It all starts with a dream. A dream of buying that home. When customers apply for a home loan dreams take a backseat and the frustration of the home loan process sets in. Any bank’s first and foremost concern is that whether the applicant will be able to repay the loan or not. Banks need an indication of the borrower’s reliability, whether that individual will be able to repay the loan and if they will repay on time.
To make the credit assessment, banks turn to credit agencies to get FICO scores and use their underwriting rules to determine borrowers’ creditworthiness. But how effective are credit agencies and banks at gauging a person’s ability to repay? Not as effective as they could be.
Flaws in the system: A limited perspective
Credit agencies rely on backward- looking assessment methods developed decades ago that doesn’t accurately represent creditworthiness. A credit score predicated on someone’s past behavior gives some indication of a person’s ability to repay loans, but it is an incomplete picture.
Also, more than 1 in 5 consumers have a material data error in their files that makes them look riskier than they are. For example, half or more of the medical bills have errors and they get reported to the credit bureau with these errors. Even when a consumer pays off the bill to the collection agent they don’t always get reflected in the credit agencies.
If incorrect data is reported to credit agencies or an agency makes a mistake, the onus to correct it largely falls on the consumer. They must undergo a difficult process to get their record corrected, during which they may be wrongly categorized as credit unworthy. From both the bank and consumer’s perspective, credit agencies fail to do their primary job and the lending company loses out on the profitability of a creditworthy customer they wrongly rejected.
Banks on their part have credit assessment/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!
Credit agencies not only have data quality issues, but they have antiquated rules like the banks. For example, when a person switches their cell 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.
Lastly, 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 incentivizing 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 approach. 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 companies 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.
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
How can banks discover better credit assessment methods and rid themselves of the old systems’ flaws? The solution calls for an experimentation-and-fail-fast approach.
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.
Change is difficult, but it’s possible for any lender to take advantage of new technologies and approaches to overcome a culture of inertia by maintaining a customer-centric and innovative mindset at the core of its business.
Authored by Mahesh Raghavan, Senior Principal, Financial Services Digital Consulting, Infosys and Amit Lohani, Principal Consultant, Financial Services, Infosys
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The article is insightful and brings out the tendency to use historical data points to assess creditworthiness. Having worked as an underwriter in the past, and now on the technology side with Infosys; the article makes a brilliant point on how the system first promotes borrowing to create a credit history, than evaluate an entity using alternate data points.
An individual’s digital footprint and behavioral traits like not revolving on card limits, clearing utility dues through Standing orders ; backed by intelligent algorithms can be an intelligent feed to robust credit decision making.