AI Drives Collection Revolution- Decisimo

Published on: 2024-08-10 18:35:48

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
Decisimo decision engine

Try our decision engine.

Related Articles

  • Build a Loan Application Demo with Retool and Decisimo

    Build a loan application demo that automates underwriting with Retool and Decisimo decision logic. Import the Consumer Lending template in Retool and capture application data. Set up Decisimo endpoints, rulesets, and decision tables, deploy the flow, and update the Retool query.

  • Antifraud Hub

    Explore the antifraud tactics, tracking methods, and rule-based approaches covered in this hub. From fingerprinting and synthetic identity fraud to case management and login security, these articles show how fraud teams can structure decision logic to detect risk and respond faster.

  • Better decision making with alternative data

    Alternative data can improve decision making, but only when the source is reliable and the collection method is clear. This article explains the difference between primary and secondary data, where enrichment helps, and why assisted login data collection creates specific risks for data quality and integrity.

  • Evaluating the ROI of Decision Engines in Financial Companies

    This article explains how to evaluate the ROI of a decision engine in financial underwriting, using a Southeast Asia loan example. It compares manual and automated decision logic, estimates monthly and annual savings, and covers integration costs, error reduction, and faster decisioning.

  • How to Monitor Scorecards Without Missing the Signals That Matter

    A scorecard can look healthy on a dashboard and still drift in ways that change decisions. The key is to monitor more than one metric: shifts, attribute stability, bin stability, predictive power, rank order, and the behavior around cutoffs.