RATESETTER’S upgraded data disclosure on its loan book and expected losses has brought its transparency to a top-tier level, according to peer-to-peer lending research firm 4th Way.
The ‘big three’ P2P platform, which recently announced it was upgrading its expected losses methodology with a new dedicated committee and quarterly reporting, published new figures incorporating the changes on Friday, unveiling lower forecast losses and a higher coverage ratio.
Based on the amended methodology, expected cumulative losses now stand at £18.06m, which paired with the current £22.44m provision fund buffer result in a 124 per cent coverage ratio – six per cent higher than last reported.
The firm now calculates separate figures for loans older than nine months and for loans originated less than nine months earlier, grouping the latter based on features such as average score, size, annual percentage rate and early delinquency rates.
Moreover, it will segment loans based on risk grade, term, channel and origination date rather than just on risk levels.
To match the more detailed future loss methodology, the platform also injected a slate of new credit data onto its public performance statistics page – placing itself at the top of the P2P class for transparency, 4th Way said.
Thanks to the sheer number of indicators that its performance table now singles out, “RateSetter now offers top-tier disclosure,” said Neil Faulkner from the P2P comparison website.
“In terms of detailed aggregated statistics on the platforms’ own websites, it is now joint-top with FundingKnight and Bondora. All three offer a lot of details.”
RateSetter’s data table now provides a clear estimate of the losses expected over the lifetime of its loan book, spelling out the losses that have already materialised and future expected losses for each year of origination, as well as a detailed breakdown of different types of arrears, provision fund adequacy levels, and investors’ expected returns.
However, Faulkner pointed out that platforms’ data disclosure should be gauged for how useful it is to customers rather than on a mere quantitative basis.
“It’s difficult to assess which of these [credit tables] is the most comprehensive, because you can’t just count the number of columns in the loan book,” he said.
“The level of usefulness is subjective and varies depending on what you want to achieve by checking the data.”
For example, RateSetter’s statistics on property development loans and their underlying securities would be less accurate than those offered by property-specialist platforms such as Landbay, Faulkner said.