Never has the issue of economic inequality in America been more prominent. The coronavirus pandemic and growing social unrest have exposed the fragile financial health of our country’s low-income population.
Consider hourly workers, who face real economic peril because of a diminished earning capacity. According to Yahoo! Finance, the federal minimum wage has remained flat at $7.25 per hour $15,080 a year for the past eleven years. Yet, the 1968 minimum wage of $1.60 equals $11.79 per hour or $24,523 per year in in 2020 dollars. Editor Andrew Serwer pointed out that means minimum wage earning Americans are actually taking home 38% less today than they did 52 years ago.
Creating a truly cashless society: why we need open banking to get payments networks up to scratch.
This math is at the root of a very basic truth: opportunity is not distributed equally in America.
The Federal Deposit Insurance Corporation (FDIC) says that 63 million people – or about one in four adults in the US – struggle with access to mainstream financial products that meet their needs. That’s one reason why 12 million Americans use payday loans at average interest rates in excess of 300% annually. It’s expensive to be poor in America.
The age-old excuse for payday and predatory loans has been that they provide credit for people who could never find it elsewhere. That is simply not true. There are a number of mission-driven companies using technology for many types of lending to offer responsible and affordable credit at scale – even to those borrowers with no traditional credit history.
One critical ingredient of financial health for many hourly workers is the ability to affordably finance transportion. But according to the Financial Health Network, underserved consumers spent $48 billion in 2018 on interest and fees charged by subprime and “Buy Here, Pay Here” (BHPH) auto lenders.
While all subprime auto lenders should be examining their lending practices and terms, BHPH has been the more egregious offender, imposing predatory terms on consumers who struggle to get by and have very limited options. Based on the 2019 National Independent Automobile Dealers Association (NIADA) Used Car Industry Report and benchmark data from the National Association of Buy Here-Pay Here Dealers, a consumer of an average BHPH transaction would pay a total of approximately $18,000 for a car which cost the dealer just over $6,000.
At the heart of this vicious cycle of credit and debt is a flawed underwriting model. For the 63 million Americans unable to access mainstream credit, a FICO-based model won’t work. Its traditional scorecard methodology cannot generate sufficient segmentation power for people below a 530, leaving credit invisibles and those with damaged credit history at the mercy of predatory lenders.
However, advances in decision science and risk models through artificial intelligence (AI) and machine learning have now made it possible to accurately segement borrowers below this threshold into clear risk categories. Using non-conventional attributes at scale, AI identifies patterns among hundreds of variables and their relationship to loan defaults.
These models continually improve, optimising with each additional loan origination, payment, or delinquency. Leveraging the power of deep learning, lenders can lower interest rates and improve their financing terms overall.
For example, Tricolor’s AI powered underwriting models have helped us accurately assess credit worthiness of credit invisible Hispanics in order to offer interest rates some 35—60% lower than competitors and on higher quality used autos. With this use of AI, our customers typically double the NIADA models, purchasing a vehicle valued at $12,000 for the same $18,000 in total payments.
For customers, this results in a more affordable loan, a more reliable vehicle, and a stronger foundation on the path to mainstream finance. But this is not unique to BHPH and subprime auto. Lenders of all types and sizes can have the same impact by placing the customer at the center of the transaction, embracing technology, and reporting positive credit performance to mainstream credit bureaus.
In this way, lenders can transform decades only predatory models and make access to credit more fairly and affordably distributed. In doing so, our industry can improve business performance while addressing America’s underlying financial inequality. Responsible lending is both our shared opportunity and responsibility.
Source: https://www.fintechfutures.com/2020/08/responsible-subprime-auto-lending-through-ai/
