Banking Banking Basics How Banks and Their Customers Benefit From Predictive Analytics By Justin Pritchard Justin Pritchard Facebook Twitter Website Justin Pritchard, CFP, is a fee-only advisor and an expert on personal finance. He covers banking, loans, investing, mortgages, and more for The Balance. He has an MBA from the University of Colorado, and has worked for credit unions and large financial firms, in addition to writing about personal finance for more than two decades. learn about our editorial policies Updated on July 12, 2021 Reviewed by Charlene Rhinehart Reviewed by Charlene Rhinehart Twitter Website Charlene Rhinehart is an expert in accounting, banking, investing, real estate, and personal finance. She is a CPA, CFE, Chair of the Illinois CPA Society Individual Tax Committee, and was recognized as one of Practice Ignition's Top 50 women in accounting. She is the founder of Wealth Women Daily and an author. learn about our financial review board Share Tweet Pin Email Photo: 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. Predictive analytics can help minimize costs and even 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, data mining, and machine learning to analyze enormous amounts of information. With those resources, the model attempts to determine what is likely to happen next, given current conditions. Note The term “predictive” doesn't mean the models always predict the future accurately. It means they deliver their best prediction based on the available information. 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. In banking, analytics can use data to help customers manage their accounts and complete banking tasks quickly. Financial institutions also benefit by reducing risk and minimizing costs. 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 your behavior, such as how likely you are to miss payments. 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. Note Using analytics, software can alert you so 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, by analyzing data patterns. 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 or add to savings. 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. Note 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 some cases, consumers can 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, which otherwise could cause financial hardship for you. Was this page helpful? Thanks for your feedback! Tell us why! Other Submit Sources The Balance uses only high-quality sources, including peer-reviewed studies, to support the facts within our articles. Read our editorial process to learn more about how we fact-check and keep our content accurate, reliable, and trustworthy. SAS. "Predictive Analytics." Deloitte. "Banking Analytics." FICO. "How Does Predictive Analytics Work?" Wells Fargo. "Wells Fargo Adds AI Enhancement to Mobile App, Giving Personalized Account Insights to Customers Nationwide." IBM Big Data and Analytics Hub. "Analytics in Banking Services." Hitachi Solutions. "An Industry at a Crossroads: Ai, Machine Learning & Predictive Analytics in Banking." IBM Big Data and Analytics Hub. "How to Improve Bank Fraud Detection With Data Analytics." Personetics. "How the Five-Year-Long Relationship Between Personetics and Ally Bank is Helping Customers Save." Emerj. "Artificial Intelligence Applications for Lending and Loan Management." Equifax. "Equifax AI Innovation Opens Doors to Millions Seeking Credit." Consumer Financial Protection Bureau. "An Update on Credit Access and the Bureau’s First No-Action Letter." McKinsey & Company. "Analytics in Banking: Time to Realize the Value."