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

  • Advanced Fraud Rule Engines: Complex Policies and Third-Party Data

    Fraud prevention is no longer a single check. It depends on a fraud rule engine that enriches data from multiple sources, applies clear knockout rules, and combines scoring with decision logic in real time. This article explains how complex policies, third-party data, behavioral signals, and machine learning fit together in a practical antifraud setup.

  • How to detect Google Suite hosted email address

    This article explains how to detect Google Suite-hosted email addresses by checking DNS MX records, and why public data cannot reliably show whether an account is a paid G Suite instance or a legacy free Google Apps account. It then shows how to query MX records through a DNS API and implement the check in Decisimo with a sample data object, data source, and decision flow.

  • How to integrate Google Places API Find Place into a decision engine

    This article shows how to connect Google Places API Find Place to a decision engine and use it for address validation and profiling. It walks through getting an API key, creating the data source in Decisimo, and adding it to a decision flow so address checks run in parallel with the rest of your logic.

  • Why segmented pre-collection activity matters for lending businesses

    Segmented pre-collection helps lending businesses focus on accounts that need attention before they become delinquent. This article explains what pre-collection is, why segmentation matters, and the main ways lenders can use it to improve recovery without unnecessary contact with borrowers who are likely to pay on time.

  • Outsourcing vs. Insourcing Debt Collection: Key Considerations and Potential Impacts

    Choosing between insourcing and outsourcing debt collection comes down to control, cost, expertise, and risk. This article breaks down the key tradeoffs, from debt type and recovery economics to brand impact and operational flexibility, so you can decide which approach fits your business.