Retail credit debt buyer (unsecured loans) using Zinia for their daily operational strategy to maximize ROI.

Highlights:

The Problem & User inputs

The retail credit debt buyer wants to know the optimal action (call/letter intensity, offer discount and send to legal etc) that maximises the net repayments on the loan, within the business constraints set by user.

Zinia will build deep neural networks for customer payment profile. Zinia then combines these models and simulates for the optimal call/letter intensity that maximises ROI. This optimisation will also tell the client when is the best time to carry out each of these actions.

Zinia has transformed the operations at this retail credit debt buyer. Previously these actions were selected using either credit/internal scores or rules.

What Zinia does automatically

First, Zinia automatically cleans and standardizes the data fields and also selects the data variables for the AI model. In this case, Zinia selected the following types of data variables (about 8 final model variables).

Product featuresOther VariablesLoan performance
Loan amountResponse to ActionsPaid in full/instalment/No Payment
Loan Origination DateDebt Type 
Default DateInternal credit score 
Other Debt InternallyPayment history 

Zinia will build deep neural networks for customer the different payment profiles. Zinia then combines these models and simulates for the optimal call/letter intensity that maximises ROI. This optimisation also tells the client when is the best time to carry out each of these actions.

Results

Zinia produces daily operational strategy (i.e. which customers to call and letter and when). This leads to an increase of 21% in total net repayments compared to the client’s previous operational strategy (which was based on propensity scoring and rules).

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