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The Future of Credit Worthiness Historically credit worthiness has been determined on a set of discretionary and sometimes nebulous criteria that help lenders determine the risk of a borrower defaulting on a loan. These models have traditionally been based on statistics that make general assumptions about borrowers. Data openness and advances in analytics are enabling fintech companies to devise more sophisticated and accurate methods to assess an individual or a business credit worthiness, creating greater transparency for lenders and borrowers alike. By Chris Crespo
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ou are being judged. Whether you like it or not, if you use financial products, you are regularly judged and scored by agencies and financial institutions on a set of discretionary criteria that defines whether you or your business is deemed worthy of financial services like a line of credit, a mortgage, or a personal loan. Credit worthiness is typically assessed on five areas of financial activity: 1. Length of credit history 2. Amount of new credit available through credit cards or other financial products 3. Different types of credit owned, for example personal loans, car loans or mortgages 4. Past consistency in meeting payment obligations 5. Previous use of available credit The idea behind a system that judges our credit worthiness is that our past financial behaviour is the best predictor of how we are likely to behave in the future, and this helps banks and other organizations, determine how much risk people, businesses and governments represent as a trading partner or a debt-
or. However, credit worthiness models are outmoded, based on gross generalizations and not entirely accurate. A perfect storm for disruption.
ments on credit worthiness, reducing the incidence of non-performing loans, and helping customers make the right choices on their use of financial products.
It’s all in the data Fintech companies have been able to deploy their technological prowess to develop new digital business models that run on data. It is the extraction, crunching and transfer of data that give Fintechs and edge and enable them to create delightfully seamless experiences for customers. But within the data are often hidden patterns of behavior that reveal much about an individual or a business purchasing habits, preferences and relationships to other businesses and institutions. By combining multiple sources of data, Fintech companies are now able to identify broader patterns that more accurately predict our likelihood to honor our financial commitments. How long we’ve been with our mobile phone provider, how often we’ve been late on paying our bills and whether we overindulge in pizza at the end of the month are all likely to show up in our bank’s data. When aggregated into a higher fidelity score, banks and other financial organizations can make better judge-
Good for lenders and borrowers Danish fintech Subaio was founded in 2016 with the aim to empower bank customers to identify and manage recurrent payments. An unidentified charge in the founder’s bank statement, led him and his founding team to develop an entire solution optimized to easily find and cancel recurring payments originating from unwanted subscriptions. What Subaio have come to realize is that by finding the recurring payment data, they are also able to document exactly how much money a customer earns and spends, shedding more light into how much a customer should be able to get in a credit line. “By building a credit worthiness assessment on the actual transactions, instead of statistics, which is what has been done historically, banks can give loans to people who have the ability to pay them back” says Soren Nielsen, COO at Subaio, “and that also goes for buy-now-pay-later companies, leasing companies, consumer finance companies and others” he adds.
Kim Lundberg Country Manager, Ark Denmark
