There’s no shortage of reporting on the dire state of lending, but there has been considerably less intelligent analysis aimed at helping risk managers do their job and deal with their portfolio’s credit health. As risk management comes under increasing pressure, risk managers need to evaluate their current tools and also look at new actions they can take to improve control on existing exposure.

Fair Isaac’s new white paper Managing Risk in the Credit Crunch explores recent Fair Isaac research comparing loan performance by FICO® scores and behavior scores for two recent periods, then discusses actions risk managers are taking today, and recommendations for changes. It’s clear from the evidence that both FICO® scores and behavior scores continue to do a good job rank-ordering accounts by credit risk, although the risk at a given score level is increasing.

For risk managers, these increases in risk—and changes in consumer credit behavior, such as the payment hierarchy—call for increased scrutiny and testing new techniques. In this article, we focus on two key areas from the paper: using transaction analytics, and managing existing credit exposure.

Transaction analytics

We recommend looking at transaction data and patterns for credit and debit products, and including these analyses in customer management scorecards, especially in risk models. Transactions patterns would be useful in traditional modeling techniques, as well as in advanced mathematical techniques that look for changes in customer usage patterns.

Let’s look at two examples of changes indicated by transaction data that would indicate a higher risk potential.

The first example involves the number of payments made per cycle. Most customers pay their bill once a month. A customer or customer segment that increases the number of payments they make on their account within a given cycle may indicate that the customer is now extending their income more narrowly from paycheck to paycheck. Traditional models will not normally look at these data, only the aggregate payment amount. By looking at the number of payments, especially in combination with other risky usage attributes such as accelerated use of cash withdrawal from credit vehicles, the lender will be able to identify variations in usage patterns. 

The second change involves the use of credit in riskier activity. Figure 1 shows the behavior expenditure change for customers that were nondelinquent and in good standing at the beginning of 2007, but had gone seriously delinquent by the end of the year. Three observations about these characteristics merit addressing:

• There are clear changes in usage patterns among those customers that deteriorate in performance. Transaction data would help the models determine the accounts worth intervention.
  
• These changes are pronounced, and convey a change in “responsible” transaction use. For example there is more spent on cash advances and travel and recreation.
  
• Transaction information also indicate an earlier and more detailed measure of  how the accountholder is taking responsibility for future payment and purchase decisions.

The change in usage pattern includes the reduction in the number of expenditures in certain categories, as evidenced by the change in retail and restaurant expenditures. While the introduction of new risky activities such as gambling or cash advance activity can be used as an event or “trigger” to identify an account at risk, it would be hard to create a trigger based on the absence of formerly routine expenditures. This is the kind of change that requires a profile or model.

Some of the patterns could not be observed until after the fact, as they involve the cessation of expenditures rather than specific events. Since these changes are complex, they warrant an analytic approach or model, rather than the use of triggered events.

Including transaction data in a scoring model enables the model to be updated more frequently (e.g., more frequently than the monthly refresh associated with behavior models). These updates can be used as triggers to accelerate timing of customer treatments, so that lenders are responding more quickly to changes in customer behavior. As consumer indebtedness grows and becomes riskier, the ability to intervene in customer activity and promote good customers sooner—or intervene with risky customers sooner—creates large financial and competitive advantages.

Balance control

One of the largest concerns of risk managers in the unsecured lines of business is contingent liability, especially in HELOC and revolving credit products. When and how to manage decreasing a credit line is a huge challenge for many issuers:

• At what level of risk should the line be reduced in order to mitigate future poor-quality receivable growth?
  
• What customers should be promoted to offset the reduced exposure amongst risky customers?
  
• How can responsible and profitable balance build be promoted?

In a credit crunch, lenders often are so diligent about reducing exposure that good customers attrite or stop balance build, preventing quality portfolio growth once the downturn has reached bottom. Lenders need a better way to assess how much credit a consumer can handle responsibly, which is why Fair Isaac has focused on building a new tool to help.

The Fair Isaac Credit Capacity Index™ helps card issuers more accurately identify those card holders who have the capacity to safely handle the “open to buy” they currently have with you, or who would be good targets for a line increase strategy. Like the FICO® score, the Credit Capacity Index is built on credit bureau data and is designed to rank-order consumers according to risk for use within lending strategies. But unlike FICO® scores, which reflect consumer risk today, the Credit Capacity Index measures a consumer’s ability to take on future incremental debt, in the form of both new credit accounts and increases in existing accounts.

When making credit limit reductions, lenders should not only pay close attention to the communication process, but also to how they evaluate and optimize the credit facility reduction (in other words, limit to balance, leave open to buy, reduce cash line, etc.). Instead of reducing the total facility, many lenders now reduce individual components, such as the cash line, either at point of sale, via the authorizations process (with the potential intention of stopping future attempts) or through absolute line reduction requiring adverse consumer communication.

How to reduce the facility is also important. In some cases, the line reduction will be executed at cycle time, or in a special batch program. In others it will be an authorization block or threshold, either directly or through incremental underwriting steps (like validation of identity). These efforts need to be tested thoroughly, as the costs and time frame for return on facility reductions are very different from traditional credit promotions. The key to the success of these programs is ongoing testing of both thresholds (score cutoffs and triggers) and actions.

Managing purchase types

In addition to managing cash facilities, lenders can manage different transaction types, either through line components such as cash line, or through the blocking of specific types of purchases. Mitigation of a customer’s risky balance build across multiple potential uses yields an optimization challenge. The objective is to limit risky types of purchases, while managing slippage in the build of risky balances.

Fair Isaac has observed that changes in customer spending patterns can identify potential bads earlier in the balance build process and prevent losses. While this process is highly complex and can be data-intensive, the observation of riskier spending activity provides a method of isolating riskier customers.

The analytics of how and when to handle this kind of reduction involve multiple dimensions. This can include complex analysis of what offers to make the customer to offset the adverse action. There are three challenges to be met:

• Which lines to lower.
  
• The component to be reduced.
  
• The method of the reduction. Should these accounts have their limits reduced, or be blocked at point of sale, and based on what criteria? Is this a credit facilities or an authorization strategy, or some combination of the two? If it is an authorization strategy, are there additional underwriting requirements at point-of-sale?

Regardless of the method, the data imply that issuers need to be reducing exposure to dormant accounts by more aggressive use of reissue decisions, as well as facility reduction.

While evaluating these actions, it is critical that lenders focus on positive actions they can take on good customer segments that will result in quality receivable build.

Earlier intervention

Any issuer that identifies behavior shifts more frequently than monthly has the potential to gain significant competitive advantage. This requires three advanced elements:

1. Triggers and/or scores calculated more frequently than once a month, enabling the earlier identification of customers’ changes in risk.
   
2. The decision technology to take these triggers and scores and create an appropriate customer treatment.
   
3. A series of actions at varying risk thresholds to promote the customer or mitigate future risk.

These are tools needed to perform predelinquent collections treatment assessment. Again, while the actions we describe below focus on risk mitigation, it is equally imperative that issuers use these advanced capabilities to positively promote good customer balances by proactively promoting price and limit promotions to those customers with good and/or improving behavior.

With pre-delinquent collections treatment assessment, we are identifying customers/accounts at varying stages of risk prior to their going delinquent or building the full balance to be lost, and developing tactics and strategies to mitigate that risk and assure collectability over time. These actions are of necessity independent of delinquent collections activity, and are therefore driven by between-cycle score changes or events.

Let’s consider a lender who evaluates the portfolio and determines five levels of increasing risk (as measured by a standard behavior or transaction-based risk score), as suggested in the example in Figure 2.

 
Each level of increasing risk corresponds with an action:

• No action.
  
• When risk increases, the phone numbers on file for the customer are validated. This is level one intervention because it is least expensive to validate the customer’s numbers in today’s technology
  
• At the next threshold of risk, the lender verifies customer data using the electronic address validation routines at the demographic data providers or the credit bureaus. This is more expensive than the phone validation. Again, the exceptions and changes would be queued for contact and verification.
  
• At the next threshold of risk, an actual contact with the customer is queued. This is used to validate the customer’s intent to pay, or to educate them as to the importance of payment. This may also be the opportunity to evaluate re-pricing opportunities for the balance, and offer the customer a payment plan.
  
• The accounts or customers at the highest level of risk would have adverse action taken. This means reducing their credit facility. Depending on risk level, this could mean the reduction of the overall credit line or facility, or as mentioned above, a reduction of the usage components of the line (issuers can modify 10 such components in Fair Isaac’s TRIAD® adaptive control system).

In addition to analytics that evaluate transaction data, issuers need to have a decision deployment capability that enables treatments at any point within the cycle, based on transactions or triggered events.

Contingent exposure

This downturn, like all others, calls for risk managers to test each tool and process more thoroughly. Those that will fare better will be those with the best decision tools and the best understanding of their data and portfolio. 

Additional prudence and accelerated timing are the keys to lenders’ success in the ongoing management of the cycle. Even as lenders take more aggressive actions to reduce exposure and losses, they should focus on “growing the denominator” by promoting balance build among those consumers who can safely handle additional credit.

Read the full white paper, Managing Risk in the Credit Crunch.

Read the related ViewPoints article: “TowerGroup survey: Banks want sharper analytics and centralized decision management.”