Technology
How A.I. supports lending growth and minimises bad-debt
Published by Gbaf News
Posted on April 9, 2020
6 min readLast updated: January 21, 2026
Global Banking and Finance Review
Company
Published by Gbaf News
Posted on April 9, 2020
6 min readLast updated: January 21, 2026
By Andy Moffat, Director, Aire
What if I told you that U.K. lenders declined nearly 2 million consumer credit applications last year due to a lack of information about the applicant, rather than poor credit histories or genuine affordability concerns?
It’s a startling statistic. Particularly at a time when it is getting harder for those in the consumer-lending sector to grow credit portfolios sustainably. The outbreak of COVID-19 (Coronavirus) could well cause the rate of consumer-lending growth, which has been declining since 2016, to go negative by the middle of this year, as prudent consumers hunker down and wait for better news.
For a few months at least, this may be inevitable, and it is right that lenders proceed with great caution around the type of consumer lending that gets written, and to whom. Longer-term, however, growth has to come from somewhere, and lenders can’t afford to standstill when it comes to keeping low-cost mainstream consumer credit flowing.
Recent developments in Artificial Intelligence (A.I.) can support this by improving how lenders serve their existing customers, and opening up new opportunities to lend to customers previously excluded from mainstream finance due to the limitations of how most credit applications are assessed today.
By our calculations, four in every five consumer-credit applications rejected by U.K. lenders in 2019 could have been accepted, without increasing their appetite for risk or burdening consumers with more debt than they could afford.
Many of the excluded at what the UK’s Financial Inclusion Commissioner calls ‘non-standard’ customers. They are people whose lack of borrowing in the six-year window seen by lenders through traditional credit reference agencies counts against them, or whose lifestyles and patterns of income are not compatible with older credit scoring methodologies.
There are 5.8 million ‘thin-file’ consumers in the UK alone that would struggle to obtain credit because they are invisible to the financial system. Thin-file consumers cannot access mainstream financial services because they do not have a credit history, or because any prior borrowing they had ended before the six-year window lenders are allowed to see.
Others are turned down marginally on often-arbitrary affordability measures because traditional data sources do not provide enough information to allow lenders to make decisions with confidence.
Many are deserving applicants: young professionals in good jobs, dynamic migrants wanting to start businesses or reliable older people who have long-since paid off their mortgage.
A.I. provides lenders with an opportunity to understand and serve these people sustainably and profitably, as well as millions more living 21st-century lifestyles, such as gig workers and freelancers.
Using A.I. to make better decisions and ensure fairer access to credit
Traditional credit-data sources look backwards. This historical performance information works well in ‘normal’ economic conditions when lenders are dealing with people who have made extensive use of credit previously. But it falls short when assessing applicants without a track record of recent borrowing.
It also struggles when economies take a turn for the worse. Faced with troubling economic conditions, credit providers need to take stock of their lending criteria, comprehend what is happening within their portfolios, and ensure they can make decisions based on the most current view possible of the consumer.
Aire estimates that, in the UK alone, well over a million more applications for mainstream consumer credit products could be accepted if lenders used A.I. to assess the real-time financial situation of consumers – at the point of application. The collection of this information, gathered directly from the consumer and known as first-party data, offers up important information for lenders and offers them greater decision-making power during the credit process.
This would give lenders an accurate and completely up-to-date picture of applicants’ circumstances and ability to repay credit. We’ve seen lenders using first-party data increase acceptance rates by up to 14%, without increasing their default rates or appetite for risk.
First-party data augments the view of the consumer that lenders get from traditional credit reference agencies. Instead of rejecting applications when there is no historical data on which to base an assessment, lenders can use a simple online interview process to gather up-to-date financial, lifestyle, and career information directly from the consumer.
A.I. plays a critical role at this stage in the process, interrogating the first-party data provided by the applicant for signs of first-party misbehaviour. Sophisticated machine learning algorithms validate the responses for accuracy and use the information to calculate credit risk and affordability scores that inform decision-making.
Through this use of A.I., we can predict how someone will perform on future commitments with just as much accuracy as the traditional credit risk model,allowing automated lending decisions for everyone, in real-time.
Supporting customers experiencing financial stress with A.I.
The use of A.I. and first-party data is also highly beneficial when consumers experience difficulties in making credit payments.
The majority of people who fall into delinquency can be rehabilitated by lenders that show understanding and forbearance. But this requires an up-to-date view of the consumer, and an assessment of their intention and capacity to pay outstanding credit – something that traditional credit or Open Banking data fails to do with any great accuracy
A.I. allows this to happen at scale without the need for a lengthy and intrusive phone call. This will be increasingly important in the current economic climate. The coronavirus crisis will test the limits of lenders’ ability to understand what is happening in their loan portfolios, as well as their ability to support customers falling behind on their credit payments.
In the immediate future, depleted workforces will add to the operational challenges faced by lenders, as they struggle to cope with resourcing their teams just as much as serving their customers.
This sensitive approach supports compliance with the FCA’s Treating Customers Fairly obligations. It is proven to deliver a 25% improvement in resolution time for outstanding credit, with no increase in debt referrals.
There are many opportunities for A.I. to improve how lenders understand and engage with customers – and these benefits stretch across the customer lifecycle. By enhancing the available insight and the accuracy of lending decisions, this exciting technology can support lenders’ growth ambitions by enabling them to serve millions of customers previously excluded from the mainstream credit market to provide credit where it’s due.
At the other end of the credit lifecycle, it reduces the value of bad debt in the portfolio and the volume of cases referred to Debt Collection Agencies and therefore delivering commercial benefit to lenders as well as fairer, kinder treatment for their customers.
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