Unsecured B2B lending business uses Zinia for underwriting decisions, improving NPV of balances lent by 20%


The Problem

The business provides loans to SME’s on a short-term basis (6 months) as a working capital solution. They used a credit risk score to do underwriting decisions prior to Zinia. The loan is repaid on a daily basis, thereby the repayment behaviour is very noisy and difficult to forecast. The credit score used by the business was not predictive enough to capture the volatile repayment behaviour. Thereby, the underwriting decision was not optimised to capture the net (of bad debt) lending opportunity available.

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 term in daysDepositsDefault/ Paid in full/Early settlement
Loan amountDaily cash 
Interest ratesIndustry 
 Internal credit score 
 Trading history 

Then, Zinia builds a set of supervised models. It builds deep neural networks for customer behaviors such as price sensitivity to take up the loan, customer default and how much they would pay back. Then Zinia combines these models to automatically produce a daily repayment forecast.

Zinia also builds an alternative KNN based forecast (semi supervised approach) and finds that the best final daily forecast was produced by blending supervised and semi supervised approaches (Neural networks & KNN)


Zinia forecast achieved 97% accuracy against actuals, which translated into an improvement of about 20% net present value for the balances lent (in comparison to their previous modelling)

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