Some financial services customers become quite valuable as they generate fees on transactions and grow a portfolio of business over the years including banking fees, credit cards, home loans, personal loans and more. Simple churn analysis uses rules based on known behaviors to identify potential churn risks. Rules-based systems, however, are inflexible and miss many customers who do churn and generate false positives that end up giving expensive incentives to customers who were not at risk to leave the bank.
AI is a great solution for customer churn prediction as the problem involves complex data over time and interactions between different customer behaviors that can be difficult for people to identify. AI can look at a variety of data, including new data sources, and at relatively complex interactions between behaviors and compared to individual history to determine risk. AI can also be used to recommend the best offer that will most likely retain a valuable customer. In addition, AI can identify the reasons why a customer is at risk and allow financial institution to act against those areas for the individual customer and more globally.