In the motor retail sector, motor finance is like fuel for revenue and market share. However, the ever-changing profiles of consumers and the surge in freelance employment – or gig workers – are factors that make achieving that goal more challenging.
The gig economy is burgeoning. According to Trades Union Congress data, 4.4 million people in the UK ‘gig’ weekly, a figure that tripled from 2016 to 2021. Yet the rise of the gig economy has thrown lenders a new curveball. While people have more freedom and income potential, it’s made it harder to gauge their creditworthiness.
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One big challenge is the unpredictable nature of gig workers’ incomes. Unlike traditional jobs, freelance earners often experience fluctuations in their cashflow, depending on the volume and frequency of available work. Plus, they often don’t have the usual income documents like payslips or tax returns. This makes it hard for lenders to identify if they’re a good risk. As a result, gig workers might get overlooked or only offered higher interest rates, even if they have a good and consistent income. It’s a missed opportunity in an already highly competitive market; it’s also a risk for lenders in the new world of Consumer Duty expectations.
The latest data from the Finance & Leasing Association showed consumer car finance new business volumes fell in May 2024 by 4% year-on-year. In the five months to May 2024, new business was 1% lower by value and 2% lower by volume compared with the same period in 2023. Whilst consumer confidence has improved through the first half of 2024 with falling inflation and the prospect of lower interest rates, the data suggests motor finance providers may still need to find ways to develop growth.
The ability to swiftly make informed decisions and customise loans for consumers are critical factors. It’s all about accessing the right datasets to navigate a complex landscape where risk assessment and growth strategies collide. And for motor finance underwriters, there’s a persistent struggle of sifting through a 30-page bank statement to figure out someone’s cash flow and affordability. It’s not just time-consuming; it’s a recipe for mistakes.
With advanced machine learning algorithms and categorisation engines, lenders can automate these processes with far more accuracy. And the extra time means they can free up valuable resources and enhance operational efficiency.
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By GlobalDataOpen banking potential
For some time now, it’s been clear that asset-based lenders need real-time income/cash flow data to optimise credit decisions. Specifically for vehicle lenders, it entails access to transactional bank statement data through a secure open banking platform to help create tailored offers and make faster decisions.
To thrive, win and retain new business, motor finance providers know they must embrace new technology, seize the opportunities presented by alternate datasets and feed that data into predictive models to underpin decisions. The integration of real-time income/cash flow data into decision-making not only provides lenders with deeper insights into their customers’ financial behaviours; it also helps open doors to a more inclusive and fair lending landscape.
In April 2024, credit risk service provider Atto partnered with global analytics firm FICO to help UK lenders integrate Open Banking data into their credit scoring processes.
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