P R E S E N T E D B Y

Sr. Risk Management Consultant

Sageworks

Managing Partner

DHG Financial Services

• Ask questions throughout the

session using the GoToWebinarcontrol panel

• The slides and the recording will be sent out to attendees after today’s

session

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• Financial information company that provides credit and risk management solutions to

financial institutions

• Data and applications used by thousands of financial institutions and accounting firms

across North America

• Awards

» Named to Inc. 500 list of fastest growing privately held companies in the U.S.

» Named to Deloitte’s Technology Fast 500

» NC Tech Awards: Excellence in Customer Service

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ABOUT DHG F INANCIAL SERVICES

DHG Financial Services, a national practice of Dixon Hughes Goodman, focuses on publicly traded and privately-held financial services companies across the U.S. Their 30 financial

services partners and more than 300 dedicated professionals provide clients with in-depth

industry knowledge and a wide range of assurance, tax and advisory services to address issues facing the financial services industry in today's challenging environment.

ABOUT DIXON HUGHES GOODMAN

With more than 1,800 people in 12 states, Dixon Hughes Goodman ranks among the nation’s

top 20 public accounting firms. Offering comprehensive assurance, tax and advisory services,

DHG focuses on major industry lines and serves clients in all 50 states as well as internationally. Visit www.dhgllp.com for additional information.

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Sr. Risk Management ConsultantSageworks

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EMILY BOGAN

Managing PartnerDHG Financial Services

WALTER MCNAIRY

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• Background on CECL & current status

• Distinction between current and expected new standards

• What changes mean for institutions

• 3 Preparation DO’s & 3 Preparation DON’Ts

• Questions

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• FASB released latest proposal December 2012

• Comment period ended May 31, 2013

• IASB issued IFRS 9 Financial Instruments in 2014, to be implemented in 2018 (includes

forward-looking ‘expected loss’ measure)

• Basel releases “Guidance on accounting for expected credit losses” in February 2015

• FASB expected to issue final CECL standard this year in Q4

• Expected to take effect in 2018

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• Understand the new CECL requirements

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Existing Accounting Standards CECL Model

Separate accounting standards for• Groups of loans collectively evaluated for impairment (ASC 450-20) • Loans identified for individual impairment evaluation (ASC 310-10-35) • Loans acquired with deteriorated credit quality (ASC 310-30) (Purchased Credit-Impaired “PCI” loans) • Debt securities (ASC 320-10-35 and 325-40-15)

One accounting standard for all loans and debt securities that are not classified as available-for-sale (other than those measure at fair value through net income)

No separate accounting standard for impaired loan or PCI loan accounting

Incurred loss model, typically using a 1-3 year look-back

Expected loss model, looking at all losses over the life of loans

Tends to produce pro-cyclical earnings based on current point in credit cycle

Expected to be less pro-cyclical than existing model

Focused on historical information and current information as of the measurement date

Considers past events, current conditions and reasonable and supportable assumptions about the future

Source: “Preparing for CECL” from DHG, August 2015

• How does it impact financial institutions?

» Forward-looking requirements

» “Probable” threshold removed

» Longer loss horizon

» Time value of money plays a role

» Collateral definitions

» Need for accessible, loan-level data

» Potential to increase ALLL levels by 10-50%

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• Incorporate expected loss concepts early

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• Compile as much loss data as you can, in as much detail as you can, as far back as you can

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DO2

• Look at current system: Can I extract data points from my system? Is this data dynamically available?

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• Limited Method

» Use core to capture detailed data (most cores go back ~13 months max)

• Static Method

» Begin storing core archives in data warehouse

• Dynamic Method

» Use automated solution to capture and store data

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WAYS TO CAPTURE LOAN -LEVEL DETAIL

• More defensible, documented calculations

• Enhanced reconciliation capabilities

• Reduce future subjectivity

• Perform more robust data analysis

» Stress Testing / Scenario Building

» Migration analysis

» PD / LGD

» Backtesting

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LOAN-LEVEL DETAIL BENEFITS

• Scenario building: assess the outcome of the ALLL under various assumptions

» Run multiple scenarios, compare results

» Create projections

• Estimate the impact that ALLL-calculation variables may have without performing a

completely new calculation

• Enables the institution to be more proactive in managing the calculation and measured

in making changes to the methodology

• Similar to stress testing

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SCENARIO BUILDING

• Displays impact of individual factors on overall analysis

• What if just “X” changed?

• What would it do to my calculation…

» Ex: If I adopted migration analysis, would it change my reserve?

» Ex: If CRE took a hit, how much would I have to provision?

» Ex: If I changed my look-back period, what would it do to my reserve levels?

» Ex: If I began lending in a new segment, what losses would peer groups reflect?

• Helps build a range for the reserve

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SCENARIO BUILDING

• Migration analysis often considered a more “robust” form of analysis

» Should result in more accurate allowance than historical loss

» More insight into sub-segmented pools (credit quality/deterioration)

» Less prone to examiner criticism if performed correctly

» More effectively justify decrease in provisions, if merited

• Adjusts ALLL provision to reflect the conditions of the current portfolio

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MIGRATION ANALYSIS

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MIGRATION ANALYSIS EXAMPLE:

• Uses pre-determined measures of default and loss to calculate expected loss

• Probability of Default (PD)

» % likelihood a customer will NOT make full and timely repayment of their credit

obligations over a given time horizon

» Assigned to each risk rating or segment

• Loss Given Default (LGD)

» Loss amount if there is a default (%)

» Variable (%) assigned to each loan that reflects losses within the loan’s industry or product

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PD/LGD METHOD

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• Artificially inflate your ALLL now to smooth the impact expected with CECL adoption

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DO3

• Determine correlation of losses to general economic data, specific risks, etc.

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Example: Correlate change in local unemployment statistics to losses in consumer loans

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3

• Overload at adoption

• Wait to prepare

1. Understand the new CECL requirements

2. Compile as much loss data as you can, in as much detail as you can, as far back as you can

3. Determine correlation of losses to general economic data, specific risks, etc.

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1. Incorporate expected loss concepts early

2. Artificially inflate your ALLL to smooth the impact upon adoption

3. Overload at adoption & wait to prepare

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When the FASB releases final guidance on the CECL model, Sageworks will conduct a webinar featuring industry consultants to cover:

• What does the guidance mean for the ALLL calculation?

• How will it impact institutions?

• What must bankers do to comply?

Pre-register at: http://web.sageworks.com/CECL/

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• September 23-25 in Chicago

• ALLL & Stress Testing, Peer Roundtables

• Banker Appreciation Night on Lake Michigan

• sageworks.com/summit

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Todd Sprang

Principal

CliftonLarsonAllen

Ariste Reno

Partner

KPMG

Walter McNairy

Managing Partner

DHG

Graham Dyer

Senior Manager

Grant Thornton

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LINKEDIN GROUPS:• ALLL Forum for Bankers

• Commercial Credit Risk Professionals

WEBSITES

• www.sageworksanalyst.com

• www.ALLL.com

• Whitepapers, webinars, thought

leadership

• www.DHGLLP.com

EMILY BOGAN

Sr. Risk Management ConsultantSageworksemily.bogan@sageworks.com866.603.7029

WALTER MCNAIRY

Managing PartnerDHG Financial Serviceswalter.mcnairy@dhgllp.com919.875.4993

• CECL Prep Whitepaper by DHG:

http://www.dhgllp.com/Portals/4/ResourceMedia/publications/DHG_Views_FinancialServices_PreparingforCECL.pdf

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