By Mahesh Raghavan, Senior Principle – Financial Services Digital Consulting, Infosys 

Modernising credit assessment for mortgages– Finding real insights to create opportunity

Between the dream of home ownership and reality, too often comes frustration. After customers find the house of their dreams, the reality of the mortgage process becomes evident. That process is rigorous and often painful. While the level of scrutiny is understandable – lenders must know borrowers can repay – the data banks turn to can be significantly flawed. These flaws are more glaring than one may realise, and significantly undercut home ownership and revenue opportunities.

Flaws in the system: A limited perspective

Credit agencies rely on backward-looking assessment methods developed decades ago that don’t truly represent creditworthiness. A credit score predicated on someone’s past behaviour gives some indication of the ability to repay loans, but it is an incomplete view.

Also, more than 1 in 5 consumers have a material data error in their files that makes them look riskier than they are.

If incorrect data is reported to credit agencies or an agency makes a mistake, the responsibility to correct it falls largely on the consumer. They must undergo a difficult process to get their record corrected, during which they may be wrongly flagged as credit unworthy. From both the bank and consumer’s perspective, credit agencies fail to do their primary job and the lending institution loses out on the profitability of a creditworthy customer they wrongly rejected.

Banks, on their part, have credit assessment and underwriting rules that do not take into account consumers’ reality and changing lifestyles. Banks have a lot of data about customers, yet they don’t know their customers!

A fitting example of the banks’ shortcoming is the case of Ben Bernanke. Bernanke served as Chairman of the Federal Reserve until 2014, after which he took a position with a leading think tank in Capitol Hill.

He applied for a home refinance only to be shockingly rejected – a downright absurd outcome considering Bernanke made $250,000 per speech and had recently signed a multimillion-dollar book deal. Did the banks know their customer or did their underwriting rules reflect the reality of Bernanke’s creditworthiness?

Credit agencies not only have data quality issues, but they have antiquated rules like the banks. For example, when a person switches their mobile phone provider, the new provider performs a credit check. The credit agencies record this as a hard credit check and, in response, reduce that individual’s FICO score. From a practical standpoint, a customer switching to a service provider that saves them $25 a month is better off from a disposable income and creditworthiness perspective, yet their credit score indicates the opposite.

Finally, the process of building a credit score is fundamentally flawed. A person must borrow to build a credit score, whether they need the money or not. Encouraging people to become debtors has the unintended consequence of incentivising the kind of poor financial behaviour that can lead to a debt-ridden society.

At the cutting edge

Currently, few lenders have taken steps to address these problems, but there are solutions for those who look towards the future.

Certain lenders in the mortgage and student loan space have embraced a more well-rounded methodology. Rather than relying solely on the backward-looking FICO score from credit agencies, they take into account a borrower’s future potential. They consider the person’s educational background, including what and where they studied, their profession, their current employer and more, to form a holistic picture of that person’s financial health and their earning potential.

Another company that is proactively addressing the problem of creditworthiness uses lifestyle data, in addition to financial, professional and educational information, to predict a borrower’s ability to repay loans.

Other institutions use analytics to perform better credit assessments and to debunk long-held myths, such as the belief that if a subprime borrower owns a house and pays a mortgage, they are more stable than a subprime borrower who rents. Through empirical data analysis, lenders have shown that myth to be false. With new insights gained from analytics and machine learning, they can make more informed decisions when evaluating loan applicants.

Regaining social metrics – Putting community into community banking

Banking has existed far longer than credit agencies. How, then, did they gauge a borrower’s worthiness?

Back in the days when a bank served only its local community, lenders examined a borrower’s financial situation and also considered that person’s social reputation. For a long time, after towns grew and banks went national, we lost that social-reputation metric, but with innovative uses of new technology, lenders can regain a community-based approach.

Certain FinTech companies apply similar principles to their lending decisions through the use of non-traditional data to create social contracts. By developing a social footprint of the borrower based on their connections, social media,browsing history, geo-location and other smartphone data, they’re able to bring a social element into a borrower’s profile to create a more complete assessment.

Exploring innovative new solutions

One thing is clear: banks can do better. It is possible to significantly improve credit assessment and rid the old system of its flaws. The solution calls for an experimentation-and-fail-fastapproach. At the cutting edge, one Infosys client has set up an initiative using non-traditional data in its credit assessment to analyse each data point’s impact in predicting creditworthiness.

Incredible progress is being made in this space, but when it comes to change, most banks remain bogged down by inertia. Banks can break this by adopting design thinking and lean on start-up led approaches to experiment quickly, fail fast and learn faster, as they explore how to improve their credit assessment.

It is never easy to change. However, new technologies present possibilities for any lender to do things better. To overcome the inertia of how things have been done and look to how they should be done – for the customer, and for the business.