Have you ever wondered how machine learning algorithms in banking determine who gets approved for a loan and who doesn’t?
The use of machine learning in banking is nothing new. Banks have been using machine learning to automate everyday processes such as fraud detection for years. Machine learning is also used in banking to automatically reach lending decisions. In this article, we will explore the machine learning process that is used to approve or deny a loan. Note that the same methodology is used when making credit decisions.When you apply for a loan or credit card, a machine learning algorithm evaluates your request against a database of thousands of other customers that have come before you. Loans are categorized into two groups: open and closed. Open loans are those that do not yet have a final disposition. Open loans are either in good standing, meaning the customer is making regular payments, or it is late. Closed loans are those that are either paid off, in default or charged off, meaning the bank acquired the expense. The machine learning algorithm concentrates on closed loans when evaluating data.
Using linear regression, the machine learning algorithm evaluates several data points to create aggregate model of past loan outcomes. The main data points considered are the loan amount, the applicant’s income, and the co-applicant’s income if applicable. These three factors bare the greatest weight in making a lending decision. But the machine learning algorithm takes into account more than just that. It may also consider your age, gender, marital status, number of dependents, education, employment status and position, credit history and the value of the property you own.By comparing your data points with the data points of thousands of prior customers, machine learning is able to generate a risk score. The bank sets the risk score threshold and may change it depending on the amount of risk they are willing to take. In fact, another machine learning mechanism is behind determining the risk score in some systems. If your risk score is under the threshold, you will be approved for the loan.
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