Instant was building a fee-free, earned-wage payments program for hourly-wage workers who may need access to additional capital before their next paycheck. The client was looking for an innovative solution for modeling risk and making credit worthiness assessments that could help them make smart lending decisions for these hourly-wage workers, many of whom may not have established credit histories.
Working with limited data, Gradient Ascent applied an innovative feature engineering approach to develop a new risk prediction model that enabled more efficient lending decisions with minimal data provided by borrowers all while identifying and reducing biases in this creditworthiness assessment process.
The implementation of Gradient Ascent’s innovative risk prediction model has dramatically improved Instant Financial’s lending efficiency. Since the deployment, Instant Financial has observed a marked increase in the accuracy of credit assessments for hourly-wage workers, enabling more informed and equitable lending decisions. This has not only enhanced the financial inclusivity for workers without established credit histories but also reduced the operational risks associated with unsecured lending.
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