Biometric Checkout Authentication Using Risk-Based Account Selection
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Solution Overview
Problem
Authenticating a user and/or a payment device during payment transactions using multi-step authentications can be slow and cumbersome due to the necessity of presenting physical payment cards or mobile devices, leading to inaccuracies and inefficiencies when biometric data is used without a physical device.
Innovation Solution
A system utilizing risk-based authentication (RBA) with biometric data and context information to generate confidence scores for multiple candidate payment accounts, selecting the account with the highest score for secure transactions, thereby reducing the need for additional verification steps.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If multi-step authentication is used to verify user identity and payment device, then transaction security is improved, but the authentication process becomes slow and cumbersome
Solution Approach 1:
The system performs preliminary biometric enrollment and risk profile creation during initial setup, storing encrypted biometric templates and risk parameters. During actual transactions, the system retrieves pre-stored data and performs rapid comparison against established profiles, eliminating the need for repeated multi-step verification and significantly reducing authentication time while maintaining security.
Solution Approach 2:
The system creates a digital copy of the biometric template and risk profile in an encrypted database. During transaction authentication, the system compares the live biometric input against the stored encrypted template using rapid pattern matching algorithms, eliminating the need for physical card presentation and multiple verification steps, thus reducing transaction time while maintaining security through the encrypted copy mechanism.
2Ease of operation
If biometric data is used without physical payment device, then transaction convenience is improved, but authentication accuracy deteriorates due to inability to verify physical device presence
Solution Approach 1:
The system uses a universal biometric verification mechanism that can authenticate users across multiple transaction types and devices. The risk-based authentication model integrates multiple verification factors including biometric data, transaction context, device fingerprinting, and historical behavior patterns into a single unified authentication decision, maintaining high accuracy while enabling contactless transactions without physical cards.
Solution Approach 2:
The system introduces a risk-based authentication intermediary layer that sits between the biometric verification and transaction authorization. This intermediary component analyzes transaction context, device information, and historical data to generate risk scores that modulate the authentication decision, compensating for the lack of physical device presence by adding contextual verification dimensions.
3Measurement precision
If confidence scores are generated for multiple candidate accounts, then account identification accuracy is improved, but system complexity increases
Solution Approach 1:
The system segments the authentication process into distinct modular components: biometric extraction module, risk profile retrieval module, confidence score calculation module, and account selection module. Each component operates independently and processes specific data types, making the overall system more manageable and easier to maintain while improving accuracy through specialized processing of different authentication factors.
Solution Approach 2:
The system dynamically adjusts authentication parameters and risk thresholds based on transaction context and user behavior patterns. The confidence score thresholds and verification requirements are adaptively modified based on historical data and risk assessments, allowing the system to maintain high accuracy while reducing complexity by automatically optimizing parameters rather than requiring manual configuration of multiple verification steps.
Data Source
AI summary
A system and method authenticate payment transactions using risk based analysis modeling when multiple payment accounts are identified by input biometric data. An authentication request includes the biometric data associated with a user. Multiple user payment accounts associated with the biometric data are identified. A confidence score is generated for each of the multiple user payment accounts based on context data associated with the payment transaction. The payment account with the highest confidence score is selected. The selected payment account is authenticated for use in processing the payment transaction to ensure payment transactions are performed on the correct payment account.