Password-based authentication system recently has been more secure as risk-based authentication system (RBA) is indentured. The RBA system monitors the parameters extracted during the user login process, and based on the proposed model, the system raises a multi-factor authentication to the user. As the vulnerability has increased concerning passwords, fingerprints’ easy access to any web application may result in a security flow. Several best practices have addressed these issues, but the security threats have been challenging during the initial login sessions. Hence, this paper proposes a novel method for an effective risk identification method during the initial login phase using a hierarchical sub-feature-based model for different categories of users in an RBA system. The FAR is comparatively better in our proposed model, with minimal re-authentication requests for the user.