Creditworthiness has a lot of baggage in the financial world. As a gauge of how likely you’ll pay your debts on time, it represents a lender’s willingness to give you a cash advance, installment loan, or line of credit.
So, what makes a person creditworthy? Traditionally, you’d find the answer in your credit file. Many of the biggest banks still use credit scores and credit reports to assess creditworthiness today.
But nowadays, the way lenders assess risk and assign creditworthiness is changing. New FinTech services rely on alternative data and Artificial Intelligence to revolutionize the underwriting process so that they’re making data-driven decisions that go beyond credit scores.
This may sound complicated, but to the average borrower, it can be a welcome change. It means you can fill out an application to borrow money fast online direct from a financial institution’s website, even if you have bad credit.
What Traditional Factors Affect Your Creditworthiness?
When a lender checks your creditworthiness, they’re looking for indicators that you can reasonably afford their loan under the terms. They do this primarily by running a credit check to see your past borrowing history.
Your credit report details your current loans and lines of credit. It also contains information from any account you’ve closed in the past seven to ten years.
A look at this report will show your credit utilization and payment history habits. Broadly speaking, a record of paying bills on time and keeping low balances on revolving accounts (like credit cards and lines of credit) reflect well on your future borrowing abilities. A record like this generally translates into a high credit score, making it easier to qualify for loans at lower rates.
In addition to this check, lenders may also verify your income and debt loads to understand your cash flow. On paper, a higher income to debt ratio puts you in a better position to repay what you owe.
Why Are Credit-Based Loans Problematic?
Only checking credit may be unfair considering how long it can take to build good credit in your file, and how long bad credit takes to fall of an otherwise flawless record. Under the current scoring models, a mistake from seven years ago can still stand in the way of getting an installment loan — even if you’re currently exhibiting creditworthy behavior.
Although credit scores have never been higher, plenty of people live with bad credit. According to credit reporting agency Experian, 16% of Americans have very bad credit. Another 18% have fair, which still falls under the agency’s subprime category.
Then there’s another 10% of the country who make up the “credit invisible.” These people either have no score whatsoever, or their credit report doesn’t have enough info in it for lenders to make a reasonable assumption of creditworthiness.
With subprime or invisible credit borrowers totaling millions of people, a large number of people have limited access to help in emergencies. Thin, invisible, or bad — this kind of credit doesn’t make borrowing impossible, but it does make it harder when the biggest banks look for prime borrowers.
How is FinTech Changing Things?
Some financial institutions and FinTech services recognize many people are overlooked by traditional measures of creditworthiness. To increase access to cash advances and installment loans to a wider audience, they’ve started to assess alternative data when making underwriting decisions.
Alternative or non-traditional data provides a more holistic approach to your finance by assessing any information that isn’t typically found in a credit check. This information may include rent or bill repayments, including your cell phone, cable package, or insurance premiums.
There’s even a growing push to consider information that isn’t directly connected to your finances. Alternative data could one day consider soft-data points, like a person’s education, job, social media presence, purchase histories, or Internet searches.
According to the International Monetary Fund, the relationship between your finances and how you use tech could broaden borrowing opportunities to people typically denied funding.
How Artificial Intelligence Calculates Alternative Data
Artificial Intelligence comes into play with how FinTech services build credit models that crunch this information. When programmed correctly, they can learn how to provide deeper insights into a person’s finances, lifestyle, preferences, and behavior.
Rather than building AI around limited structured data (like credit scores or incomes), FinTech employs machine learning that can go beyond hard financial data to assess subjective (or qualitative) factors like your social media activity.
When going through large, complex data sets like social media or bill payments, AI sets a pace no human can match, and it can deploy predictive analysis to this data in real-time. It also reduces human error or oversight that often occur when the same analysis is done manually.
The Advantages of Using Alternative Data
Increasing AI’s data sets to include alternative information can help those borrowers who are responsible with money and have a bad score. This process can consider other sources (like alternative bill payments) to determine if they’re likely to pay bills on time.
Looking at your LinkedIn or Twitter account can reveal a lot about your employment status and spending habits that aren’t apparent in your credit history, too. Including this soft data paints a broader picture of your current financial situation. This data may show you’re a high-earned who’s responsible with cash compared to a credit score that may still reflect a mistake from your past.
Another big advantage of alternative data is how it can be automated, making it a faster option than manual applications. Automating this part of the lending process can also lower operating costs for financial institutions. And with fewer overheads, financial institutions may share these savings with their borrowers.
The Disadvantages of Using Alternative Data
As with anything in life, every coin has two sides. For every advantage, there are possible disadvantages to broadening creditworthiness beyond the typical credit data.
By far the biggest potential con is how difficult it may be to stratify and systemize this data. Credit histories and scores, by comparison, are highly regulated by the government and third-party financial parties. Alternative data has yet to receive similar standards, making it more prone to errors and privacy concerns.
Without these standards, there’s also a possibility that alternative data may create a new kind of barrier for people looking to borrow money.
What makes someone’s social media presence creditworthy? Right now, it has no clear benchmark. This could make it hard to build alternative creditworthiness, as there are no obvious steps to improve your online image.
Using alternative data is not something that will happen in the distant future; it’s already here. Today, the financial industry has evolved to use some non-traditional information during the underwriting process. According to Experian, 65% of lenders are using some information beyond the traditional credit report.
But how they integrate even greater soft data (like social media accounts) will depend on how they fine-tune different machine learning to handle these assessments.