End to ‘one size fits all’ interest rates as banks learn to pinpoint how and when borrowing goes bad

Credit card and mortgage interest rates are set to become tailored to our exact credit history and behaviour, thanks to a new model developed by Edinburgh academics that is being presented at a global gathering of credit experts in August.

The ‘intensity model’, developed at the University of Edinburgh Business School, is particularly good news for those with high credit ratings, who will see banks increasingly vying for their business with more tempting rates in the near future. It also means banks can better protect themselves from the £13.2 billion* lost to defaults each year.

Banks using the new system will be able to much more accurately predict which borrowers will miss loan repayments and when, enabling them to offer ‘tailor-made’ interest rates based on specific past borrowing behaviour and credit risk. Borrowers with poor credit scores and histories will pay higher interest while those with a good credit profile will increasingly benefit from lower interest rates as lending institutions begin to compete more vigorously on lending rates.

Banks that adopt the model will be able to better protect themselves from the £13.2 billion black hole of loans written off each year by foreseeing exactly where those write-offs are likely to come from, and when, and adjusting the capital they hold in their books accordingly.*

Professor Jonathan Crook at the University of Edinburgh Business School will be presenting the ‘intensity model’ at the Credit Scoring and Credit Control conference, held at the University at the end of August.

Professor Crook’s and Dr Mindy Leow’s model is based on a new application of statistical theory that means banks can more accurately predict when and where their customers are most likely to fall behind on payments – and go beyond that to ascertain the customers that will continue to fall further behind and default. It’s a more accurate and informative way of analysing risk, which goes beyond working out a customer’s risk of default over a 12 month period, to analysing the risk of default – or even of falling one payment behind in any given month.

The model then goes even further, and can predict these risks against different macro-economic backgrounds – meaning banks can more accurately stress-test their systems and ensure they are retaining enough capital to protect themselves, reducing the chances of getting into difficulties in the event of a future credit crunch.

Professor Jonathan Crook, Professor of Business Economics and Director of the Credit Research Centre at the University of Edinburgh Business School said: “The intensity model is designed to take an already effective system used by banks, and create the next generation of risk modelling.

“The ability to accurately predict consumer delinquency, default and catch up payments over any given period in the life of a loan, and even stress-test these against different economic conditions, is a hugely powerful tool that will benefit consumers and lenders, and help to ensure our banking system is better protected in the future – which is good for the economy as a whole. This model really could bring about a seismic shift in how banks assess credit risk – something people with poorer credit histories should start thinking about now if they don’t want to be penalised when borrowing in the near future.”

The work of Professor Crook and his colleagues at the Credit Research Centre is leading the way in the development of credit risk models. If employed across the financial sector this new model could play a significant role in helping banks and regulators take steps to avoid the build-up of excessive consumer debt, leading to difficulties for individuals and institutions alike.

The 14th bi-annual Credit Scoring and Credit Control Conference will be held at the University of Edinburgh Business School’s Credit Research Centre from the 26-28th August. Around 400 experts will attend Europe’s leading conference on the topic to discuss the latest findings and issues in the field.

* Average total loans written off annually between March 2010 and March 2015; BoE statistics.