With the banking industry in flux, disruptive competitors grabbing market share, and customers raising the bar on what kind of experiences they expect, banks must find ways to attract and retain customers on a level never known before. Today’s banking customers grow impatient more quickly than they have in the past, and if they are unhappy with an experience, their loyalty is fleeting.
According to CDC/NCHS National Vital Statistics, each year, most banks lose about 10% of their account deposits due to customers closing their accounts. Of that segment, 50% leave because they are dissatisfied with the bank’s service, fees, rates, products, or lack of convenience. The other 50% leave due to events that the bank cannot control, such as death, divorce, or displacement.
For example, if a bank handles $700 million to a billion in deposits annually, nearly $100 million in capital walks out the door each year due to customers closing their accounts.
A regional bank saw an opportunity to reduce attrition in this area. This long-time client wanted to be able to predict which checking accounts were likely to close within the next 90 days so they could take action to retain the customer. They knew machine learning could provide them with that data, but they had never leveraged it before.
Machine learning is a data science technique that analyzes massive quantities of data, especially historical, to discover trends and insights and rapidly predict future behaviors and outcomes. The technique lets the data learn from itself, free of human bias or the need for explicit instruction.
Traditional analytics tools don’t have the capability to rapidly uncover patterns when there are billions of data points to be analyzed, nor can humans identify patterns in such large quantities of data, not to mention in real-time. This is the value of machine learning in enabling your data to be a market differentiator, and that’s why this bank wanted to explore a proof of concept through a Fusion Alliance Machine Learning Jumpstart.