AI Collection Scorecard: Predict Repayment Likelihood

Debt collection specialists in the analytical field play a vital role in predicting the likelihood of recovery and optimizing collection strategies. One useful tool for achieving this is a collections scorecard. In this article, we will outline how to build a comprehensive collections scorecard that accurately predicts the probability of recovery.

I. Variables and Data Types

A. Collection Behavior Data

  1. Number of phone calls made in the last 1, 2, and 3 periods
  2. Total number of phone calls made
  3. Number of phone calls connected in the last 1, 2, and 3 periods
  4. Total number of phone calls connected
  5. Connection rate for the last 1, 2, and 3 periods
  6. Pverall phone connection rate
  7. Number of days lost contact in the last 1, 2, and 3 periods
  8. Total days lost contact
  9. Number of valid friends
  10. Number of valid contacts
  11. Average call duration per call
  12. Total call duration
  13. Total number of call backs
  14. Total collection messages sent
  15. Total number of collectors involved
  16. Total number of collection letters sent

B. Customer Personal Information

  1. Age
  2. Gender
  3. Occupation
  4. Education
  5. Monthly income
  6. Marital status
  7. Housing situation

Frequently asked questions

What does a collections scorecard predict?

A collections scorecard estimates the likelihood that money owed will be recovered. It uses collection behavior data and customer personal information as variables for predicting recovery. Listed behavior measures include calls, connection rates, lost-contact days, and collection activity.

What data can a collections scorecard use?

A collections scorecard can use collection behavior data and customer personal information to estimate recovery likelihood. Collection behavior data includes call attempts and connections, contact rates, lost-contact days, valid friends and contacts, call duration, callbacks, messages, collectors involved, and letters sent.

What customer information can a collections scorecard include?

Customer personal information listed for a collections scorecard includes age, gender, occupation, education, monthly income, marital status, and housing situation. These are distinct from collection behavior measures, such as calls, contact rates, and collection messages.

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