credit card recovery analytics outsourcing Gmid Associates
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To help prioritize accounts for collection efforts by identifying the ones having the highest recovery potential, thereby optimally allocating collection efforts and maximizing recoveries.
Due to economic recession and increased default rates in credit card industry, written-off portfolios are piling up every day. Banks are digging deeper into their books of written-off customers to hunt for recovery potential.
The bank wanted to have a recovery propensity scorecard on their written-off portfolio to help them run a targeted recovery campaign.
The data provided by the bank was mainly profile rich. In order to identify the customer behavior, transaction data and calling history are also needed. We started by profiling of the customers. We used past one-year data for our analysis. The bank provided us the complete charge off portfolio of last one year along with the details of the settlement cases.
Various Good/Bad scenarios were considered based on the payment propensity and settlement propensity of the entire debt pool. The final scorecard evaluated the payment propensity amongst fresh charge-offs.
Different statistical techniques were used to identify the important attributes that help in identifying the potential settlement cases. Some of the fields that came important for the predictive model were-
Using our analysis, each customer was given a score and the portfolio was divided into ten segments of customers with varying propensities to pay. The bank then designed design their recovery campaigns for each of these segments with customers in higher segments are targeted with higher intensities and optimal settlement offers. This has allowed the bank to tap 80 % of their potential customers in 20 % of the segments thus saving on costs with improved focus.
The exercise is carried on 25th of every month and the revised segments are attempted for recovery in the subsequent months.
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