This document discusses key data elements that financial institutions need to collect and store to properly calculate allowances for loan and lease losses (ALLL) and comply with regulatory requirements. It covers loan-level data, collateral data, customer data, risk ratings, and historical loss rates. The document also discusses challenges related to data quality and availability, and preparations needed for the new Current Expected Credit Loss (CECL) model. Overall, it emphasizes the importance of centralized, accessible loan-level data for accurate ALLL calculations and regulatory reporting.

1. Garrett Morris
Director – Client Services Group
Sageworks
Date of Last Revision: 9/18/2015
Date of Last Review: 9/18/2015

2. • Important data collection elements
• Information technology and security
• Data storage
• Loan portfolio information
• Data for ASC 310-10-35 calculations
• Data for ASC 450-20 calculations
• Stress testing data
• Preparing for FASB’s CECL model
• Utilizing data, knowing your clients, knowing your loan portfolio
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3. • The 2006 Interagency Policy Statement articulates:
“The ALLL represents one of the most significant estimates in an institution’s
financial statements and regulatory reports… each institution has a responsibility
for developing, maintaining, and documenting a comprehensive, systematic, and
consistently applied process for determining the amounts of the ALLL and the
provision for loan and lease losses.”
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4. • Data access
• Data availability – redundancy and backups
• Disaster recovery and business continuity strategy
• Data security – Internal
• Strong security awareness program
• Data security outside your organization
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• 5 Stages
» Planning
» Due diligence and third-party selection
» Contract negotiation
» Ongoing monitoring
» Termination
• Throughout the Life Cycle
» Oversight and accountability
» Documentation and reporting
» Independent reviews

6. • Central repository for all data
» Centralized system for all data, which can be fed to other systems as needed
» Should be accessible, secure and reportable/extractable
» Avoid storing data on personal computers, in Excel, etc.
» Month-end snapshot balances (archives)
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Central Data
Repository

7. • Types of data for the ALLL
» Detailed loan data
• ASC 450-20 / FAS 5 segment
• Risk rating, GL balance, etc. ~30-40 fields
» Basic collateral data (collateral type, appraisal date, appraisal value)
» Basic customer data (name, total checking/savings balance, industry, etc.)
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8. • Antiquated or otherwise limited bank core
» Pursue core systems with flexible data and reporting features
• Internal bank issues
» Disjointed or conflicting operations, policies and processes
» Lack of uniformity with information
• Collateral data gathering and storage
• ASC 310-10-35 (formerly known as FAS 114)
» Proper training of staff
» Data integrity
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10. • Most important data element
• Varies by institution based on calculation and reporting requirements
• Archive monthly or quarterly
• Ensure consistent format of the data
• More data is better
• Data checks
• Field data examples
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Fiserv: Premier
Sageworks Field Name Data Type Bank/Core Field Name
Core Intelligence
Score Rank
Accrued Interest Loans interest 63 1
Accrued Interest Loans accruedinterest 22 2
Accrued Interest Loans earnedinterestbalance 5 3
Amortization Days Loans currentinterestmethod 41 1
Amortization Days Loans amortizationdays 30 2
Amortization Days Loans originalinterestmethod 7 3
Current Balance Loans netactiveprincipalbalance 34 1
Current Balance Loans currentbalance 13 2
Current Balance Loans activeprincipalbalance 11 3
Customer Number Loans taxidnumber 49 1
Customer Number Loans formattedtaxid 11 2
Customer Number Loans uniqueidentifierofborrower 11 3
Loan Officer Loans responsibilitycode 53 1
Loan Officer Loans loanofficer 15 2
Loan Officer Loans responsibilitycodedesc 14 3
Call Code Loans purposecode 49 1
Call Code Loans callcode 16 2
Call Code Loans classcode 5 3
Category Loans classcode 46 1
Category Loans nonaccrualinterest 46 2
Category Loans category 1 3

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Jack Henry: 20/20
Sageworks Field Name Data Type Bank/Core Field Name
Core Intelligence
Score Rank
Accrued Interest Loans accint 41 1
Accrued Interest Loans accruedinterestlnmast 28 2
Accrued Interest Loans accruedinterest 18 3
Amortization Days Loans ibase 27 1
Amortization Days Loans interestbaselnmast 27 2
Amortization Days Loans amortizationdays 8 3
Current Balance Loans qrybal 23 1
Current Balance Loans cbal 17 2
Current Balance Loans querybalancelnmast 12 3
Customer Number Loans cifno 47 1
Customer Number Loans ciflnmast 14 2
Customer Number Loans cifcfmast 6 3
Loan Officer Loans officr 50 1
Loan Officer Loans officerlnmast 16 2
Loan Officer Loans officer 14 3
Call Code Loans calrep 50 1
Call Code Loans callreportcodelnmast 21 2
Call Code Loans callreportcode 10 3
Category Loans census 91 1
Category Loans category 1 2
Category Loans classcode 1 3

16. • Data related to TRI
• Appraisal values
• Yearly updated appraisals
• Discount rates
• Liens at other institutions
• Lien positions
• Selling costs
• Taxes
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18. • TRI
• Expected payments
• Effective interest rate
• Remaining term
• Amortization days
• Balloon payments / seasonality
• Evidence of how we arrived upon expected payment – credit analysis
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20. • Impact of historical loss rates on reserve amount
• Methods to calculate historical loss rates for their FAS 5 pools:
» Traditional historical loss rate calculation
» Migration analysis
» Peer group
» Call report
» Others
• PD / LGD
• Vintage analysis
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21. • Segmentation of loan
• Balance of loan
• If sub-segmenting:
» Days past due
» Risk rating
» Risk level
» FICO score
• Charge offs and recoveries attached to loans
• Historical time horizon
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24. • Traditionally subjective
• Should be as objective and directionally consistent as possible
» Need to store drivers and adjustments
» Data driven
» Well-documented qualitative factors
• Each Q factor has drivers that are the recommended variables to measure over
time
» Internal drivers
» External drivers
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25. • Federal Reserve Economic Data (FRED) provides free, customizable
macro-level data:
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26. • Changing segmentation
• Changing historical loss calculations
• Changing risk ratings & risk levels
» Changing scorecard
• Other LOB’s affected by data changes – finance, loan operations, loan
administration, credit, etc.
• Updating processes leads to future accuracy
• Defining loss emergence period
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28. • Formulating concentrations
• Financial data – EBITDA, NOI, personal income, vacancy rates
• Collateral data – appraisals, cap rates
• Debt service – annual debt payments, interest rates
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29. • 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 released “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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30. • How does CECL 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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Potential data required each quarter:

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Understand needed data components
Increase data integrity
Improve data collection
CECL 1
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33. • More defensible, documented calculations
• Enhanced reconciliation capabilities
• Perform more robust data analysis
» Stress Testing / Scenario Building
» Migration analysis
» PD / LGD
» Backtesting
• Reduce future subjectivity
• Enhanced reporting capability
• Advising on risk strategy
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34. Director – Client Services Group
garrett.morris@Sageworks.com
984.242.2568
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