Dynamic Risk Assessment for Account Security Features
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Solution Overview
Problem
Existing security systems face a challenge in balancing fraud risk and user experience, as increased security measures can negatively impact user convenience and lead to inefficient use of computing resources.
Innovation Solution
A dynamic risk assessment method that determines a risk metric for accounts based on applied features, allowing for the adjustment of security and convenience features to balance fraud risk and user experience, including the use of biometric authentication and other features on portable devices.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If more rigorous security measures are implemented to reduce fraud risk, then security level is improved, but user experience deteriorates due to increased actions required from users
Solution Approach 1:
The system dynamically adjusts security measures based on real-time risk assessment. Instead of applying fixed rigorous security to all users, the system adapts security requirements according to the calculated fraud risk for each user and transaction, allowing security levels to vary dynamically rather than being static
Solution Approach 2:
The system changes security parameters based on risk metrics. When fraud risk is low, security requirements are relaxed to improve user experience. When risk increases, security measures are intensified. This parameter adjustment allows the system to optimize both security and usability based on current conditions
2Reliability
If more rigorous security measures are implemented to reduce fraud risk, then security level is improved, but computing resource usage increases due to additional security checks
Solution Approach 1:
The system dynamically adjusts security measures based on real-time risk assessment. Instead of applying fixed rigorous security to all users, the system adapts security requirements according to the calculated fraud risk for each user and transaction, allowing security levels to vary dynamically rather than being static
Solution Approach 2:
The system applies security measures proportionally to the assessed risk level. For low-risk users, minimal security checks are performed. For high-risk users, more comprehensive security measures are applied. This partial action approach avoids the excessive computing resource consumption that would result from applying full security measures to all users
Data Source
AI summary
Techniques are described for determining account features based on a risk assessment. A first set of account features may be determined, including security feature(s) such as mode(s) for authenticating and/or verifying the identity of a user associated with account(s). Based on the first set of features, a risk metric may be determined for the account(s). The risk metric may indicate a risk that fraud may be committed against the account or using the account. Based on the determined risk metric, a second set of account features may be determined for the account(s). The first and second sets of account feature(s) may be applied to the account(s). Disabling a particular feature may cause a reevaluation of the risk metric and a redetermination of the feature sets to be applied to the account(s).