Predicting Customer EMI Credit Risk in Banking: A Behavioral Model Utilizing Machine Learning

Abstract

The financial industry has gone through changes due, to advancements in technology and data analysis. In this abstract we discuss the use of Python and machine learning techniques to improve a real-life banking system. The focus of this project is to utilize machine learning for fraud detection customer support and investment recommendations. Advancements in technology and data analysis have prompted significant changes in the financial industry. This abstract explores the application of Python and machine learning techniques to enhance a real-life banking system. The project primarily focuses on utilizing machine learning for improving fraud detection, customer support, and investment recommendations. The project begins by gathering historical transaction records, customer information, and relevant datasets to prepare the data. Several steps, such as cleaning the data, handling missing values, and ensuring data privacy and security in compliance with regulations, are taken. Next, machine learning models specifically tailored for the banking system’s use cases are selected and trained. This involves employing algorithms like decision trees, random forests, logistic regression, and deep neural networks to address challenges such as fraud detection, credit scoring, customer service enhancements, and investment recommendations. Effective feature engineering techniques are applied to derive valuable insights from the data. The models are then tested to evaluate their performance using a combination of training and testing datasets. Once the models produce satisfactory results, they are implemented in a production environment to handle either time-based or batch processing, as required by the banking system.