rank orders from a minimum of 0.2 percent to a maximum of about 15.7 percent. There is some variability between the bad rate based on counts and the predicted bad rate from the model (average predicted probability) due to low counts.
● For the score range 163 to 172, for example, the expected marginal bad rate is 5.31 percent. This means 5.31 percent of the accounts that score in that range are expected to be bad.
● For all accounts above 163, the cumulative bad rate, shown in the column “cumulative event rate,” is 2.45 percent. This would be the total expected bad rate of all applicants above 163.
● If we use 163 as a cutoff for an application scorecard, the acceptance will be about 70 percent, meaning 70 percent of all applicants score above 163.
Exhibit 1.2 Gains Chart
Based on factors outlined above, as well as other decision metrics to be discussed in the chapter on scorecard implementation, a company can then decide, for example, to decline all applicants who score below 163, or to charge them higher pricing in view of the greater risk they present. “Bad” is generally defined using negative performance indicators such as bankruptcy, fraud, delinquency, write-off/charge-off, and negative net present value (NPV).
Risk score information, combined with other factors such as expected approval rate and revenue/profit potential at each risk level, can be used to develop new application strategies that will maximize revenue and minimize bad debt. Some of the strategies for high-risk applicants are:
● Declining credit/services if the risk level is too high.
● Assigning a lower starting credit limit on a credit card or line of credit.
● Asking the applicant to provide a higher down payment or deposit for mortgages or car loans.
● Charging a higher interest rate on a loan.
● Charging a higher premium on insurance policies.
● Adjusting payment terms for business customers.
● Asking the applicant to provide a deposit for water/electricity utilities services, or for landline phones.
● Offering prepaid cellular services instead of postpaid, or offering a lower monthly plan.
● Denying international calling access from telecommunications companies.
● Asking high-risk applicants for further documentation on employment, assets, or income.
● Selecting applicants for further scrutiny for potential fraudulent activity.
Conversely, high-scoring applicants may be given preferential rates and higher credit limits, and be offered upgrades to better products, such as premium credit cards, or additional products offered by the company.
Application scores can also help in setting “due diligence” policies. For example, an applicant scoring very low can be declined outright, but those in middling score ranges can be approved but with additional documentation requirements for information on real estate, income verification, or valuation of underlying security.
The previous examples specifically dealt with credit risk scoring at the application stage. Risk scoring is similarly used with existing clients on an ongoing basis. In this context, the client’s behavioral data with the company, as well as bureau data, is used to predict the probability of ongoing negative behavior. Based on similar business considerations as previously mentioned (e.g., expected risk and profitability levels), different treatments can be tailored to existing accounts, such as:
● Offering product upgrades and additional products to better customers.
● Increasing or decreasing credit limits on credit cards and lines of credit.
● Allowing some revolving credit customers to go beyond their credit limits for purchases.
● Allowing better customers to use credit cards even in delinquency, while blocking the high-risk ones immediately.
● Flagging potentially fraudulent transactions.
● Offering better pricing on loan/insurance policy renewals.
● Setting premiums for mortgage insurance.
● Deciding whether or not to reissue an expired credit card.
● Prequalifying direct marketing lists for cross-selling.
● Directing delinquent accounts to more stringent collection methods or outsourcing to a collection agency.
● Suspending or revoking phone services or credit facilities.
● Putting an account on a “watch list” for potential fraudulent activity.
In addition to being developed for use with new applicants (application scoring) or existing accounts (behavior scoring), scorecards can also be defined based on the type of data used to develop them. “Custom” scorecards are those developed using data for customers of one organization exclusively, for example, if a bank uses the performance data of its own customers to build a scorecard to predict bankruptcy. It may use internal data or data obtained from a credit bureau for this purpose, but the data is only for its own customers.
“Generic” or “pooled data” scorecards are those built using data from multiple lenders. For example, four small banks, none of which has enough data to build its own custom scorecards, decide to pool their data for auto loans. They then build a scorecard with this data and share it, or customize the scorecards based on unique characteristics of their portfolios. Scorecards built using industry bureau data, and marketed by credit bureaus, are a type of generic scorecards. The use of such generic models (and other external vendor built models) creates some unique challenges as some of the know-how and processes can remain as black boxes. We will discuss how to validate and use such models in a guest chapter authored by experienced industry figures Clark Abrahams, Bradley Bender, and Charles Maner.
Risk scoring, in addition to being a tool to evaluate levels of risk, has also been effectively applied in other operational areas, such as:
● Streamlining the decision-making process, that is, higher-risk and borderline applications being given to more experienced staff for more scrutiny, while low-risk applications are assigned to junior staff. This can be done in branches, credit adjudication centers, and collections departments.
● Reducing turnaround time for processing applications through automated decision making, thereby reducing per-unit processing cost and increasing customer satisfaction.
● Evaluating quality of portfolios intended for acquisition through bureau-based generic scores.
● Setting economic and regulatory capital allocation.
● Forecasting.
● Setting pricing for securitization of receivables portfolios.
● Comparing the quality of business from different channels/regions/ suppliers.
● Help in complying with lending regulations that call for empirically proven
