Predictive Analysis for Defector in Banking System

An essential component of risk management for financial organizations is predictive analysis for defectors in the banking system. The amount of credit transactions is growing, and conventional techniques frequently fail to reliably identify possible defaulters. In this paper, suggesting machine learning algorithms which helps to anticipate defaulters more effectively and accurately. Making use of an extensive dataset that includes credit history, transactional data, and demographic data, we apply ANN, random forests, and K-means among other machine learning methods. By applying feature engineering approaches, the raw data is transformed into relevant insights that improve the models' predictive capacity. On both training and test datasets, the suggested models are assessed using common performance metrics like accuracy, precision, recall, and F1-score. The outcomes of the experiments show how well the suggested method works to correctly identify possible defaulters, giving financial institutions the ability to proactively manage risks and streamline decision-making procedures. Detecting Defaulters with machine learning algorithms which produces 83% Accuracy,71% F-Score in Random Forest, 71% F-Score in Random Forest when compared to other Algorithms.