KEY POINTS

  • Traditional consumer lenders, like banks and credit unions, have historically served segments of the population they can conduct robust risk assessments on. But the data they collect from these groups is limited and typically impossible to analyze in real time, preventing them from confirming the accuracy of their assessments, confining the demographic segments they're capable of serving, and creating a lengthy, often inconvenient process for potential borrowers.
  • This has hobbled legacy lenders at a time when alternative lending firms — which pride themselves on precision risk assessment and financial inclusion — are taking off. Alternative lenders are just starting to break into the untapped borrower market, which, even in developed regions, is huge — some 64 million US consumers don’t have a conventional FICO score, and 10 million of those are prime or near-prime consumers.
  • Alternative lenders are disrupting the credit scoring space in two key ways: by using alternate credit scoring methods, like psychometric scoring, which use data besides borrowing history to measure creditworthiness, and by integrating new technologies, like artificial intelligence (AI), to improve the accuracy of conventional risk assessment methods.
  • There's a range of methods and technologies incumbent lenders can choose to implement. But the solutions that are best suited for a particular lender will vary based on its specific business needs, the demographics it aims to attract, and its jurisdiction's regulatory landscape.
  • If executed correctly, the payoff can be huge for incumbent lenders. In addition to boosting financial inclusion and enabling lenders to tap into new demographic segments and markets, new methods and technologies can improve returns on existing demographics.
  • Despite this massive opportunity, disruptions carry both short- and long-term risks that both fintechs and incumbent lenders must navigate. These include inbuilt biases, fraud, conflict with third-party data policies, and poor financial literacy among underserved demographics.

Introduction

Traditional consumer lenders, like banks and credit unions, have historically served individuals with credit histories and credit scores — in other words, segments of the population they can conduct robust risk assessments on. But the data they collect from these groups is limited and typically impossible to analyze in real time, preventing them from confirming the accuracy of their assessments, confining the demographic segments they're capable of serving, and creating a lengthy, often inconvenient process for potential borrowers. This [...]