AI-BASED FINANCIAL FRAUD DETECTION IN REAL-TIME TRANSACTIONS

Abstract

Financial fraud detection in real-time transactions has become a critical concern for financial institutions due to the rise in digital transactions and the sophistication of fraudsters. Traditional systems relied on rule-based approaches and human oversight, which struggled to adapt to new fraud patterns, resulting in high false positives and slow responses. Early fraud detection methods used statistical models and threshold-based systems, but these were inadequate for detecting complex, evolving fraud behaviors. With the rise of machine learning, financial fraud detection has entered a new era. AI-based systems can now learn from past transaction data and detect subtle fraud patterns with greater accuracy. The motivation to develop AI-based solutions comes from the need for real-time, automated fraud detection that can quickly identify and mitigate risks, reducing human error and financial losses. Traditional systems face several issues, such as their inability to quickly adapt to new fraud techniques, high false positive rates, and scalability challenges. The proposed AI-based system leverages machine learning models like support vector machines (SVM), decision trees, and deep learning techniques to analyze real-time transaction data, providing faster, more accurate fraud detection. By processing transactions in real-time, it improves detection speed, reduces losses, and offers a scalable, efficient solution for combating fraud in a dynamic digital environment. Additionally, the integration of anomaly detection techniques and ensemble learning approaches further enhances accuracy, reducing false positives and improving overall fraud detection performance. This AI-driven approach ensures financial institutions can proactively prevent fraudulent activities, safeguarding customer transactions and maintaining trust in digital financial services.