profits from losses: 4 ways banks can use alll data to improve operations and boost earnings

The allowance for loan and lease losses (ALLL) is one of the most important and complex estimates a bank makes. An appropriate ALLL estimate is essential to the integrity of a bank’s financial statements and integral to its credit risk management process.

But gathering the required data inputs for a rigorous ALLL assessment is an arduous task, both time-consuming and expensive. Once the data are in hand, they are subjected to multiple layers of internal review, external audit and regulatory examination. As a result, ALLL data are probably some of the most tested and trustworthy information available inside a lending institution. So, besides estimating a reasonable ALLL, what else might banks do with this data to better manage the bank? We believe banks can offset the cost of collecting this valuable information by leveraging it in a few meaningful applications to reduce costs and increase opportunities for profits.

Profits from losses: 4 ways banks can use ALLL data to improve operations and boost earnings

Estimating the ALLLInformation gathered for the ALLL calculation covers an extremely wide scope, ranging from terms for an individual loan to national economic statistics. The data include:• Charge-offs by loan, loan type, risk grade, officer

and location• Risk grades and categories of loans, and

loan portfolios (including past due and performance information)

• Collateral valuations • Loan portfolio balances by type, risk grade

and seasoning• Recoveries by loan, loan type and risk grade• Bankruptcies and fraud by loan type, vintage

and period• National, regional and local economic indicators• Loss discovery period (i.e., the time from loss

to charge-off or default, as discussed in more detail below)

• Loan restructuring nature and frequency

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To help your organization get started, below we outline four ways you can use the valuable, high-cost information that flows through your ALLL estimation process to do the following:

1. Reduce bad debt losses

2. Lower the overall level of the ALLL

3. Achieve better loan performance by linking compensation to performance

4. Improve product selection and pricing

You may be ahead of the game and already have in place a few creative applications for ALLL data, and even so, we hope this paper will stir up a few new ideas.

1. Reduce bad debt lossesAn analysis of historical loss patterns — which include correlations to loans’ origination source, underwriting terms and so forth — can reveal favorable and unfavorable trends and tendencies that can be used to trim bad debt losses. For example, a loan product may begin to perform poorly because of changing economic conditions in a specific geographic area. By identifying these changes, a bank can begin to cut losses.

Besides loan products, looking for patterns (correlations) between losses and other factors may be useful in detecting problems with the following:

• Loan officers

• Bank branches

• Collateral types and acceptable loan-to- value thresholds

• Underwriting policies, including covenants

• Loan monitoring activities, frequency and follow-up

Armed with this knowledge, banks can manage their products, people and investments in technology to improve bank efficiencies and increase profitability by taking measures that include the following:

• Dropping or modifying loan products

• Taking appropriate personnel actions

• Closing or reallocating resources among branches

• Improving collateral policies

• Modifying reliance on loan guarantees

• Modifying loan covenants and customers’ compliance report deadlines, as well as generating more frequent and more reliable financial information

• Reacting to business cycles more quickly and effectively — in other words, knowing when to lend and when to pull back

• Investing in technology to improve timeliness and effectiveness of loan administration and problem follow-up

Achieving substantial benefits requires more than an ad hoc approach, however. Banks should institute a formal program that quantitatively analyzes sources of losses and, based on that study, recommends changes in lending practices. Even better would be a continuous program of data analysis and experimentation that seeks to hone products, processes and technology over time.

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2. Lower the overall level of the ALLLReceiving and acting on more frequent, reliable information from and about borrowers can help (a) reduce charge-off rates and (b) shorten loss discovery periods. Achieving either or both objectives will generally decrease the needed ALLL balance, lowering loss provisions.

Charge-off ratesWhy is timely, accurate information important? The ALLL (under the current incurred loss accounting standard) is the bank’s best estimate of the unconfirmed loan losses incurred from the inception (or acquisition) of loans up to the date of the financial statements. As losses are incurred, a provision is made through bad debt expense, adding to the ALLL balance. As losses are confirmed, they are written off and the ALLL is reduced (see Figure 1).

Loan losses are, of course, not immediately known. Deciding whether a loan might have gone bad, and how much the loss will likely be, requires information that a bank doesn’t immediately receive when loss-causing events occur — and may not even be immediately known by the bank’s client. For example, a borrower may be unaware that the business of its major customer is worsening and it will soon be cutting orders substantially.

The better the information the bank has about its customers, and the more quickly it gets it, the more effectively it can pursue repayment. Faster reaction

to borrower repayment issues assists the collection process and results in reduced charge-off rates, leading to lower estimated loan losses.

Loss discovery periodThe loss discovery period (LDP) — also called the loss emergence period or loss confirmation period — is the time it takes, on average, for the bank to identify the specific borrower and amount of loss incurred by the bank for a loan that has suffered a loss-causing event. The LDP begins with the event or culmination of events that causes the borrower to be unable to honor its commitment and ends with the confirmation of the loss.

As noted in our simplified formula shown in Figure 2 for the measurement of the FAS 5 component, the computation often starts with the bank’s charge-off rate by loan type adjusted for changes in conditions between the period leading up to the charge-off and the conditions at the date of the financial statements (usually called qualitative factors).

Whether the charge-off rate in the formula is calculated based on an average of one, three or five years of charge-offs divided by the time period selected — or using some other approach — the charge-off rate generally is stated in terms of an annual rate. That is, it’s a factor that describes how much in charge-offs for a given loan type have been (and may be expected to be) taken, on average, over a one-year period.

Figure 1

Loss happens…. the problem borrower is identified…

and the loss is confirmed and charged off

Loss discovery period

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It is often assumed, explicitly or implicitly, that multiplication of the adjusted annual rate of charge-offs times the applicable period-end loan balance will give the appropriate allowance amount. That assumption would be correct only if it takes, on average, one year from the loss-causing event for the loan loss to be confirmed and the loss to be charged off. This is seldom actually true.

We note that the charge-off rate (for example, an annual rate) is independent of the loss-averaging period selected for its calculation. The bank could average charge-offs over a five- or 10-year period or over the most recent quarter and use that to calculate an annual charge-off rate. The point of tracking charge-offs and attributing them to loan types and risk categories is to calculate appropriate average rates.

But the rate must be combined with a reasonable estimate of the LDP, which might be one year, more than one year or less than one year, to arrive at an estimate of the dollar amount of losses. That is, mathematically, a rate of loss does not by itself tell us the amount of loss in a loan portfolio. So what if it takes a longer or shorter period for losses to be confirmed? In that case, the LDP time period should be incorporated into the allowance calculation such that the allowance is not understated or overstated. (The Grant Thornton LLP paper Allowance for loan and lease losses (ALLL): Loss discovery periods provides a more in-depth discussion and explanation of the LDP.)

Under GAAP, banks use the guidance provided by ASC 450 (formerly, FAS No. 5 — Accounting for Contingencies) to estimate the ALLL1 (see Figure 2).

The faster the bank learns vital information that determines losses, the shorter the LDP will be, and thus, the lower the needed ALLL (and loan loss provisions) will be. Using the information the bank collects with respect to loss rates and LDPs, management can spot opportunities to make changes in loan terms and administration that shorten the LDP.

Figure 2

ASC 450 General Reserve = Loan

category balance

Historical charge-off

rate

Adjustment factor

Loss discovery

period x[ + x[Once a bank has the right data…

…it can make changes that add value.

Frequency of borrower reports of sales, cash flow, inventory

Adjustments in loan covenants and follow-up practices

Delay in receiving financial statements

Adjustments in loan covenants, penalty clauses and follow-up practices

Frequency of inspection of operations, inventory counts

Loan servicing practices, loan officer behavior

Covenant ratios/metrics, cure and reaction times

Adjustments in loan covenants and follow-up practices

Figure 3Factors affecting the loss discovery periods can be managed

To obtain the benefits of lower loss provisions, institutions should have a formal program to examine charge-off and loss discovery period data for clues that will lead to improved underwriting terms and loan administration. Good data analysis and experimentation with the balance between costs and the benefits of measures to reduce charge-offs and LDPs can help optimize profits and respond to changing circumstances.

1 The calculation is for grouped loans. Under ASC 310 (formerly FAS No. 114 — Accounting by Creditors for Impairment of a Loan), individual loans are treated separately. See the two previous papers in this series — Allowance for loan and lease losses (ALLL) adjustment factors and Allowance for loan and lease losses (ALLL): Loss discovery periods — for an in-depth discussion of the distinction and the different treatments.

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3. Achieve better loan performance by linking compensation to performance The manner in which loan originators are compensated can incentivize loan originations or it can incentivize profitable loan originations. To be profitable, the loans need to perform over their lifetime, not just meet minimal underwriting guidelines at the moment of origination. Therefore, to build a better incentive system for loan originations there is a need for information about each originator’s loan performance to be fed back into the compensation system.

The Truth in Lending Act (TILA) restricts banks’ ability to tie staff compensation to loan terms but not to loan performance.2 Notably, compensation cannot be based on loan transaction terms (such as higher interest rates) or prepayment penalties.

But banks can link compensation to these yardsticks:

• Origination of loans

• Long-term performance of an originator’s loans

• Realizing performance goals over the life of a portfolio

• Rewards for new versus existing customer loans

• Originator’s file quality

As we’ve seen, charge-offs can be tracked over time at different levels of detail, including loan type, risk grade, branch and officer. Such charge-off information linked to personnel, besides being highly useful for reducing bad debts, can also be a factor in employee pay. Often this is accomplished through the adoption of a form of deferred compensation, determined over time on the basis of portfolio performance (e.g., expected versus realized yield that considers such items as prepayments and loan losses). Charge-off data can thus be employed not only to monitor employee performance, but reward it as well.

Portrait of the 2013 Loan Originator RuleIn June 2013, the Consumer Financial Protection Bureau (CFPB) came out with the “2013 Loan Originator Rule” based on Reg. Z §1026.36(a)(1) of TILA. These rule changes came out of the Dodd–Frank Wall Street Reform and Consumer Protection Act.

These rule changes have evolved since the 2009 draft regulations issued by the Federal Reserve Board (FRB). • Aug. 26, 2009: FRB publishes a Proposed Rule in

the Federal Register pertaining to closed-end credit. As part of that proposal, the FRB seeks to prohibit certain compensation payments to loan originators and reduce incentives to steer consumers to loans with particular terms.

• Dec. 24, 2009: Comment period of the Proposed Rule expires.

• July 21, 2010: Dodd-Frank is enacted into law. Among other provisions, Title XIV amends TILA to establish certain mortgage loan origination standards.

• Aug. 16, 2010: FRB publishes Final Rules amending Regulation Z.

• Sept. 24, 2010: FRB issues final rulemaking and official staff commentary with respect to the loan originator compensation rules and anti-steering provisions.

• Jan. 26, 2011: FRB issues Compliance Guide for Small Entities on Loan Originator Compensation and Steering.

• January 2013: CFPB issues final regulations.

2 See CFPB “2013 Loan Originator Rule” based on Reg. Z §1026.36(a)(1) of the Truth in Lending Act.


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4. Improved product selection and pricingA basic premise of finance is that cash yields on a loan portfolio should over time cover the cost of funds, operations and loan losses and provide a return on investment commensurate with the risk. So the obvious question is whether the bank is performing such analyses on the performance of its loan products. Considering loan loss information available from the ALLL estimation process, and comparing the return on investment to the bank’s cost of capital, could be very useful.

Collateral value analysis is another good example of where benefits may be gained from available data. Collateral values and changes in value are used primarily for underwriting and evaluating the risks of specific loans. But analysis of this data, summarized by locale and industry, can also signal economic trends and business conditions (good and bad). Banks should take a look at how they’re tracking collateral information at macro levels (e.g., by geographic region and collateral type), and whether it’s being incorporated into economic analysis of geographic expansion and lending product decisions.

Banks might also wish to examine their aging and re-aging experience.3 Aging information, analyzed by geography, loan product, loan type, industry, etc., can inform management about trends (good and bad) in geographies serviced and categories of customers. It may also reveal product and credit opportunities.

ContactsDorsey BaskinManaging PartnerAssurance ServicesT +1 214 561 2328E [email protected]

Jack KatzGlobal LeaderNational Managing PartnerFinancial ServicesT +1 212 542 9660E [email protected]

ConclusionThe flow of expensive, high-quality information into the ALLL estimation process should be used to improve management of the bank, not just be filed away. The data gathered for the ALLL methodology should be analyzed in a continuous feedback loop with the product management, loan underwriting, pricing, borrower relationship management and human resources functions. Better use of ALLL information can result in higher earnings, ROI and return on equity; improved capital; and a more favorable view of the bank by both investors and regulators.

ALLL information for stress testsThe data gathered in the ALLL estimation process could be part of the baseline historical data used to support the modeling in stress testing. For example, banks might track loan losses in commercial real estate loans (often floating rate and LIBOR-linked) versus fluctuations in LIBOR to establish a correlation between interest rates and loan losses, and use that correlation to drive the impact on loan losses in stress tests. In addition to establishing correlations between levels of stress and losses, the analysis of losses over complete business cycles can point toward reasonable limits and boundaries reflecting worst-case scenarios. Stress-testing exercises and forecasting under the Current Expected Credit Loss (CECL) model may become complementary exercises in the future — or, at the very least, they shouldn’t be contradictory. To some extent, the same data used to forecast loan losses under CECL may be used in stress-testing approaches.

3 “Aging accounts” is the act of classifying accounts by the length of delinquency to determine the likelihood of payment. Re-aging is reclassifying a past-due account as current. Guidelines concerning account re-aging have been issued by the Federal Financial Institutions Examination Council.

profits from losses: 4 ways banks can use alll data to improve operations and boost earnings

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