Creditfix LB 03-2017
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Young Consumers Turn to Alternative Lenders

Despite traditional banks still remaining the most popular choice for consumer borrowing, more young consumers are turning to alternative lenders.

Research by financial services company Logical Glue has found that the average person has had over eight loans in the past and high street banks are still supplying 83 per cent of all loans.

However, millennials and younger consumers are increasingly turning to alternative lenders after advice from peers and online reviews.

Of the 16-35 age group 50 per cent have had loans with alternative providers, compared to just 26 per cent of those aged 45 or over. Furthermore, when quizzed about future loans, three times as many under 35s were likely to use alternative lenders than over 45s.

Much of the younger preference can perhaps be put down to the success rate of securing loans from traditional lenders. 44 per cent of under 35s have been rejected for traditional loans, compared to just 18 per cent of over 45s.

64 per cent of those younger borrowers rejected by the traditional route were then accepted by a challenger or alternative lender.

Credit cards also seem to be going out of fashion for the younger consumer. Almost half of 18-24 year-olds have never had a credit card, compared to around a third of 25-34 year-olds.

CTO at Logical Glue, Jon Poulter, commented: ‘For any lender, increasing approval rates is necessary to grow revenue. But in order for this to have a high-rate of success, the ability to accurately identify credit-worthy borrowers who are highly unlikely to default is key. Traditional scorecards are robust; however, they cannot provide marginal gains for competitive advantage.

‘They also cannot take into consideration the shifting habits of Generation Rent, who are reluctant to turn to credit cards and are therefore lacking in the traditional data to make decisions with.’

He continued: ‘This is where alternative lenders – born in the digital, data-driven era – have the advantage. They are set up to move at speed and are taking advantage of the latest machine learning techniques to utilise new types of data and spot new patterns.’

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