Financial Fraud Detection Using Machine Learning
A.V Vamshi Krishna,
K. Priyavarshitha,
K. Inraju,
B. Shiva,
E. Madhu
In today’s rapidly evolving digital financial ecosystem, fraudulent transactions have become a significant
concern for individuals, businesses, and financial institutions. Traditional fraud detection methods, which
rely on manual verification and rule-based systems, are often inefficient and fail to identify complex
and emerging fraud patterns. To address this challenge, this project proposes a machine learningbased
approach for financial fraud detection using the Decision Tree algorithm. The system is designed
to classify transactions as legitimate or fraudulent based on multiple features such as transaction
amount, time, location, frequency of transactions, and account behaviour. The model is trained on
these parameters to identify patterns associated with fraudulent activities. During implementation,
the decision-making process is represented through feature-based conditional logic, enabling realtime
analysis of both single and multiple transactions. When suspicious activity is detected, the system
generates an automated email alert to notify the respective user or authority. This approach provides a
reliable, efficient, and scalable solution for fraud detection, enhancing the security of digital financial
systems and reducing the risk of financial losses through early detection and predictive analysis.
