q factors: data, drivers and documentation
TRANSCRIPT
Q FACTORS: DATA, DRIVERS
AND DOCUMENTATION
Date of last revision: May 27, 2015
Date of last review: May 26, 2015
Questions
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Slides: http://web.sageworks.com/qualitative-
factors-slides/
About Sageworks
+ 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
+ Provides resources including: whitepapers, webinars, videos, and templates for bankers, accessible at www.sageworksanalyst.com
Who will be speaking?
Emily Bogan - Moderator
Sr. Risk Management
Consultant at Sageworks
Aaron Lenhart - Presenter
Risk Management Consultant
at Sageworks
Learning Objectives
+ Q Factor Overview
+ Challenges
+ Drivers
+ Internal factors
+ External factors
+ Other Q Factors
+ How to present Q Factors to examiners/auditors
+ Future of Q Factors
What are Qualitative Factors?
+ Qualitative and environmental factors are used to reflect risk in the portfolio not captured by the historical loss data
+ Made as adjustment to historical loss experience
+ Opportunity to leverage your unique knowledge of portfolio
Largest Obstacles
+ Limiting subjectivity
+ Justifying assumptions
+ Providing proper documentation and defense
Challenge – little direction from guidance
+ Subjective by definition
+ 2006 Interagency Policy Statement on ALLL provides little direction
+ “Management should consider those current qualitative or
environmental factors that are likely to cause estimated credit losses
as of the evaluation date to differ from the group's historical loss
experience.”
+ “These determinations are to be based on a comprehensive, well-
documented and consistently applied analysis of its loan portfolio.”
Challenge – what data/drivers to use?
+ Any adjustments to Q Factors must be thoroughly supported with data
+ No direct guidance on what data to use
Drivers for Internal Q Factors
1. Changes in lending policies & procedures
+ Considerations
+ Have lending policies and procedures changed in a way that will affect the
collectability of the portfolio, not considered elsewhere?
+ Have there been noted changes to:
+ Underwriting standards and collection?
+ Charge-off and recovery practices?
+ Supporting Data
+ Changes in debt coverage ratios (DSCR, D/I) and LTV
+ % renewed with policy exceptions
2. Changes in nature/volume of portfolio
+ Considerations
+ Has the nature or volume of the portfolio changed in a way that would affect risk
+ Has lending commenced or ramped up in new or riskier markets?
+ Supporting Data
+ Number of new loan products (or products for which bank has no substantial
loss history)
+ % change in high risk lending (Concentration reports)
+ Concentration stress test results (maturity analysis, vintage analysis)
External data – Sageworks Industry Data
+ Objective industry analysis based on financial performance metrics weighted by NAICS code
+ More granular analysis to reflect the unique industry composition of each pool
3. Changes in lending management/staff
+ Considerations
+ Has there been turnover among lending management?
+ What is the average tenure of lending management?
+ Have training or professional development programs been strengthened?
+ Supporting Data
+ Turnover rates; # of new positions; Change in % of staff with <3 years experience
+ Average tenure of lending staff; % with >good performance
4. Changes in volume/severity past due loans
+ Considerations
+ For past due, nonaccrual, and substandard (or worse) or watch list loans; has the
trend improved or worsened?
+ Supporting Data
+ Past due loans/Total loans
+ Nonaccrual loans/Total loans
+ # or % of TDRs
5. Changes in quality of loan review system
+ Considerations
+ Has the scope (e.g. portfolios, lenders) of the review or experience of the review
team changed?
+ Supporting Data
+ # and trend of documented deficiencies and exceptions
+ # and trend of any inconsistencies in assignment of ratings
+ Frequency of reviews
+ Average tenure of review team and staff levels
6. The existence/effect of credit concentrations
+ Considerations
+ What concentrations exist and warrant additional analysis (impact to capital)?
+ Loan types
+ Geographic areas
+ Specific industries
+ Supporting Data
+ Concentration reports (current balance, total commitment, % of risk based capital)
+ Concentration stress test results
Drivers for External Q Factors
1. Changes in economic & business conditions
+ Considerations
+ Are macro/national economic factors improving or deteriorating?
+ What about regional/local factors?
+ Supporting Data
+ GDP, CPI/PPI, National unemployment, Consumer Confidence
+ State/MSA/County unemployment trends,
+ Industry specific employment, Housing starts
External data - FRED
+ Federal Reserve Economic Data (FRED) provides free, customizable macro-level data:
2. Changes in value of underlying collateral
+ Considerations+ What is the general valuation environment?
+ Are prices trending up or down?
+ Has your process for determining collateral values improved?
+ Supporting Data+ Occupancy/rent rates
+ # or % of RE-secured loans with LTV > 70%
+ % of cash/CD secured and unsecured loans in the portfolio
+ % of appraisals > 2 years old
+ # of “stale” appraisals
+ Case Shiller Index
3. Effect of other external factors
+ Considerations
+ Has the competitive landscape changed? If so, what changes has it prompted at
your institution? Undertaking additional risk?
+ Have new laws or regulatory changes affected collectability?
+ Data used?
+ Competition may result in marginal debt coverage ratios or weaker LTVs
+ Impact of regulatory changes or litigation may be evaluated with updated financials
Other Q Factors?
Other factors?
+ Can be used for institutions that have unique risk
scenarios to incorporate
+ Ex: For bank with large concentration of loans to Native American
businesses, tribal news might be a significant factor
+Ex: Dependence on specific industry (coal, oil/gas, etc.)
Presenting Q Factor Adjustments
+ As factors change direction, qualitative rates should
change accordingly:
Presenting Q Factor Adjustments
Future of Q Factors
+ Transitioning to an expected loss model
+ Forward looking adjustments
+ Q-factors will play an expanded role
+ Basel Committee’s consultative document alludes to forecasting component of Q factors in ECL model:
+Examples of factors that may require qualitative adjustments are the
existence of concentrations of credit risk and changes in the level of such
concentrations, increased usage of loan modifications, changes in
expectations of macroeconomic trends and conditions, and/or the
effects of changes in the underwriting standards and lending policies […].”
Questions
Emily BoganSr. Risk Management Consultant
866.603.7029 ext. 770
Aaron LenhartRisk Management Consultant
866.603.7029 ext. 532
2015 Risk Management Summit
+ September 23-25 in Chicago
+ ALLL & Stress Testing
+ Peer Roundtables
+ Banker Appreciation Night on Lake Michigan
+ sageworks.com/summit
Todd Sprang
Principal
CliftonLarsonAllen
David Heneke
Principal
CliftonLarsonAllen
John Behringer
Partner
McGladrey
Graham Dyer
Senior Manager
Grant Thornton
Resources
+ The destination website for the ALLL calculation
+ Latest news, peer discussions, industry expert opinions
+ www.ALLL.com
+ www.sageworksanalyst.com
+ Whitepapers, webinars, thought leadership
+ CECL Webinar
+ Fill out form, we’ll email you invite when guidance passed
+ Web.sageworks.com/CECL/
Questions?