The main challenge of this use case is to predict whether the high valued customer will become churn or not. The model has to be built for the existing customers and not applicable for new customers. Here we are considering high valued customers as the customers having amount more than threshold amount in their account. The solution has to address multiple solutions.
We developed the model and deployed using Machine learning and deep learning algorithms and few other deployment tools. By analysing the customer demographics and few other features model for existing has been built. The churn problem will cause the bank to encounter the cash rotation problems and few other problems. This model helped to identify the churn at the early stages which helped in churn customer retention.
The AI & Analytics Engine can analyse a variety of data, including new data sources, and at relatively complex interactions between behaviours and individual history and recommend models that predict the risk of the customer churning. In addition, The Engine’s models can identify the variables that have the most importance to this prediction, allowing FIs to act to improve those areas for customers. The Engine can also be used to recommend the next best offer that will have the highest likelihood of retaining the individual customer.
Our efficient models helped the bank to identify potential leavers and then decide on the right course of action to prevent their departure. As a result, our client has optimized related processes and is now better able to identify customers at risk of churn and prevent churn.