Finance
For a financial institute We did a complete EDA (Exploratory data analysis) and ML modeling to understand the behavior of their customers. We did various analyses on the dataset to identify the probability of default. In which segment and what level of POD? We did data analysis to identify the customer churn pattern and the areas that can be improved that determine what type of customers are generating high revenue and what type of customers are probable to churn. This helped in reducing the overall NPA for the institute. We have also determined the customer life cycle value (predictive modeling) that helps in identifying which customer can generate what level of revenue for an institute.
