Ai backed risk intelligence for decentralised credit rating"

Project Code :TCMAPY2148

Objective

The project aims to develop an accurate credit score prediction system using Random Forest, XGBoost, and LSTM models. It will feature a user-friendly web interface built with Flask, allowing users to register, log in, and receive credit score predictions. The system will ensure secure and immutable storage of the predicted scores through blockchain hashing, with only hashed values stored to maintain data privacy. The models will be fine-tuned for optimal performance, including preprocessing and evaluation steps, to ensure accurate predictions. The solution will provide instant score generation and clear result display, while offering a decentralized, transparent, and efficient credit rating system accessible through a browser.

Abstract

This project presents an AI-driven credit score prediction system integrated with blockchain technology to ensure data transparency, privacy, and security. The system analyses key financial attributes such as income, savings, liabilities, and expenditure patterns to predict an individual’s credit score using advanced machine learning models including Random Forest (RF), XGBoost, and LSTM. A user-friendly web application, developed using HTML, CSS, and Flask, allows users to register, log in, and submit their financial details. Once the data is entered, it is processed by the trained model to generate an accurate credit score prediction.

To strengthen security and eliminate the risks of data tampering, both the user-provided financial inputs and the predicted credit score are stored on a blockchain in hash format. This decentralized approach ensures immutability, traceability, and protection against unauthorized modifications or data breaches. By combining AI-based risk intelligence with blockchain-powered data integrity, the proposed system delivers a secure, transparent, and reliable credit rating solution that enhances user trust and improves decision-making within financial ecosystems.

Keywords:

AI, Credit Rating, Machine Learning, Blockchain, Decentralized System, Risk Intelligence, Random Forest, XGBoost, LSTM, Flask, Web Application, Financial Prediction, Data Security, Hash Storage, Immutability.

NOTE: Without the concern of our team, please don't submit to the college. This Abstract varies based on student requirements.