Predicting Customer Churns for Banks
Banks have a plethora of customer data in the form of reference data, transaction data, and social data. With all this information, banks are well poised to understand when their customers are churning and more importantly, to whom they are churning. However, the key to this strategy is in adopting a better AI strategy. AI algorithms can understand customer engagement by looking at recency frequency and monetary models and effectively provide actionable insights when a customer's engagement drops.
AI platforms can also learn from large volumes of customer engagement data like transaction, feedback and help categorize customers into relevant customer segments, which in turn helps predict the probability of customer churn at a segment level. Hence, it's evident and intuitively obvious that AI can help banks arrest customer churn by proactively engaging with them. Banks, on the other hand, should also start adopting better and more responsible AI strategies to be able to pre-empt and prevent further customer churn. With the advent of the Web 3.0 fintech rising up, this AI-driven churn prediction model is not just a differentiator, but also a business necessity.