Using Machine Learning to Identify and Retain At-Risk Customers
Customer churn is a critical issue in the banking sector, directly affecting profitability. Using Machine Learning, banks can proactively identify customers who are likely to leave by analyzing patterns in historical customer behavior and demographics. This enables financial institutions to take strategic actions for retention.
Our Bank churn prediction model leverages features like credit score, age, account balance, tenure, and activity level.
We use classification algorithms such as Logistic Regression, Decision Trees, and Gradient Boosting to make accurate predictions.
The model is evaluated using metrics such as accuracy, ROC-AUC, confusion matrix, and F1-score to ensure reliable performance.