DECENTRALIZED FINANCE (DEFI) PLATFORM USING BLOCKCHAIN

Abstract

Decentralized Finance (DeFi) revolutionizes financial systems by eliminating intermediaries, enabling peer-to-peer transactions through blockchain technology. It enhances transparency, security, and accessibility, allowing users to access financial services such as lending, borrowing, and trading without reliance on centralized institutions. Predictions for DeFi indicate exponential growth, with AI and machine learning integration driving advancements in fraud detection, risk assessment, and transaction analysis. Before AI integration, financial fraud detection relied on rule-based systems, manual audits, and traditional statistical models, which lacked adaptability and real-time decision-making capabilities. Legacy systems such as credit scoring models and transaction monitoring frameworks struggled with scalability, requiring continuous human intervention. The increasing sophistication of fraudulent activities and cyber threats has highlighted the inefficiencies of existing solutions, necessitating the adoption of AI-driven approaches. By leveraging machine learning, transaction patterns can be analyzed with higher accuracy, detecting anomalies in real-time and significantly reducing financial risks. The motivation behind this development is to enhance security, improve accuracy in transaction classification, and provide a scalable solution to financial crime detection. Conventional fraud detection mechanisms often fail to keep pace with evolving threats, leading to significant financial losses. Manual reviews are time-consuming and prone to errors, while static models lack the ability to adapt to new fraudulent patterns. Machine learning enables real-time monitoring and predictive analysis, allowing financial institutions to detect suspicious activities with greater precision. The proposed system integrates decision trees, logistic regression, AdaBoost, gradient boosting, k-nearest neighbors, and random forest classifiers to improve transaction classification accuracy. AI-driven analysis enhances fraud detection by learning from historical data, reducing false positives, and enabling automated de-anonymization of transactions. The system applies advanced algorithms to identify fraudulent patterns, optimize financial security, and streamline transaction verification. By automating the process, AI-powered models provide a more robust and efficient approach to securing financial transactions, ensuring reliability and trust in decentralized finance.