How Banks Use Predictive Analytics for Service, Marketing, & Security

Businesswoman talking in meeting
••• David Lees / Getty Images

Artificial intelligence is making its way into your bank account. As computers get smarter, financial institutions can use consumer databases and historical transactions with the goal of predicting the future. It may sound boring to you, but predictive analytics can help minimize costs and (hopefully) improve your experience with your bank.

What Is Predictive Analytics?

Predictive analytics is the process of using computer models to predict future events. Sophisticated programs rely on artificial intelligence and data mining to analyze enormous amounts of information. With those resources, the model attempts to determine what is likely to happen next, given current conditions.

The term “predictive” may be a bit optimistic—the models don’t know everything, and they don’t always predict the future accurately.

In banking, predictive analytics can help customers manage their accounts and complete banking tasks quickly. Financial institutions also benefit by reducing risk and minimizing costs. For better or worse, institutions use a variety of data sources and machine learning. For example, they have your transaction history, and they may tie in demographic information and additional details from external databases.

How Bank Customers Benefit

Predictive analytics can improve your experience as a customer in several ways. That said, some may find it unsettling that financial institutions have so much information, and that they depend on computers to make decisions that affect your life. On the bright side, computers are always available, and they don’t discriminate against customers they don’t like (assuming the model is built to avoid bias).

Credit scoring: You may already be familiar with predictive analytics—credit scoring models use data to predict your creditworthiness. For example, the FICO credit score uses statistical analysis to predict how likely you are to miss payments within the next 90 days. Your score is based, in part, on how borrowers similar to you have performed in the past.

Help with budgeting: Computer models can help you manage your finances. They can identify when income and expenses typically hit your account, and they can see where your money goes. As a result, they may be able to prevent problems. For example, if your mortgage payment hits your account on the 15th of every month but you’re running low on cash, your bank can send an alert. With advance notice, you can transfer funds from other accounts or contact your mortgage servicer so you avoid overdraft charges, late payment penalties, and other problems.

Fraud prevention: Sometimes identity theft is entirely out of your control. Even if you’re extremely careful, thieves can steal your information in data breaches and use your card number or other sensitive details. Banks with predictive analytics are better equipped to spot problems. They may notice when somebody else uses your credit card or if somebody logs in to your account in an unexpected way. They may also be able to reduce bad check scams, which can cause significant losses for victims (you typically lose money in those cases—not the bank).

Financial management: Software can assist with bigger-picture decisions as well. For example, after reviewing your finances, an intelligent program can determine whether or not it makes sense to make extra payments on loans, and how much you might be able to put toward eliminating your debt. Banks might also be able to coach you on how to earn higher rates on your savings.

Loan approval: Lenders are getting more sophisticated about how they evaluate loan applications. They realize that not everybody has a high FICO score—but they should still qualify for loans. Some people have never established credit, and others are still good borrowers, even with a few negative items in their credit reports. An internal Equifax study showed that some lenders unnecessarily deny loans due to outdated loan underwriting criteria, but artificial intelligence may help nontraditional borrowers get approved.

How to Use Predictive Analytics in Your Finances

It’s easy to take advantage of machine learning and improve your finances.

Personal financial management (PFM): Use PFM tools to help you manage your finances and identify opportunities to improve things. Banks increasingly offer features to help you categorize and predict transactions in your accounts, and third-party apps focus on things like budgeting, debt management, and more. Learn how those apps earn revenue, as they may be designed to entice you to open new bank or credit card accounts. If you come out ahead, that’s great, but it’s critical to understand everybody’s incentives.

Forward-thinking lenders: When you need to borrow money, look to lenders that consider more than your traditional FICO score and your income. Online lenders increasingly use alternative credit information to approve loans, including your job history, your education, and even your online behavior.

It’s already happening: To some degree, you don’t need to do anything. Financial institutions already employ predictive analytics behind the scenes. In many cases, consumers find those applications annoying—like when you’re trying to use your debit card and the bank thinks you’re a thief. But you benefit from reduced fraud, some of which might cause financial hardship for you.