In the 2011 film Moneyball, we are introduced to the coach of the struggling Oakland Athletics baseball team Billy Beane. Faced with a small budget and a string of defeats, he realises – with a little help – that many player recruiting decisions are based on a host of human biases which are not relevant to the quality of the player. If these biases – age, looks, off-field behaviour, personal chemistry – could be stripped from the recruitment decision making process, there was a possibility to build a winning team on a shoestring budget.

If we look at mortgage applications today, and reasons why people are rejected, similarly (potentially) subjective biases abound. A short online search will find these reasons:

- Self-employed/lack a consistent income
- Lack of credit history
- Gaps in employment
- A mistake on the application form
- Salary level
- Loan to value
- Too many mortgage applications made

The list goes on and on. And schemes and factors which are purported to help them – Help to Buy, low interest rates, and the Stam Duty holiday – are merely serving to drive up prices further.

The open banking, “Moneyball” approach
Like Billy Beane, if we could strip out all the potential for human biases and error in the application process, and simply look at the data that matters, we would potentially be able to offer a good chunk of Generation Rent a foothold on the property ladder.

By now you are probably familiar with the concept of open banking, but just to briefly recap, the principle is that banks must share account data with third parties, under strict regulations, not the least of which is that the applicant must give their consent. With an open banking third party provider (TPP) supplying the account information, data is shared direction from the account via API, removing any chance of human error.

This is a simple concept. However, it offers a ground-breaking advantage for the mortgage lending industry, by enabling you to almost instantly build an accurate, detailed, up-to-date financial profile of your applicant, without any manual processes, and free of unstructured data and human error.

With this approach, you can zero in on the data that matters – chiefly how much money goes in and out of your applicants’ accounts – free of the potential biases, mistakes, or other errors that can creep into the decision-making process. And armed with the information that matters, you can capture a potentially significant part of the 6-8 million UK citizens who are unable to access traditional forms of credit, including self-employed entrepreneurs, lower income, fiscally responsible singles and couples, ambitious graduates, thrifty parents, and more.

Make a dent in Generation Rent
It is entirely understandable why a responsible mortgage agent would put potentially undue weight on factors including steady income, credit history, and so on. And human error in the application process is never going to disappear as long as manual work collecting documents and filling out forms is required. But these things aren’t fair for the applicant. And they are not good for your lending business.

In the 2002 baseball season, the Oakland Athletics famously went on a winning streak of 20 consecutive games after adopting their data-driven approach. If you could use open banking data to make objective decisions about your mortgage applicants, what would your statistics look like?

Learn more: https://yts.yolt.com/

Share Story: