A guide to loan grading – John Mould, ThinCats CEO
John Mould, CEO of ThinCats, shares his thoughts on the ins-and-outs of loan grading.
Credit scoring in the wider world is well documented, and loved and hated in equal measure. But when it comes to accurate analysis in the alternative finance industry, there is huge variability across the platforms.
Because the direct lending industry is relatively young within the finance sector, few platforms have had the chance to build up data-rich, seasoned loan books to use for developing risk prediction models. As a result, many rely on scorecards developed using information from the wider UK universe of companies, partner with credit reference agencies and use a mixture of off-the-shelf credit risk scorecards, their own metrics and human judgement.
Commercial Credit Data Sharing is expected to kick off later this year; a scheme launched in April 2016 that requires nine major banks to share credit information on all their willing SME clients, furnishing finance providers, including alternative lenders, with a wealth of current account and credit information not previously available. This is expected to provide a considerable uplift in the accuracy of credit scoring models over time, and hopefully will facilitate the alternative finance industry in serving a host of currently overlooked smaller businesses.
However, it is not as simple as just having access to information; not all grading systems predict the same event. Some are calibrated on publicly-available data, to predict formal insolvency events; others are trained to predict all forms of company closure, insolvency and dissolutions; still others are trained on proprietary customer data, including events such as late payments, not necessarily associated with insolvency, as practiced extensively by the main banks. This is the current challenge that data scientists face: building risk models that predict very specific outcomes; accurately reflecting investors’ experience of risk and return, but also affording borrowers fair and objective assessments.
ThinCats has allocated a considerable amount of time and resources to these issues, and the company is able to give UK SMEs more than just a number crunching, ‘computer says no’ experience, whilst also protecting the interests of the lenders.
As a secured lending platform, investors’ risk exposure and net returns are driven by both default risk and the ability to recover capital given a default. The ThinCats grading system makes the distinction between these two risk components, providing every loan on the platform with two grades; a number of security ‘padlocks’ and credit ‘stars’.