A fast-growing US/Canadian retail micro-lending company was looking to improve lending decisions for buy now, pay later financing programs despite a lack of borrower data. With the difficulty of having to manually retrieve a key piece of data usually needed to determine eligibility, lending decisions were inefficient and costly.
Gradient Ascent developed a risk modeling system that leveraged a combination of historical time-series data paired with borrower information to predict the missing input that had previously required manual retrieval as part of the approval process. With this factor accurately forecasted, lending decisions could be made more quickly and much more profitably.
The deployment of the new risk modeling system has significantly streamlined MDG’s approval process, reducing the time and cost associated with manual data retrieval. This has enabled quicker lending decisions and has positioned MDG to capitalize on market opportunities more effectively.
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