Behavioral Stochastic Authentication for Financial Transactions
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Solution Overview
Problem
Existing secure communication systems face challenges in authenticating consumer electronic devices and their users effectively, particularly in financial transactions, as deterministic methods are prone to spoofing and identity theft due to the reliance on single-factor authentication like usernames and passwords.
Innovation Solution
A behavioral stochastic authentication system that compiles and analyzes user behavior data from various sources, including cookies, accelerometers, and GPS, to create a behavioral portrait, allowing for a more accurate and reliable authentication process by comparing current device activity with known patterns, thereby enhancing security beyond traditional deterministic methods.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If deterministic authentication methods (username/password) are used, then ease of operation is improved, but reliability deteriorates due to susceptibility to spoofing and identity theft
Solution Approach 1:
The patent combines multiple authentication factors including deterministic methods (username/password) with stochastic behavioral analysis (device fingerprints, usage patterns, location data) to create a multi-layered authentication system that maintains ease of use while significantly improving security reliability
Solution Approach 2:
The system introduces an intermediary behavioral analysis layer that mediates between the user's authentication attempt and the authentication decision, analyzing stochastic behavioral data from cookies, device sensors, and usage patterns to verify authenticity without adding direct user interaction
2Reliability
If stochastic behavioral analysis is added to authentication, then reliability is improved, but device complexity increases
Solution Approach 1:
The system employs self-service mechanisms where the authentication system automatically collects and analyzes behavioral data from existing device sources (cookies, sensors, usage patterns) without requiring additional user actions or complex manual configuration, reducing the perceived complexity for users
3Measurement precision
If multiple data sources are collected for behavioral analysis, then measurement precision is improved, but loss of information increases due to data management requirements
Solution Approach 1:
The system extracts only the essential and most reliable behavioral features from multiple data sources (cookies, device fingerprints, usage patterns, location data) while discarding redundant or less informative data, maintaining high measurement precision while reducing data management complexity and information loss
Data Source
AI summary
Methods and systems for authenticating a user and a consumer electronic device (CED) to a financial services provider (FSP) for purposes of communications initiated from the device and needing security, such as purchases and financial transactions, are provided. The FSP may compile information about a user's behavior from various sources, both public and private, including the CED. The information may be of a stochastic nature, being gathered by sampling user data and behavior at chosen times. The information may include indicators of user behavior—such as the user using the device to check various accounts and web-pages—and data from the device—such as GPS location. Based on the compiled stochastic information, and using a sliding scale, a throttling mechanism, acceptance variation, and pinging information, the FSP can compare current information from the device with what is known about the user and the device to provide a more accurate and reliable authentication process.