Multi-Source Authentication Risk Scoring for Fraud-Resistant Banking
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Solution Overview
Problem
Banks and financial entities face challenges in accurately authenticating clients due to the proliferation of data breaches, leading to impersonation and unauthorized account changes, resulting in significant monetary and reputational 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 machine learning and artificial intelligence to enhance authentication accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If banks use traditional authentication methods, then the system is simple to operate, but authentication accuracy deteriorates leading to fraud losses
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 diverse data elements enables comprehensive risk assessment and improves authentication accuracy by considering multiple factors simultaneously rather than relying on single authentication methods.
Solution Approach 2:
The authentication system is designed to handle multiple types of data inputs and generate universal authentication decisions applicable across different banking operations. The system processes various data types (client information, device fingerprints, claims history, cyber threat data) through a unified risk scoring mechanism that can be applied to diverse authentication scenarios.
2Measurement precision
If banks aggregate multiple data sources for authentication, then authentication accuracy improves, but data processing time increases
Solution Approach 1:
The system pre-processes and stores client data, device data, and claims data in structured formats before authentication events occur. By maintaining pre-computed risk indicators and organized data repositories, the system reduces the computational burden during actual authentication transactions, enabling faster processing despite the comprehensive data aggregation.
Solution Approach 2:
The system automatically collects, aggregates, and analyzes multiple data sources without requiring manual intervention. The risk scoring mechanism operates autonomously by integrating data from various sources and generating authentication decisions, eliminating the need for time-consuming manual review processes while maintaining high accuracy.
3Reliability
If banks implement comprehensive data aggregation, then fraud detection capability improves, but operational complexity increases
Solution Approach 1:
The patent introduces a centralized data aggregation platform that serves as an intermediary between multiple data sources and the authentication decision-making process. This intermediary layer standardizes data formats, manages data quality, and coordinates information flow from diverse sources including client profiles, device fingerprints, claims databases, and cyber threat intelligence, simplifying the overall operational complexity.
4Reliability
If banks use multiple data elements for authentication, then authentication reliability improves, but infrastructure requirements increase
Solution Approach 1:
The system transitions from traditional two-dimensional authentication (user credentials only) to multi-dimensional authentication by incorporating additional data dimensions such as device characteristics, behavioral patterns, claims history, and cyber threat context. This dimensional expansion enables more reliable authentication decisions by evaluating requests from multiple perspectives simultaneously.
Data Source
AI summary
A system that aggregates client data and cyber indicators to authenticate a client is provided. The system receives a request to access a financial account of the client, and retrieves a client profile. The client profile includes an aggregation of data into a plurality of data indicators. An optimal number of data indicators is determined based on an initial threshold inquiry regarding the request, with the optimal number of data indicators being determined based on a phone requesting the access to the financial account. A risk score is generated based on a weighting for each of the plurality of data indicators included within the optimal number, with the weighting representing a confidence in the accuracy of each of those data indicators. Access to predetermined actions by the client is granted based on the risk score.