STRENGTHENING SECURITY PROTOCOL TO COMBAT FINANCIAL FRAUD ADVANCES IN AUTHENTICATION AND ACCESS CONTROL

This research delves into the potential of combining machine learning with multi-factor authentication (MFA) to strengthen financial system security protocols. The main objective is to fight fraud using improved authentication and access control methods. The study looks at practical implementation techniques to fix current security flaws, such as issues with centralised authentication systems that are vulnerable to hacks and system malfunctions. The study highlights the value of energy-efficient and ecologically friendly security measures in addition to traditional encryption and biometric techniques. Different architectures, consensus protocols, applications, services, and implementation goals are all covered in the analysis of various security frameworks. Six machine learning models are thoroughly examined to see how well they identify fraudulent activity and improve overall money security. The results demonstrate how ML and MFA together may greatly enhance network intrusion detection and fraud prevention, eventually improving the security of financial transactions.