Financial Transaction Authentication via Device Data Merging
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Solution Overview
Problem
Existing user authentication systems face challenges in accurately authorizing financial transactions due to limited data availability, leading to false declines of valid transactions and authorization of fraudulent ones, as they rely mainly on historical transaction patterns without real-time device data from internet-enabled devices.
Innovation Solution
The system enhances authorization decisions by gathering and combining data from internet-enabled devices, including device fingerprints, user interactions, and third-party data sources, to create identity and transaction consistency scores, which are used to assess the legitimacy of transactions in real-time.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the system relies mainly on historical transaction patterns for authentication, then the authorization process is simple and fast, but the accuracy of authorization decisions deteriorates leading to false declines and fraudulent transaction approvals
Solution Approach 1:
The patent combines multiple data sources including device fingerprints, user interaction data, third-party data, and historical transaction patterns into a unified authentication framework. This merging of diverse data types enables more accurate authorization decisions by creating a comprehensive view of user behavior and device characteristics, directly resolving the contradiction between decision accuracy and system complexity.
Solution Approach 2:
The system performs preliminary data gathering and analysis by collecting device fingerprints, user interaction patterns, and third-party data before the actual transaction occurs. This preliminary action prepares authentication scores and risk assessments in advance, enabling faster and more accurate real-time authorization decisions without sacrificing precision during the critical transaction moment.
2Reliability
If the system uses limited historical transaction data, then the authorization process is quick, but false declines of valid transactions increase
Solution Approach 1:
The system collects and analyzes device fingerprints, user interaction data, and third-party information in advance before transactions occur. This preliminary data gathering creates pre-computed authentication scores and behavioral profiles that can be quickly referenced during actual transactions, reducing false declines without adding significant time delays to the authorization process.
Solution Approach 2:
The system continuously monitors and updates user behavior patterns, device characteristics, and transaction outcomes to refine authentication models. This feedback mechanism learns from past decisions including false declines, progressively improving reliability over time while maintaining efficient processing speeds through optimized data utilization.
3Reliability
If the system lacks real-time device data, then the authorization process is simple, but fraudulent transactions are more likely to be approved
Solution Approach 1:
The patent integrates real-time device data including fingerprints, user interactions, and third-party information with historical transaction patterns into a unified authentication framework. This combination enables sophisticated fraud detection capabilities by analyzing multiple dimensions of device and user behavior simultaneously, directly improving reliability in detecting fraudulent transactions while managing system complexity through integrated processing.
Solution Approach 2:
The system employs intermediaries such as third-party data sources and device fingerprinting services to collect and verify real-time device information. These intermediaries handle the complexity of data gathering and validation, allowing the core authorization system to focus on decision-making while maintaining high reliability in fraud detection without bearing the full burden of complex data integration infrastructure.
4Measurement precision
If the system incorporates diverse data sources, then the accuracy of authorization decisions improves, but the system complexity increases
Solution Approach 1:
The patent merges diverse data sources including device fingerprints, user interaction data, third-party information, and historical transaction patterns into a unified authentication framework. This integration enables comprehensive analysis across multiple data dimensions, significantly improving authorization decision accuracy while managing system complexity through unified processing architecture and standardized data interfaces.
Solution Approach 2:
The system employs universal data collection and processing mechanisms that can handle multiple data types and sources through standardized interfaces and protocols. This multi-functional approach allows the same infrastructure to process various data sources efficiently, improving authorization accuracy without proportionally increasing system complexity through redundant specialized components.
Data Source
AI summary
Various examples are directed to computer-implemented systems and methods for authentication of financial transactions. The method includes receiving a request from an initiation point to authenticate a user to perform a transaction, and obtaining device data gathered by internet-enabled devices used by the user. Third-party data is received from a third party in response to a third-party authorization inquiry, and a processor located at a decision point uses the device data and the third-party data to authenticate the user for the transaction.