Presents business-focused process for the development and implementation of risk prediction scorecards, one that builds upon a solid foundation of statistics and data mining principles. The Risk Manager is now challenged to produce risk adjudication solutions that can not only satisfactorily assess creditworthiness, but also keep the per-unit processing cost low, while reducing turnaround times for cus- tomers. Turnaround time: time required to complete a task

In the past, financial institutions acquired credit risk scorecards from a handful of credit risk vendors

1 - application software became available that allowed users to develop scorecards without investing heavily in advanced programmers and infrastructure

2 - advances in intelligent and easy to access data storage have removed much of the burden of gathering the required data and putting it into a form that is amenable to analysis

Experience has shown that in-house credit scorecard develop- ment can be done faster, cheaper, and with far more flexibility than before.

Custom scorecard - developed using internal resources

Scorecard General Overview:

a scorecard consists of a group of characteristics, statistically determined to be predictive in separating good and bad accounts.

Scorecard characteristics may be selected from any of the sources of data available to the lender at the time of the application. Examples of such characteristics are demographics (e.g., age, time at residence, time at job, postal code), existing relationship (e.g., time at bank, number of products, payment performance, previous claims), credit bureau (e.g., inquiries, trades, delinquency, public records), real estate data, and so forth.

The total score of an applicant is the sum of the scores for each attribute present in the scorecard for that applicant.

Some of the strategies for high-risk applicants are:

These are the strategies followed at Application stage:

  • 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

  • Asking the applicant to provide a deposit for utilities services

  • Offering prepaid cellular services instead of postpaid

  • Denying international calling access from telecommunications companies

  • Putting the applicant into a “watch list” for potential fraudulent activity

Conversely, high-scoring applicants may be given preferential rates and higher credit limits, and be offered upgrades to premium products, such as gold or platinum cards, or additional products offered by the company.

Strategies followed at ongoing stages are:

  • Offering product upgrades and additional products

  • Increasing credit limits on credit cards and lines of credit

  • Allowing some revolving credit customers to go beyond their credit limits

  • Flagging potentially fraudulent transactions

  • Offering better pricing on loan/insurance policy renewals

  • 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

  • Put an account into 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 : developed using data for customers of one organization exclusively. Uses internal data or data obtained from the credit bureau for this purpose, but the data is only for its own customers.

Generic or Pooled data Scorecards: built using data from multiple lenders. Merging data from multiple banks. Scorecards built using industry bureau data and marketed by credit bureau are types of generic scorecards.

Risk scoring, therefore, provides creditors with an opportunity for consistent and objective decision making, based on empirically derived information. Combined with business knowledge, predictive modeling technologies provide risk managers with added efficiency and control over the risk management process.

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