Risk-Based Authentication (RBA) is a security measure that evaluates the risk associated with a login attempt or transaction and adjusts the level of authentication accordingly. It considers various factors, including user behavior patterns, device information, location, time of day, and transaction context. RBA uses machine learning to improve its accuracy by learning from past data, spotting patterns, and predicting potential threats. The system adapts over time based on user behavior, trends, and emerging threats. To ensure the effectiveness of RBA, it's essential to use historical data to understand normal user behavior, evaluate a combination of factors, log authentication attempts, monitor logs for unusual behavior, review and update policies regularly, leverage external threat intelligence, and provide users with multiple MFA options. However, there are still challenges to address, including the balance between security and user convenience, algorithmic bias, and staying ahead of cybercriminals.