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Collection Analytics

Objective

To develop an easy to use statistical tool that scores active accounts in the portfolio on their propensities to default in the coming billing cycle.

Methodology

  • Used historical profile, payment and last two years’ collections data to develop statistical models to predict future default behavior.
  • Developed a MS Excel based tool with the model algorithms built into it.
  • The tool is run on 1st day of every billing cycle; it scores all the accounts in the portfolio on their propensity to default.
  • The scores are used for targeted collection treatments.

Impact

The collections team now targets only top 20% accounts with highest scores (90% of potential defaults are in top 20%). This minimizes collection expenses and delinquencies both.

Collection expenses have been reduced by 30% and monthly default rates have also gone down by 6% within six months of implementation.

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