The number of start-ups providing consumer finance has grown substantially in recent years. The ability to assess the credit quality of applicants quickly and accurately is a key competitive advantage for these highly scalable, technology-driven platforms.
Applying a scorecard that allows for sufficient acceptance rates to grow the business while maintaining strong loan book performance is critical to attracting capital to drive growth and reach profitable scale.
Raising debt capital to reach profitable scale is often a primary constraint. Debt providers are hesitant to invest in platforms that are yet to reach critical scale and don't have a long track record of strong loan book performance. A classic chicken and egg problem.
The use of machine learning will provide greater robustness to the credit assessment process and the ability to optimise the scorecard used by these platforms to make their credit decisions earlier. It is likely this will allow these platforms to demonstrate stronger credit performance and robustness earlier in their life cycle.
This may go some way to help address the capital constraint problem in the sector and provide greater comfort to debt providers to invest at an earlier stage of development to drive growth.
Machine-learning is also good at automating financial decisions, whether assessing creditworthiness or eligibility for an insurance policy. Zest Finance has been in the business of automated credit-scoring since its founding in 2009. Earlier this year it rolled out a machine-learning underwriting tool to help lenders make credit decisions, even for people with little conventional credit-scoring information. It sifts through vast amounts of data, such as people’s payment history or how they interact with a lender’s website.