FRAUD DETECTION IN FINANCIAL TRANSACTONS

Financial fraud, considered as deceptive tactics for gaining financial benefits, has recently become a widespread menace in companies and organizations. Conventional techniques such as manual verifications and inspections are imprecise, costly, and time consuming for identifying such fraudulent activities. With the advent of artificial intelligence, machine-learning-based approaches can be used intelligently to detect fraudulent transactions by analyzing a large number of financial data. Fraud has been a persistent challenge in the realm of financial transactions, posing significant threats to businesses, financial institutions, and individuals alike. As technology advances and financial systems become increasingly digitalized, the methods and sophistication of fraudulent activities also evolve. This paper explores cutting edge technologies such as machine learning and are revolutionizing fraud detection. By analyzing transaction patterns and employing anomaly detection algorithms, such as CNN organizations can identify and mitigate fraudulent activities in real time. In response to this ongoing threat, the field of fraud detection in financial transactions has emerged as a critical area of focus for organizations worldwide. In order to identify fraudulent behavior in financial transactions, this research paper suggests a unique technique. Key challenges include handling imbalanced datasets where fraudulent transactions are rare compared to legitimate ones, ensuring the privacy and security of sensitive financial information, and maintaining low latency to prevent delays in transaction processing.