Banking Block Chain

The project presents an advanced banking management system that integrates machine learning (ML) for credit score prediction, enabling more efficient and secure banking operations. The system comprises two types of users: Admin and Customers. The Admin module allows for managing users, monitoring loans, and updating loan statuses, while the Customer module facilitates registration, login, credit/debit operations, fund transfers, loan applications, and credit score predictions. Machine learning, specifically the Random Forest algorithm, is utilized for predicting a customer’s credit score, which assists in better decision-making for loan approvals. The platform offers enhanced functionality compared to traditional banking systems by providing an automated, transparent, and user-friendly interface. It ensures secure transaction processing and minimizes manual interventions in loan approvals, making the system more efficient. This integrated system reduces risks associated with loan defaults and enhances customer experience by providing predictive insights into financial decisions. The architecture employs secure authentication, structured databases, and scalable design to ensure reliable performance and data privacy. The implementation of predictive algorithms improves risk assessment, streamlining the process of loan management. This project is suitable for financial institutions aiming to enhance their banking operations while also providing a seamless experience for customers.

Keywords: Banking System, Credit Score Prediction, Machine Learning, Random Forest, Loan Management, User Authentication, Account Management