Last week’s Asset Finance International webcast discussed forbearance and operations, and how these are being impacted by the current Covid-19 situation. Unsurprisingly, the webcast touched upon the level of uncertainty for asset finance and leasing businesses around asset residual values in these unusual times. The different perspectives as to what might happen highlighted to me the need for data and data driven decision making, to ensure that any action companies are taking right now are driven by the current needs of both their businesses and their customers.
I spent most of 2019 working on a government digital project. One of the biggest takeaways for me personally from GDS (the Government Digital Standard) for doing software delivery was that data-driven decision making was fully embedded throughout the entire process. This ranged from customer research (to understand and prioritise end customer needs) to carefully crafted KPIs for the ultimate solution, which would be baked in and measured to drive continuous improvement over time.
Right now, there is uncertainty about the future across the whole world. Specifically in our world of asset finance, this uncertainty relates to the level of impact on businesses from forbearance and increased delinquency rates, falling prices on used cars and an economic outlook which (at least short-term) will certainly include a recession.
Our decision-making, as we look to rebound and reinvent in relation to this changing world we find ourselves in, needs to be data driven. This data needs to be leveraged both in terms of ensuring we are meeting the real (rather than assumed) needs of our customers, and ensuring that any changes we make to our processes, infrastructure and resourcing, deliver demonstrable value both to them and to our businesses.
Part of the challenge right now is being able to obtain accurate data – residual values for PCP (personal contract purchase) and lease assets are important information in terms of understanding your current portfolio. With CAP effectively freezing their RV data for the time being (saying they will "not be adjusting used values while there is insufficient data"), asset finance providers may need to look to alternative data sources who use a wider range of available data from public sources in their analysis to get a better picture of where residuals are heading. Used car values are likely to be more volatile for a period, borne out by the data that is available with up to 60% of used car dealers making weekly price adjustments; making the right decisions (in real-time with up to the minute values based on the data that is available) will be more helpful than assuming nothing has changed.
Another part of the challenge is being able to leverage the data that finance houses already have, by getting analytics and predictive analytics to inform things like likely forbearance requests across the whole book, likely delinquency rates and better remarketing choices in a market where selling the vehicle may not be the best option at least in the short-term. Next best action calculations can easily be crunched through to determine whether extending, refinancing or selling will make the most margin in the circumstances, although again this really does depend on used car values which are timely, up to date and as accurate as possible.
Our Advanced Analytics Group has worked on applying real-time analytics, predictive analytics and machine learning to different industry verticals, including financial services. Their solutions have helped inform and address real business challenges and enabled businesses to understand and anticipate the actions of their customers, whether goal is to assess the current portfolio, predict future delinquency and forbearance requests, make smarter ‘next best action’ decisions on maturing contracts, or ensure that any new business being written uses the most accurate RV possible at the time.
This blog is a follow up to my colleague Adam Kobeissi’s blog on Digital Innovation, which can be found here. If you’re interested in learning more about using data to drive your decisions, or a discussion around your current uses of data, please do get in touch – email@example.com.