A groundbreaking system designed to combat fraud in digital banking has been filed for patent under the title "Real-Time Fraud Prevention in Digital Banking: A Cloud and AI Perspective." This innovative solution aims to enhance security and reliability in digital banking through advanced machine learning and cloud-based infrastructure.
Developed by Jeyasri Sekar, a proficient software engineer in the USA, the system effectively tackles the escalating complexity of fraud schemes in the digital banking sector.
The invention employs a combination of machine learning models, including Support Vector Machines (SVM) and Naïve Bayes (NB), to classify transactions as either fraudulent or legitimate.
This real-time detection framework mitigates fraudulent activities instantly, safeguarding financial institutions and customers from potential financial losses.
A key feature of the system is the Improved Red Piranha Optimization (IRPO) technique, a novel feature selection method that enhances model performance and efficacy in fraud detection. Advanced AI algorithms enable the system to analyze vast amounts of transactional data in real-time, identifying patterns indicative of fraudulent behavior.
The system's ability to manage large data volumes efficiently ensures accurate and reliable detection. The adaptive nature of its machine learning models allows it to stay ahead of new and sophisticated fraud tactics. Furthermore, the solution maintains compliance with stringent regulatory requirements concerning data privacy and security.
This comprehensive approach to fraud prevention is expected to significantly enhance the security and trustworthiness of digital banking systems. By enabling real-time detection and prevention of fraudulent transactions, the system promises to reduce financial losses and bolster consumer confidence in online banking services.
