Collection Analytics

by Jul 28, 2016Financial Services, Success Stories

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.

success-finance

 

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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