Predictive Mobile Payment Authorization via Behavioral Biometrics
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Solution Overview
Problem
Mobile payment systems face a challenge in balancing security and user experience, as requiring authentication for each transaction can be cumbersome, while authorizing payments without authentication may lead to potential abuse and financial loss.
Innovation Solution
A computing device analyzes sensor data to determine a risk level for transactions, allowing for predictive authorization without explicit user authentication, by comparing sensor inputs to preconfigured patterns and performing a risk assessment based on user behavior and transaction characteristics.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If authentication is required for each payment transaction, then security is improved, but user experience deteriorates due to cumbersome procedures
Solution Approach 1:
The system performs preliminary authentication by analyzing sensor data patterns (typing rhythm, scrolling behavior, device handling) before the payment transaction occurs. This pre-authentication establishes a baseline of user behavior that enables subsequent transactions to be authorized without requiring explicit authentication, thus improving user experience while maintaining security through advance verification.
Solution Approach 2:
The system uses the user's own natural interactions with the device (typing, scrolling, swiping) as authentication credentials. These self-generated behavioral patterns serve as the authentication mechanism, eliminating the need for separate authentication actions and improving ease of operation while maintaining security through unique behavioral biometrics.
2Reliability
If authentication is required for each payment transaction, then security is improved, but transaction efficiency deteriorates
Solution Approach 1:
Authentication is performed in advance by continuously monitoring and analyzing user behavior patterns during normal device usage. This preliminary authentication allows multiple subsequent transactions to be processed efficiently without repeated authentication steps, thereby improving transaction efficiency while maintaining security through the pre-established behavioral verification.
Solution Approach 2:
The system continuously monitors user behavior patterns during normal device operation, transforming routine interactions into ongoing authentication verification. This continuous monitoring enables seamless transaction processing without interrupting the user's workflow, maintaining both security and high transaction efficiency.
3Ease of operation
If authentication is eliminated for payments, then user experience is improved, but risk of unauthorized transactions increases
Solution Approach 1:
The system uses the user's inherent behavioral patterns (typing rhythm, scrolling speed, device holding position) as the authentication mechanism. These self-generated patterns are difficult to replicate by unauthorized persons, providing strong security without requiring explicit authentication actions. This enables payments to be made seamlessly while maintaining security through behavioral biometrics.
Solution Approach 2:
The system introduces behavioral pattern analysis as an intermediary between the user and the payment authorization. Instead of direct authentication or complete elimination of security checks, the system uses behavioral biometrics as a mediator that continuously verifies user identity through natural interactions, enabling frictionless payments while preventing unauthorized transactions.
4Ease of operation
If behavioral pattern analysis is used for authentication, then explicit authentication steps are reduced, but system complexity increases
Solution Approach 1:
The system leverages existing sensor capabilities (accelerometer, gyroscope, touchscreen) that are already present in modern devices for their primary functions. By repurposing these existing sensors to capture behavioral patterns during normal usage, the system avoids adding dedicated authentication hardware, thereby reducing overall system complexity while still enabling sophisticated behavioral biometric authentication.
Data Source
AI summary
A device is described that includes one or more processors, one or more sensors to generate sensor data, one or more communication units and one or more modules. The one or more modules are operable by the one or more processors to, prior to initiating a payment transaction, analyze the sensor data to determine a risk level for the payment transaction, and initiate the payment transaction with a payment system. The one or more modules are further operable by the one or more processors to determine a risk level threshold for the payment transaction, and selectively send, based on the risk level determined prior to the payment transaction and the risk level threshold and using the one or more communication units, authorization for the payment transaction.