data quality considerations for cecl measurement
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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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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).
Forecasting
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should reflect changes to related
relevant data
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).
Forecasting
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should reflect changes to related
relevant data unemployment
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).
Forecasting
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should reflect changes to related
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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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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adjust historical information
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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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
Commercial RE Loan Count Loan Balance Loss Rate Estimated Losses
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
Forecasting - Application
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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%
Forecasting - Application
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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%
Forecasting - Application
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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%
Forecasting - Application
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Include Static Date Balance Charge-offs Recoveries Loss Rate
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)
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
Example calculation – No prepayments – No forecasting
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
Commercial RE Loan Count Loan Balance Loss Rate Estimated Losses
Total 1,150 499,500,000 0.82% 4,115,950
Example calculation – No forecasting
Forecasting - Application
27
Commercial RE Loan Count Loan Balance Loss Rate Estimated Losses
Total 1,150 499,500,000 1.35% 6,752,500
Commercial RE Loan Count Loan Balance Loss Rate Estimated Losses
Total 1,150 499,500,000 0.82% 4,115,950
Commercial RE Loan Count Loan Balance Loss Rate Estimated Losses
Total 1,150 499,500,000 0.55% 2,747,250
Example calculation – Prepayments - Forecasting
Forecasting - Application
28
Commercial RE Loan Count Loan Balance Loss Rate Estimated Losses
Total 1,150 499,500,000 1.35% 6,752,500
Commercial RE Loan Count Loan Balance Loss Rate Estimated Losses
Total 1,150 499,500,000 0.82% 4,115,950
Commercial RE Loan Count Loan Balance Loss Rate Estimated Losses
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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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
Data Assurance Business Case
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Option 1 (Too common) – replication and inconsistency of effort
Accounting
Loan Committee
Audit Regulatory
Credit
Data Assurance Business Case
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1) Model Once
Data Assurance Business Case
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2) Measure Once
Data Assurance Business Case
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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
Garver.Moore@Sageworks.com
Danny SharmanSageworks Integration Services
Danny.Sharman@Sageworks.com
PRESENTERS
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