Authentication Risk Scoring Using Aggregated Client and Cyber Data
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Solution Overview
Problem
Banks and financial entities face challenges in accurately authenticating clients due to the lack of integration of comprehensive client data, device data, claims data, and cyber fraud analysis, leading to increased fraud and reputational and monetary losses.
Innovation Solution
A system and method that aggregates client data, device data, claims data, and cyber fraud analysis to generate an authentication determination, using a risk score based on these data types to enhance authentication accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If banks use traditional authentication methods with limited data elements, then the authentication process is simple and quick, but authentication accuracy is low leading to fraud
Solution Approach 1:
The patent combines multiple data sources including client data, device data, claims data, and cyber data into a unified authentication system. This merging of previously separate data elements enables comprehensive fraud assessment while maintaining system coherence through a centralized data model that integrates all information streams for holistic client verification
Solution Approach 2:
The authentication system is designed to handle multiple data types and authentication scenarios through a single multi-functional platform. The system can process client information, device fingerprints, claims history, and cyber threat data simultaneously, making it adaptable to various authentication contexts while maintaining consistent security standards across different use cases
2Measurement precision
If banks aggregate comprehensive client data for authentication, then authentication accuracy improves, but data processing time and computational resources increase
Solution Approach 1:
The system performs preliminary data aggregation and profiling during client onboarding and ongoing interactions, building comprehensive client profiles in advance. This pre-processing of data enables rapid authentication decisions during actual login events, as the system already has pre-computed risk scores and behavioral baselines ready for immediate comparison against current authentication attempts
Solution Approach 2:
The authentication system continuously learns from authentication outcomes and fraud patterns, adjusting its data processing priorities and thresholds based on feedback. The system refines its authentication models by analyzing successful and failed authentication cases, optimizing the balance between comprehensive data analysis and processing speed through iterative improvement of its decision-making algorithms
3Reliability
If banks implement comprehensive fraud analysis, then fraud detection capability improves, but operational complexity increases
Solution Approach 1:
The fraud analysis system is divided into distinct functional modules that handle different aspects of fraud detection: client data processing, device fingerprinting, claims verification, and cyber threat analysis. Each module operates independently with specialized algorithms, allowing the system to maintain high fraud detection reliability through divided expertise while reducing operational complexity through modular architecture that enables independent maintenance and optimization of each segment
Data Source
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AI summary
The invention relates to a method and system that aggregates client data and cyber indicators to authenticate a client. The system comprises: a computer server comprising at least one computer processor and coupled to the memory, programmed to: receive, via an electronic input, an authorization request from a requester for access to an account; identify a client identifier associated with the authorization request; using the client identifier, retrieve, from the memory, a client profile, wherein the client profile is based on an aggregation of client data, client device data, claims data and cyber data; generate a risk score based on the aggregated combination of the client data, client device data, claims data and cyber data to determine whether the requester is authenticated to access the account; and automatically apply an authentication determination to the authorization request.