In the era of AI, many fields of our digital lives are undergoing transformation, driven by rapid processing and automation. Customer service, consulting, language learning, accounting, social media, and many more – all have more and more AI tools integrated to help users access the services they require faster and more conveniently. Financial tools are also affected by this shift, affording better opportunities in terms of credit, loans, investments, and managing bank accounts. Borrowing is an aspect greatly influenced by the application of AI, since a user can be presented with a real-time selection of options available to them, adjusted by their capabilities automatically.
Another avenue for improving and adjusting a given field, particularly the lending market, is alternative data usage. This refers to information outside of official traditional metrics like credit scoring and bank histories. Key sources of alternative data include transactional data (cash flow, digital wallet or CC use), recurring payments (for rent or utilities), employment and income data (gig jobs), and even behavioral metadata (app usage, online activity). The combination of those helps assess one’s ability to repay the loan based on their actual situation, rather than a fixed set of parameters reviewed with traditional financial tools.
How Does AI Help Improve Borrowing?
Lending is becoming more convenient with AI tools. One of the possibilities AI provides is real-time analytics tailored to the client’s needs. For example, it can scan for all offers with indicated rates and present a variety of loan types. Then, it can help the client assess their own income and assets and offer the best strategies for taking out loans and paying them back. This kind of assistance is done by calculations against a slew of data, not only of this particular user, but gathered from across the internet and the financial sector.
AI is also used in assessing the actual financial situation of a borrower. While a credit score tells some things, the current income, expenses, and possibilities are left out of the picture. With AI analysis, the financial circumstances of a person can be evaluated blindly, based on the raw data. It also helps integrate alternative data, which is in and of itself a great lens for gauging what the client’s situation is.
AI-Driven Credit Assessment as a Means for Inclusion
More and more lending companies harness the power of AI to improve their own odds at finding clients and offering them a wider range of possibilities. But AI-powered loan assessment is also beneficial to the public because it allows more people of different backgrounds to apply for loans, while traditional banking would flat-out exclude a large portion of the population.
We let Shania Brenson, CEO of 15M Finance, explain what it entails:
“Historically, marginalized communities were not only deprived of better jobs and home purchase opportunities, but shunned away from “prestigious” bank offers. Even without active prejudice on the side of the banks, African Americans and Brown people tend to have lower credit scores, not only because they had more problems repaying the money owed, but also because they were oftentimes not approved for loans in the first place to build any credit score.
Then, after the 2008 crisis, the banks that were famously bailed out tightened their restrictions even harder. While they were correct in being cautious for their own good, it excluded even more people. Since the advent of the remote work environment and the prevalence of the gig economy, some people can earn a living just fine but are severed from traditional institutions and cannot easily obtain the necessary documents to apply at a large bank. Small businesses especially need credit to function, but they are also “off the grid” for most banks. Banks tend to invest in Fortune 500 and work with successful companies, which deprives young specialists and aspiring entrepreneurs of getting the money necessary to jumpstart their careers.
Now, modern lending companies found this niche (or rather, a large section of society left unattended), and AI-driven tools paved the way to include people in financial services. Some opponents of AI claim that it drives people like artists, writers, or accountants out of business, and it may be true. But in this case, it provides the possibility to actually “meet” a person applying for a loan, judge them for what they are in terms of work and sustainable income, rather than refuse them based on an outdated statistic imbued with a history of prejudice and favoring those already wealthy. This is the tide that rises all boats at least slightly, and we hope it will help our clients reach their dreams”.
Alternative Data Contributing to Affordable Lending
A traditional credit score is a tool most usable for banks and lenders themselves. What it highlights is whether a potential client is for sure reliable in terms of repayments, while erring on the side of caution. So, it sets a very rough “floor” required to get another loan, without much concern for the actual situation a person is in. Meaning, it not only prohibits a person with a bad credit score from getting new loans, but also deprives the lending institution of that very client. For large established banks and hedge funds, it might be just fine to “avoid” these clients and settle with a formal floor for accepting clients, since they have lots of operations and are too big to fail in some cases.
For other lending businesses, though, a thorough assessment of potential clients using their alternative data is really helpful. A person may well have a lower credit score from a bad loan they took out and have gone overdue while being a student – still, they might have a good, stable salary and near-perfect financial habits nowadays. Gathering individual data on income and expenses helps see precisely that a potential client is actually a good one, but with a bad credit score, they were not able to fix it yet.
Thus, both AI in its many applications, and applying alternative data sources, help improve borrowing possibilities for many people, reaching those who would be excluded from the framework of traditional loans and credit.
