maintaining credit quality in banks and credit unions

Neekis HammondSenior Risk Management Consultant

Sageworks

P R E S E N T E D B Y

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• Ask questions throughout the session using the GoToWebinarcontrol panel

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

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• 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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Neekis Hammond

Senior Risk Management ConsultantSageworks

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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.

Part 1: How financial institutions can maintain credit quality

» Why monitor quality during growth periods

» Who is responsible for credit quality?

» How each department can help maintain credit quality

Part 2: Measuring credit quality

» Tracking credit quality trends of the loan portfolio

» Drawing insights from credit quality reports

» Updating strategy based on data

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Goal is to reduce subjectivity and improve documentation in all departments

1. Prospect intelligently using data

» Prioritize lower risk industries, products, geographies, etc.

2. Request a consistent set of product-specific documents

» Ensures comparability throughout the loan approval process

» Reduces bottlenecks that can slow down approvals

3. Request documents in digital form

» Decreases manual errors and reduces costs

» Ensures necessary data is available in system for future loan reviews

4. Motivate lenders to use risk-based pricing

» Encourages lenders to protect asset quality

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1. Require consistent spreading and cash flow analysis

» Consider using automated systems for analysis

» Avoids double-counting and reduces subjectivity

2. Standardize the risk rating process

» Directly impacts the allowance calculation

» Sets the stage for when and how loans will be reviewed

» Creates template-specific measures and thresholds for risk

3. Compare borrower to peer set

» Shows strengths and weaknesses of borrower ratios

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1. Update and manage loan documents

» Provides leading indicator of future credit risk

» Automated system creates standard practices, improves efficiency and reduces the likelihood of human error

2. Improve the stress testing process

» Excellent way to forecast issues that may impact your allowance as well as your capital ratios

3. Use probability of default models to inform decisions

» Uses historical data and trends to inform underwriting and strategy decisions

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1. Stay up to date with regulatory changes

» Gives institution time to prepare data and processes for change

2. Leverage results of stress tests

» Provides opportunities to plan for contingencies and for meeting capital ratio requirements

» Gives management a sense of future potential credit risk

3. Fully utilize allowance data

» Provides a consistent and defensible methodology

» Allows management to monitor credit quality by reviewing trends in charge offs, concentration growth and watch lists

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Review risk grade experience to ensure proper correlation with losses.

0.00% 0.00% 0.00% 0.18%

2.56%

1.36%

4.35%

7.31%

0.00%0.00%

1.00%

2.00%

3.00%

4.00%

5.00%

6.00%

7.00%

8.00%

0 1 2 3 4 5 6 7 8

Loss

Rat

e

Risk Rating

Data analysis and management reports should be used to evaluate the need to make adjustments to the risk rating system and underwriting/renewal process.

Loan review department should:

» Review loans after 90 days

» Be able to re-rate the loan and get the same risk rating

» Look for proper documentation

» Report back on the risk rating variation

» Institute controls for updating existing loans quarterly/annually

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Institutions can use migration analysis to evaluate risk rating processes over time

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Migration/Default Rate

Beginning Risk Rating 0 1 2 3 4 5 6 7 8 Default Total

0 77% 0% 0% 10% 11% 1% 1% 0% 0% 2% 100%

1 0% 98% 0% 2% 0% 0% 0% 0% 0% 1% 100%

2 0% 0% 79% 18% 2% 1% 0% 0% 0% 1% 100%

3 0% 0% 0% 97% 2% 0% 1% 0% 0% 1% 100%

4 0% 0% 0% 4% 92% 1% 2% 1% 0% 3% 100%

5 0% 0% 0% 0% 7% 81% 5% 4% 3% 8% 100%

6 0% 0% 0% 1% 4% 2% 78% 7% 8% 22% 100%

7 0% 0% 0% 0% 0% 2% 17% 53% 28% 38% 100%

8 0% 0% 0% 0% 0% 1% 14% 3% 82% 37% 100%

Grand Total 4% 2% 3% 68% 17% 2% 2% 1% 1% 2% 100%

Institutions can use Probability of Default to analyze risk rating performance over time.

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Beginning Risk Rating

Note Type 0 1 2 3 4 5 6 7 8 Total

C & I 0% 1% 1% 5% 10% 23% 37% 57% 0% 4%

Construction 0% 0% 1% 4% 13% 28% 64% 37% 0% 6%

Multifamily 1% 0% 1% 2% 8% 18% 52% 27% 0% 1%

Non-Owner 0% 0% 5% 0% 1% 3% 19% 15% 0% 3%

Owner 0% 0% 0% 1% 3% 9% 17% 50% 0% 2%

Personal Credit Lines 0% 0% 2% 1% 5% 5% 15% 29% 0% 2%

Residential Real Estate 0% 0% 0% 2% 3% 26% 41% 38% 0% 1%

Total Default Rate 0% 1% 2% 4% 8% 22% 38% 36% 0% 2%

LGD adds impact to the equation and allows institutions to make strategic decisions about Probability of Default.

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Note Type Net Charge Off Exposure at Default LGD

C & I 9,200,000 51,000,000 18%

Construction 3,500,000 7,500,000 47%

Multifamily 250,000 1,000,000 25%

Non-Owner 3,600,000 158,000,000 2%

Owner 1,100,000 10,000,000 11%

Personal Credit Lines 710,000 860,000 83%

Residential Real Estate 1,600,000 13,000,000 12%

Total 19,960,000 241,360,000 8%

Loss Rates combine PD and LGD to give an overall rate of loss for each risk rating and product.

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Beginning Risk Rating

Note Type 0 1 2 3 4 5 6 7 8 Total LGD

C & I 0.0% 0.2% 0.2% 0.9% 1.8% 4.1% 6.7% 10.2% 0.0% 0.7% 18%

Construction 0.0% 0.0% 0.5% 1.9% 6.1% 13.2% 30.2% 17.5% 0.0% 2.8% 47%

Multifamily 0.0% 0.0% 0.0% 0.0% 0.2% 0.4% 1.2% 0.6% 0.0% 0.0% 2%

Non-Owner 0.0% 0.0% 1.2% 0.0% 0.2% 0.7% 4.4% 3.5% 0.0% 0.7% 23%

Owner 0.0% 0.0% 0.0% 0.1% 0.3% 1.0% 1.9% 5.5% 0.0% 0.2% 11%

Personal Credit Lines 0.0% 0.0% 1.6% 0.8% 4.1% 4.1% 12.4% 23.9% 0.0% 1.6% 82%

Residential Real Estate 0.0% 0.0% 0.0% 0.2% 0.4% 3.1% 4.9% 4.5% 0.0% 0.1% 12%

Total Default Rate 0.0% 0.2% 0.4% 0.8% 1.7% 4.6% 8.0% 7.6% 0.0% 0.4% 21%

Red Flags:» Inconsistent loss curve when evaluating risk rating performance.

» Inconsistent default or loss characteristics within the same risk rating across business lines.

» Dramatic shifts in upgrades or downgrades.

» Lack or shifts in upgrades or downgrades.

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Takeaways and Action Items:» Evenly distributed loss curve provides for risk rating support and defensibility

» Consistent loss rates across products and risk ratings provides for institution utilization

» Risk ratings outperforming expectations may indicate growth and yield opportunities

» Products with positive migration movement may provide for customer retention initiatives

» Compare PD, LGD, Yield and WAM for pricing opportunities

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Once your institution is comfortable with your risk rating system and PD/LGD reporting, you can use this to make strategic decisions.

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Strategies PD LGD WAM WAY Growth

Strategy 1

Strategy 2

Strategy 3

Over time, you should standardize your reporting into a consistent Portfolio Credit Quality Report

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Note Type Balance WAY WAM PD LGD

C & I 150,000,000 5% 3 4% 18%

Construction 50,000,000 5% 2 6% 47%

Multifamily 50,000,000 4% 5 1% 25%

Non-Owner 200,000,000 4% 5 3% 2%

Owner 250,000,000 4% 6 2% 11%

Personal Credit Lines 100,000,000 6% 4 2% 83%

Residential Real Estate 200,000,000 4% 8 1% 12%

Total 1,000,000,000 4% 4 2% 8%

Run a peer analysis report to see how your institution compares to your peers

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3.98%

3.73%3.00%3.50%4.00%4.50%5.00%5.50%6.00%6.50%

Average of12/31/2006

Yield on EarningAssets



















Above Avg. C&I Below Avg. C&I

4.53%

2.21%

0.00%

1.00%

2.00%

3.00%

4.00%

5.00%

Average of 12/31/2011 Loan GrowthRate






0.65%

0.11%

0.32%

0.02%

0.00%0.10%0.20%0.30%0.40%0.50%0.60%0.70%

Average of12/31/2006 NetCharge-Offs to

Loans


Loans


Loans


Loans


Loans


Loans


Loans


Loans


Loans


Loans


1.29%

0.82%

0.60%

0.80%

1.00%

1.20%

1.40%

1.60%

Average of12/31/2006 Loss

Allowance toLoans


Allowance toLoans


Allowance toLoans


Allowance toLoans


Allowance toLoans


Allowance toLoans


Allowance toLoans


Allowance toLoans


Allowance toLoans


Allowance toLoans


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Neekis HammondSenior Risk Management [email protected]

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maintaining credit quality in banks and credit unions

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