Reimagining the Credit Scoring System

America has an extremely complex financial system,and credit scores are possibly one of the most difficult aspects to navigate. A credit score summarizes a person’s dependability to lenders by providing information about their ability to repay loans, based on past records. For example, if someone pays a car loan each month on time and in full, their credit score would rise. Alternatively, if someone were to consistently miss paying their credit card balance each month, their credit score would plummet. 

However, a credit score is not just generated from payment histories. A significant percentage of someone’s score is influenced by how long someone has had a credit history for, in addition to how many lines of credit they have open. 76 million Americans have thin credit files or no history at all, which creates a dent in their ability to obtain credit.  61 million people have “thin files,” meaning they have little to no credit history, and 16 million are completely “credit invisible,” meaning they have no credit history at all. 

Most of the people that fall into the category of having a thin or non-existent credit file are people that have not been exposed to consistent credit-using practices, either due to age or life circumstances. Young adults, new immigrants, widows, divorcees, and cash-reliant people have lower levels of creditworthiness in the eyes of lenders, which can hurt their chances to build a strong financial foundation. 

A credit score can impact many areas of a person’s life if not properly maintained, which is why building credit matters. Employment opportunities, lending conditions, and rental availability can all be reduced with a poor credit score. Higher interest rates can be a silent killer as well- simple loans can end up costing thousands of dollars more without the proper credit scores to reassure lenders. For example, a $500 emergency loan can end up costing an additional $400 more in interest over the course of 3 months. A loan for a $10000 used car can cost an extra $3000 over three years. Most jarringly, a 30 year mortgage on a house can cost homeowners over an extra $30000 in interest, driving the average US mortgage to $1775 a month. These differences highlight how important credit scores are to a person’s capacity to maintain personal financial stability. 

Clearly, there is a pressing need to establish equality in America’s financial landscape in regards to the credit system. Millions of Americans struggle to borrow from lenders without predatory interest rates because of their lack of credit history. One of the best solutions to this problem is encouraging the use of alternative data in credit assessments. Alternative data is Fair Credit Reporting Act compliant information, such as bill payment history, that can demonstrate a consumer’s financial reliability. Overall, it can provide a more comprehensive view of someone’s financial responsibility, outside of merely assessing whether someone pays their credit card bill on time. 

These bill payments can include monthly utility and telecom payments. By using this data, 6.5 million people that were previously thought to be unscorable would be able to be moved from this category. By layering this data with specialty finance data, an additional 1.9 million people could be scored. Altogether, these methods could help over 30% of people previously thought to be unscorable out of this category. Considering that the majority of adult Americans own utility bills (91%), using alternative data offers a viable way to increase credit availability. Even people with existing credit scores would have their scores improved. 66% of people would experience at least a 10 point score increase. 

Artificial intelligence and machine learning is another way to equitably improve credit scoring. It is expected to accuracy and equity, enabling lenders to extract meaningful insights from a wide range of data sources and make more informed decisions about rating someone’s credit. Equifax has recently introduced an AI tool that is quickly becoming the industry standard for machine learning credit scoring systems. 

In summary, there are many existing problems in the current credit scoring system. The conventional method needs to be reevaluated, because millions of Americans are currently being penalized for their lack of credit history. By leveraging alternative data sources and cutting-edge technologies, the financial industry is ready to break down barriers to loan availability and provide a lifeline to marginalized communities. By doing so, more people may achieve their goals of financial success and stability and ultimately reduce the population of people living in fear of a financial emergency.

Expanding Access to Credit with Alternative Data

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