Developing AI-based Fraud Detection Systems for Banking and Finance
- Resource Type
- Conference
- Authors
- Dash, Samikshya; Das, Simanchala; Sivasubramanian, S.; Sundaram, N. Kalyana; G, Harsha K; Sathish, T.
- Source
- 2023 5th International Conference on Inventive Research in Computing Applications (ICIRCA) Inventive Research in Computing Applications (ICIRCA), 2023 5th International Conference on. :891-897 Aug, 2023
- Subject
- Communication, Networking and Broadcast Technologies
Components, Circuits, Devices and Systems
Computing and Processing
Robotics and Control Systems
Logistic regression
Neural networks
Finance
Banking
Machine learning
Fraud
Regression tree analysis
Fraud detection
Artificial intelligence
Decision trees
Data management
Performance evaluation
Legal frameworks
- Language
Safeguarding financial institutions and their consumers against fraudulent activity makes fraud detection a top priority in the banking and finance business. There has been a rise in the development of artificial intelligence-based fraud detection systems in tandem with the popularity of machine learning methods. This study presents a comprehensive evaluation of modern machine learning approaches like neural networks in comparison to more conventional ones like logistic regression and decision trees. These techniques are tested using financial and banking data from the real world, and the findings indicate that neural networks are superior to more conventional approaches. In addition, our research emphasizes the significance of data gathering and administration in the evolution of fraud detection systems.