

No 6/30/2011 300,000,000 3,500,000 160,000 1.11%

Yes 9/30/2011 309,000,000 2,700,000 145,000 0.83%

Yes 12/31/2011 320,000,000 2,300,000 130,000 0.68%

Yes 3/31/2012 324,000,000 1,850,000 130,000 0.53%

Yes 6/30/2012 343,000,000 1,850,000 130,000 0.50%

Yes 9/30/2012 365,000,000 1,700,000 130,000 0.43%

Yes 12/31/2012 400,000,000 1,400,000 55,000 0.34%

Unemployment > 8% (exceeds

current forecast)


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Total 1,150 499,500,000 1.35% 6,752,500

Example calculation – No prepayments – No forecasting


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Total 1,150 499,500,000 1.35% 6,752,500


Total 1,150 499,500,000 0.82% 4,115,950

Example calculation – No forecasting


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Total 1,150 499,500,000 1.35% 6,752,500


Total 1,150 499,500,000 0.82% 4,115,950


Total 1,150 499,500,000 0.55% 2,747,250

Example calculation – Prepayments - Forecasting


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Total 1,150 499,500,000 1.35% 6,752,500


Total 1,150 499,500,000 0.82% 4,115,950


Total 1,150 499,500,000 0.55% 2,747,250

Example calculation – Prepayments - Forecasting

Information: Quality

Information: Quality – Data

Source: Fivethirtyeight.com

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Information: Quality – Narrative

“First, Clinton’s overall lead over Trump — while her gains over the past day or two have helped — is still within the range where a fairly ordinary polling error could eliminate it.”

“Second, the number of undecided and third-party voters is much higher than in recent elections, which contributes to uncertainty.”

“Third, Clinton’s coalition — which relies increasingly on college-educated whites and Hispanics — is somewhat inefficiently configured for the Electoral College, because these voters are less likely to live in swing states. If the popular vote turns out to be a few percentage points closer than polls project it, Clinton will be an Electoral College underdog.”

Source: Fivethirtyeight.com

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https://twitter.com/DrewLinzer/status/794989102084931584

http://fivethirtyeight.com/features/how-trump-could-win-the-white-house-while-losing-the-popular-vote/

Poll

Data Quality

Data and Narrative: Back to Banking

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Data Deep-Dive: Data Adequacy

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• 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:

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: Origination Date

• 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?

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Average

*But as low as 65% at some institutions

This should never change!

Data Deep-Dive: Renewal Date and

Renewal Balance

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• Impacts life of loan

• Impacts vintage disclosures

• What percentage of clients has accurate Renewal Date and Renewal Balance archived?

Renewal Date

Renewal Balance

START NOW

Data Deep-Dive: Credit Quality Data

• Commercial Risk Ratings

• Delinquency Data (consumer)

• Consider FICO

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Your borrower is past due

Bank adjusts for credit risk

Bank Reports Delinquency to Agencies

Credit agencies report a drop in credit score

Consider Risk Rating alternatives

Poll

FICO data

Data Deep-Dive: Customer/Contract vs.

Book/GL Balance

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Important to have a time series to determine expected future cash flows

against the book balance.

Among clients, what percentage provide separate fields for Contract/Customer-Facing Balance and GL/Book Balance?

Data Deep Dive: Codes

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Segmentation is the highest-leverage decision in future guidance.

• 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

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: Amortization

Structure

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• Revolving line?

• Paydown / draw speeds?

• Probability of funding?

• Assumptions for principal wind-down?

• When is payment amount calculated?

LINES OF CREDIT

• Balloon Dates and Payments?

• Pre-payments?

• Payment Amounts – P&I Only?

AMORTIZING LOANS

A time-series of balances permits inference of key parameters

Data Deep-Dive: Available Credit

• 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

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“Disclaimer” is severely applicable here, but archive this data.

Poll

Demographic Data

Data Audit Considerations

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Loan Number MonthsRemaining

Origination Date

Loan Balance

12365438 72 1/31/2015 300,000.00

12363612 62 9/8/206 150,000.00

12363242 3 9/1/2016 1,800.00

12368555 15 8/1/2014 7,200.00

12365438 2 10/13/2012

12367893 96 10/5/2016

12366543 2 4/8/2014

12361322 3 5/5/2015

12361111 18 9/3/2016

12360237 11 10/15/2016

Loan Number MonthsRemaining

Origination Date

Loan Balance

12365438 20 8/31/2015 20,000.00

12363612 62 9/8/206 150,000.00

12363242 3 9/1/2016 1,800.00

12368555 15 8/1/2014 7,200.00

12365438 2 10/13/2012 80,000.00

12367893 96 10/5/2016 1,500.00

12366543 2 4/8/2014 5,000.00

12361322 3 5/5/2015 1,200.00

12361111 18 9/3/2016 12,000.00

12360237 11 10/15/2016 15,000.00

? ?

Audit Coverage Considerations

• Check every loan type and structure

• Look at the current state and the past

• Ongoing or origination?

• Geographic Data – What does it refer to? How reliable?

• Reliability Decision > Accuracy

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Is it right, now? (Data Audit) Will it be right, later? (Data Assurance)

Project Process

Defect Prevention – Demographic Data

• Process steps at origination

• QA at renewal

• Loan Ops/Loan Admin are the vanguard

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Is it right, now? (Data Audit) Will it be right, later? (Data Assurance)

Project Process

Data Assurance Business Case

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Reality (Messy)

Your Data Model(G/L, Core, Documents)

You’re already spending effort mapping the reality to your model


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Option 1 (Too common) – replication and inconsistency of effort

Accounting

Loan Committee

Audit Regulatory

Credit


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1) Model Once


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2) Measure Once


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3) Deploy (Data Warehouse)

Accounting

Loan Committee

Audit Regulatory

Credit

Thought Experiment: Rocket Bank

• Rocket Bank has a single analysis and business segment: C&I loans with NAICS code X212221

» Mining asteroids for gold

» Experimental technologies: about 10% of rockets will fail to orbit (default)

» Secured by existing gold stocks and exotic insurance products – about 15% of a loan is exposed to default risk

» Extraordinarily well capitalized; inexhaustible supply of loans

• You enter this space: think of your portfolio loans as a “sample”

» What if you win 10 loans of their business? 50? 500?

• Knowing a priori the “platonic” default rate and LGD for these kinds of loans, let us examine how the portfolio size changes your certainty in calculating your own experience in X212221 lending

Concept: Confidence Intervals

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Documenting reliability in measurement

-4.00%

-2.00%

0.00%

2.00%

4.00%

6.00%

8.00%

10.00%

12.00%

1 10 100 1000 10000

The reliability of a measurement (e.g., Loss Rates or PD) scales with how that measurement is conducted.

Specific Fields Checklist – CECL

Takeaway (Strong Recommend)

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Element Comments Element Comments

Loan System Data Source (which core, etc.) Renewal and Origination Date/Amount

Required for accurate vintage disclosures and Life of Loan calculation. Origination date should never change over the time-series.

Loan ID Unchanging identifier for loan if numbers can change Payment Structure Software can solve for this if not available (balloon, amortization-through, fixed principal, etc.) if there is a sufficient, accurate time series.

Customer Balance Contract balance Revolving Status Is it a revolving line?

Book Balance G/L Balance Interest Basis e.g. 360/360, actual, etc.

Coupon Rate Contract interest rate Accrued Interest Receivable

Required for total recorded investment

Maturity Date Check for historical accuracy; can this be inferred to be the balloon date?

Credit Quality Risk rating, delinquency status (# of pmts, days past due, times-past-due), nonaccrual status, credit scores (prop or industry), TDR status

Segment Identifiers

Call, Product, Geography, etc. As many as are mutually exclusive/collectively exhaustive and reliable

Available Credit For computation of funding probability

Not all of these items are required historically

Specific Fields Checklist – CECL

Takeaway (Recommend)

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Element Comments Element Comments

Loan Officer For reporting, visibility, measurement Next/Last Repricing Date

Useful for portfolio analytics and planning

Payment Amount Likely useful for some credits, unusable for others depending on core limitations.

Tenor/Payments Remaining

Convenient vs. Constantly doing math on Maturity Date

Branch (LoanLevel)

Useful for reporting and analytics Addresses Collateral? Borrower? Should ideally be structured (e.g. Addr1, City, State, Zip)

Payment Frequency

Quarterly, Annually, Maturity, Monthly, etc. Amount Past Due Reporting and increases accuracy of DCF analysis

Floor/CeilingRate

Useful for analytics and portfolio risk management activities

Gov Guaranty Information

e.g. percent, balance. Can be “worked around” (and seems to mostly be so).

Spread Useful for analytics/forecasting Participation Information

e.g. percent, balance. Can be “worked around”.

Floating Rate Peg E.g. Prime, LIBOR Audit / Audit Process Date

Consider a flex / user-defined field to track and report on your quality assurance efforts.

How Sageworks Can Help

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ADVISORY SERVICES

SAGEWORKS ALLL

CECL Transition Assistance

Data Quality AuditAdvanced Analytics

Automation to spend 80% less

time

Supported by risk management

experts

Dedicated integration project

manager

Sageworks ALLL – New Features in

Pipeline

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• Enhanced period selection/exclusion for vintage and other models

• Enhanced intelligence and graphical capabilities (trending, etc.)

• Select economic / qualitative indicators from public & private data and time-link them to archives

Poll

About Sageworks

For Clients – New Fields in Pipeline

• Loan System – for clients with multiple cores and to simplify / clarify acquisition reporting

• Alternate Loan Number – A workaround for customers with migrating loan identifiers

• Loan-Level FICO – for consumer-facing customers who have credit score migration data at a loan-level and wish to track and report it

• Branch – For clients who wish to track Branch at the loan (rather than borrower) level

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• Floor/Ceiling rates and Spread

• Floating rate peg (e.g., Prime / LIBOR / etc.)

• Next / Last Repricing Date

• Loan-level address fields

• # of Extensions

• Explicit revolving status

• Explicit Amount Past Due

• Prepayment Penalty fields

• Explicit G/L Segments

Q&A

• Follow up email

• ALLL.com

• SageworksAnalyst.com – latest whitepapers and archived webinars

• SageworksAnalyst.com – product and advisory services information

• Risk Management Summit 2017 –September 24-27 in Denver, CO

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RESOURCES

Garver MooreSageworks Advisory Services

[email protected]

Danny SharmanSageworks Integration Services

[email protected]

PRESENTERS

data quality considerations for cecl measurement

Economy & Finance