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Assessing non-mainstream borrower credit risk needs more sophistication – Cheetham

by: Stuart Cheetham, chief executive, M:QUBE
  • 07/02/2020
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Assessing non-mainstream borrower credit risk needs more sophistication – Cheetham
Data and artificial intelligence (AI) have revolutionised the way many industries work, from manufacturing to advertising and even SME loans.

 

Yet, the mortgage industry has been largely unable to benefit in the same way, with lenders typically relying on legacy systems and sub-optimal processes.

This is particularly surprising given the challenging market conditions, fuelling ongoing margin pressure for lenders.

 

Profitable customers seen as riskier

Low interest rates and intense competition between lenders led to some names, such as Tesco Bank and Magellan Homeloans, closing their operations last year.

Opportunities for growth may seem limited but opening up profitable customer segments – such as first-time buyers – is one way forward.

However, many of these customer segments are seen as inherently riskier and often a decision in principle is made on applicants based on limited information, such as three months of bank statements, payslips and a driving licence.

Relying on these narrow sources of information is not an effective method of gaining a true view on affordability.

Particularly if the applicant is non-mainstream and does not have a regular income. And looking at the stats, it’s clear that the non-mainstream is fast becoming more mainstream.

The number of self-employed people increased from 3.3 million in 2001 to 4.95m in 2019, according to figures from the Office for National Statistics.

It is time for the industry to evolve and look to more sophisticated methods to assess the credit risk of these groups.

 

The end game: Automation

Tech-enabled lending platforms provide the perfect opportunity to open up new profitable customer segments.

Innovations, such as Open Banking, help lenders to source more data on mortgage applicants.

Using advanced statistical analysis, this can be assessed in conjunction with other information to better determine both an applicant’s intent and ability to repay.

Data can be analysed to make decisions in real-time, representing full automation of the lending process, rather than just digitisation.

For the consumer, this means a legally binding offer can be granted in as little as 15 minutes, affording them the same purchasing power as a cash buyer.

For lenders, it opens up new customer segments while reducing the time and cost of processing applications.

 

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