Adaptive Authentication System Policy Management
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Risk-based authentication systems face challenges in efficiently managing transactions to minimize financial fraud while reducing the cost of issuing challenges to users, as the current processes can be expensive and intrusive.
Innovation Solution
An adaptive authentication system that stores information from previous requests, receives policy requests to alter authentication operations, and generates policy alterations based on stored data and machine learning patterns to optimize challenge issuance, reducing unnecessary challenges and call center calls.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a risk-based authentication system issues challenges to users with high risk scores, then fraud detection capability is improved, but operational cost increases
Solution Approach 1:
The system dynamically adjusts risk thresholds and challenge issuance parameters based on historical authentication data and observed fraud patterns. By changing the parameters of when and how challenges are issued rather than applying a static rule, the system maintains high fraud detection capability while reducing unnecessary challenges to genuine users, thereby lowering operational costs.
Solution Approach 2:
The system uses stored information from previous authentication requests to automatically learn and adapt its challenge issuance policy without requiring manual intervention. The machine learning component enables the system to self-optimize by analyzing patterns in historical data and automatically adjusting which users receive challenges, reducing the need for costly manual review while maintaining security.
2Reliability
If challenges are issued to verify user identity, then security against fraud is improved, but user experience deteriorates
Solution Approach 1:
The system applies challenge issuance selectively to specific users based on their individual risk profiles rather than applying a uniform policy to all users. By analyzing historical authentication data and identifying user-specific patterns, the system issues challenges only to those users who pose a genuine risk, allowing genuine users to experience smooth, uninterrupted transactions while maintaining security for potentially fraudulent users.
Solution Approach 2:
The challenge issuance policy is dynamically adjusted based on real-time analysis of user behavior and historical data. The system adapts its security measures to each user's risk level, making the authentication process flexible rather than rigid. This dynamic approach ensures that security is maintained when needed while avoiding unnecessary friction for legitimate users, thereby improving overall user experience.
3Reliability
If risk thresholds are lowered to catch more fraud, then fraud detection rate is improved, but number of false positives increases
Solution Approach 1:
The system incorporates feedback loops where the outcomes of authentication challenges and fraud detection decisions are fed back into the machine learning model. By continuously learning from historical data including both successful fraud detections and false positives, the system refines its risk assessment algorithms. This feedback mechanism enables the system to maintain high fraud detection rates while progressively reducing false positives by learning from past performance.
Solution Approach 2:
The system performs preliminary analysis of user authentication patterns and risk factors before issuing challenges or making fraud determination decisions. By pre-processing and analyzing historical authentication data to establish baseline behavior patterns, the system can more accurately distinguish between genuine and fraudulent users, improving fraud detection while reducing false positives through informed preliminary assessment.
Data Source
AI summary
There is disclosed a technique for use in managing policy. The technique comprises storing information relating to at least one previous authentication request. It should be understood that the information can be used in an authentication operation performed in connection with an authentication request. The technique also comprises receiving a policy request to alter a policy relating to an authentication operation that can be performed in connection with an authentication request. The technique further comprises generating an alteration to the policy based on the stored information and the received policy request.