How AI enables more inclusive lending
A Broken Credit System
Artificial intelligence (AI) and machine learning (ML) present a unique opportunity for credit unions to contribute to a fairer, more inclusive financial system. Unfortunately, the traditional credit model perpetuates bias and excludes a large portion of creditworthy borrowers, including minorities, young adults, military veterans and other groups.
Many studies have found that minority groups often face discrimintion when applying for loans, leading to higher interest rates or rejection. In fact, a study from Upstart found that while over 80 percent of consumers have never defaulted on a credit obligation, fewer than half would qualify for credit based on traditional metrics like credit score.
Unlocking the “Hidden Prime”
There is a huge opportunity to improve the current credit underwriting and decisioning process in particular. Most credit unions, however, worry that in order to lend more inclusively, they will need to take on potentially high amounts of risk. In order to tap into the “hidden” or “invisible primes,” or borrowers with low credit scores and short credit histories with low propensity to default, credit unions can leverage AI and ML technology.
AI and ML models safely employ thousands of data points to help improve the accuracy of identifying and measuring credit risks. These variables include substantiating data points such as employment history, transactions, and educational background.
By taking a deeper look at data and using more sophisticated prediction techniques like AI and ML, credit unions can acquire additional members and serve traditionally disadvantaged segments of their communities. Lending using AI enables greater access to fair credit, creating better economic outcomes for borrowers that would otherwise be excluded from receiving credit.
Harnessing the Power of Data
In a recent survey of credit union professionals attending the NACUSO conference, eight out of ten say they believe AI and ML technology will lead to better credit scoring, and more than half said AI underwriting is an investment priority in 2022. Credit unions clearly see the value in unlocking and leveraging data to make more scientific decisions, but they face the challenge of being limited to their own institution’s data and project resources. Furthermore, building and implementing a solution in a short period of time is a tall order for institutions with competing priorities, opening the door for fintech partnerships.
The Growing Importance of Fintech Partnerships
Traditionally, financial institutions would need to rely on their in-house IT and project teams to execute on new technology solutions, from ideation through implementation and sustained success. With the growth of fintechs specializing in building and validating AI models, credit unions no longer need to dedicate internal project teams, high costs, and lengthy time periods for implementing a solution. As a result, credit unions have the capacity to stand up an AI lending model and begin lending to new segments in as little as three months.
Expanding Greater Access to Credit
AI and ML have the capacity to transform a credit system that has traditionally excluded a large portion of the population. Critics of AI and ML claim that there is potential for the technology to be biased, especially when there is limited training data available. The key lies in partnering with a provider that employs rigorous testing on every loan and application for fairness, with direct oversight from regulators and other expert stakeholders. By leveraging the opportunity of AI and ML, credit unions can improve access to affordable credit and contribute to a fairer and more equitable financial system.
To learn more about preventing bias in AI algorithms, check out this previous article on The Transformational Capacity of AI and ML.
