Recovery Analytics services help organizations identify accounts with the highest potential of repayment, improves recoveries, reduces bad debts and write-offs, optimizes collections and improves retention and customer profitability.

Market Equations helps organizationsprioritize and optimize their recovery efforts based on the propensity of repayment of select accounts by identifying customers who have a higher propensity of repayment and targeting them with innovative and tailor made recovery strategies.

Our Centre of Excellence in India combines advanced analytics expertise and decades of consulting experience to help organizations in the Banking, Financial Services and Telecom industry develop innovative and targeted recovery strategies to increase recoveries, maximize customer retention and profitability.

Market Equations helps a leading Bank develop a recovery propensity scorecard on their written-off portfolio by identifying accounts which have the highest propensity of repayment, thereby helping them optimize their collection efforts, maximize retention and recoveries.

Objective:

• Identify and prioritize accounts that have the highest recovery potential
• Improve credit collections, optimize settlements and eliminate write offs
• Intensity optimization using statistically chosen treatment channels on targeted accounts

Customer Recovery Analytics outsourcing services from Market Equations India helps organizations identify accounts with the highest potential of repayment, improves recoveries, reduces bad debts and write-offs, optimizes collections and improves retention and customer profitability.

Approach:

Data Audit: The data provided primarily consisted of customer profile attributes. Identifying and predicting customer behavior required additional inputs including transactional data and historical calling data. We used 12 months of data to perform the data sanity check and start our analysis. The bank provided the complete written off portfolios for the one year period along with the details of the settlement cases.

Data Analysis: The analysis started with the customer profiling exercise. Once the profiling was compete 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 and scores were aligned based on the following:

• Good: Success defined by the event of any payment within the next 6 months of charge-off
• Indeterminate: If the account makes payment less than the fixed amount
• Bad: No payment

Model: Statistical techniques were used to identify the key attributes that help in identifying the potential settlement cases. Some of the important fields for the model were:

• Number of times the account has been in Bkt5 for the last 6 months
• Account age at charge-off
• Difference between last purchase and last payment
• Time since last payment

Outcome:

Market Equations helped the client optimize their recovery efforts, minimizing collection costs and maximizing settlements by targeting the customers in higher segments with higher intensities through optimal settlement offers. The scores were evaluated in the last week of every month and used for the next months recovery efforts. The company was able to reach the recovery target by collecting 40 per cent more recoveries than anticipated at the beginning of the exercise.