In banking, analytics can use data to quickly help customers manage their accounts and complete banking tasks. Financial institutions also benefit by reducing risk and minimizing costs.
FREMONT, CA: Predictive analysis helps predict future events with the use of computer models. Complex programs depend on AI, data mining, and machine learning to examine large amounts of information. With these resources, the model tries to determine what is likely to happen next, given the current conditions.
Banks increasingly offer features to help customers categorize and predict transactions in their accounts, and third-party apps focus on things like budgeting, debt management, and more.
Predictive analytics can improve and help improve your experience as a customer in several ways, like:
In predictive analytics, credit scoring models use data to predict your creditworthiness. FICO credit scoring uses statistical analysis to predict the behavior of how likely you are to miss a payment and base your score on how borrowers similar to you have performed in the past.
Computer models can identify when income and expenses usually hit your account and see where the money goes. And as such, it may be able to help you avoid that problem.
Banks with predictive analytics can identify problems like when somebody uses your credit card or if someone logs in to your account in an unexpected way. By analyzing patterns, they can also reduce bad check scams.
After reviewing your finances, an intelligent program can determine whether you can make extra payments on loans, and how much you might be able to put towards eliminating debt or add to savings.
Lenders realize that not everyone has a high FICO score; however, they should still qualify for loans because some have never established credit. Others are good borrowers even though there are few negatives on their credit report. All these issues can be settled with AI assistance, as it may help non-traditional borrowers get approved.