AI offers advantages to both lenders and borrowers, offering speedy, accurate and convenient transactions.

Artificial intelligence (AI) is changing the way lenders assess creditworthiness. Instead of the usual credit check, which requires borrowers to have an actual credit history, lenders can input material such as a borrower’s mobile phone and utility bills. These can prove a borrower’s on-time payment history. So can credit card statements, which show how much and how often a borrower spends money. Both are useful in determining a borrower’s creditworthiness.

AI also gives lenders more opportunities. It is faster and more accurate. And when AI is properly programmed, it is immune to bias. By analyzing nontraditional data about borrowers, the computer program can nearly instantly assess that person’s ability to repay any loan. Additionally, if the borrower in question does not meet the criteria currently, AI can track the borrower’s information and notify the lender when the borrower does meet those criteria.

Loan Application Scoring
Traditional credit scores only consider a borrower’s past ability to pay back what had been borrowed; they do not take into account a borrower’s present ability to pay back owed money. For example, a person with poor credit could experience a sudden windfall from any number of sources and be perfectly capable of making monthly payments even if that person’s credit history were poor.

Conversely, someone with stellar credit could suddenly be without income or the prospect of acquiring an income and be completely unable to make monthly payments. In either case, AI can accurately assess each person’s monetary situation.

Lending software has come a long way since its inception. The newest lending software takes not only borrower-provided information into account but also the person’s social media presence and other attributes that indicate creditworthiness or unworthiness. The process can also help people who have no credit history secure a loan.

The best part about using AI for credit evaluation is that it saves time and mountains of paperwork. Results can be nearly instantaneous instead of taking weeks.

Churn Prediction
Churn itself is just when people come and go, no matter the business or kind of relationship they have with that business. Churn can torpedo a business in short order if it’s not controlled. Even for those who are experts in attracting customers, churn is a problem if more people leave than show up.

AI is a terrific tool for controlling churn. It can analyze gigantic chunks of raw data very quickly, and it can be reprogrammed to accommodate changing trends within the industry. Most AI is equally good at finding short-term and long-term solutions, based upon the data analysis parameters the programmers set.

AI can be adept at spotting customers who are likely to churn based upon the behavior of previous customers who have either left the company for good or returned at a later date. AI can recommend appropriate courses of action based upon its analyses.

Of course, borrowing money is seldom a short-term prospect, unless it involves payday loans or other similar products. When it comes to lending, churn occurs at 10-year, 15-year or even longer intervals. Still, lending software must predict these outcomes accurately for the lender to remain on solid ground.

Risk Management
Even the savviest of all lenders cannot anticipate every trend, accurately forecast every
possibility or plan for every contingency. The 21st-century world of business is too fast-paced to be able to do that with any frequency. AI, however, with its ability to store everything it’s learned, can perform the risk assessment for any business. And, when it comes to lending, risk assessment is perhaps the most important thing.

Risk management, particularly when measured against ROI, is at the core of any lending relationship. Under traditional lending practices, people without credit scores, with poor credit scores or in similar situations would automatically be poor risks. They would not be able to gain access to credit.

With AI, however, it’s possible to extend credit to these people, many of whom are good risks despite their lack of appropriate credit. Better still, by lending to these people, a business will help them build credit. That, in turn, will likely build a good relationship between the customer and the lender, reducing churn and creating a long-term relationship.

The big advantage of AI over human prognostication is that it can work with data that wouldn’t appear on a normal spreadsheet. It’s a whiz at extrapolation, which is why it’s

so useful in situations where the borrower has little or no credit and must rely on other data to prove worthiness.

AI is also capable of learning from AI-human interaction. For example, if the AI deems a customer a poor risk when the customer truly is not a poor risk, then a human being could later input data proving why the person was actually a good risk. The AI could take that correction and learn from it. In the future, then, it would not name others in similar circumstances as poor risks.

Credit Insights
Unlike marketing in today’s business world, which involves segments, demographics and other groups, lenders must instead focus on each individual separately because every customer will be different. That means AI has to be able to process lending applications based on an individual’s data and not on the data of any group to which that individual belongs.

These data include a borrower’s:

• Past lending relationships.
• Current lending relationships.
• Payment history in these relationships.
• Income
• Debt-to-income ratio.
• Past adverse credit events.
• Ability to repay loans irrespective of traditional creditworthiness.

To be effective with these data, lenders must consolidate their channels. For example, the “behind-the-desk” lending officers must be able to access the same data as the people in the lender’s call center. Instead of being competitive, the channels must work together.

Lenders also must be able to interact with borrowers, prospective or otherwise, in real time. They must know the right time to extend a lending offer. As an example, a borrower wants a loan in August but does not meet any criteria. It would not pay to extend an offer to that borrower. If, however, the borrower is good to go in October, that would be the right time to reach out. AI can monitor that borrower’s information until the right time and then notify the lender.

AI lets lenders make all processes customer centric. Customers prefer speedy, accurate and convenient transactions. AI provides them with all three.

Lending has moved beyond stuffy offices, power suits and three briefcases of paper. Through AI, loan approval can be instantaneous. AI also allows people who would previously be poor risks to obtain credit and contribute to their local and national economies.

AI learns on the go, too, so it gets better over time at performing these tasks.