Adaptive Authentication System Fraud Detection

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Solution Overview

Problem

Current risk-based authentication systems are time-consuming and expensive in identifying transaction attributes correlated with fraudulent activities, necessitating an improvement in fraud detection techniques for online transactions.

Innovation Solution

An adaptive authentication system employing unsupervised machine learning operations to analyze user data, identify features of authentication behavior, and adjust the authentication process to differentiate between legitimate and fraudulent activities, thereby improving risk scoring and fraud detection.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Measurement precision

If manual investigation of transactions is used to identify fraudulent patterns, then fraud detection accuracy can be improved, but the process becomes time-consuming and expensive

Engineering Contradiction:

Improvefraud detection accuracyVSAvoidtime required for pattern identification

Core Design Contradiction:

Measurement precisionVSLoss of time

Solution Approach 1:

The system performs self-learning by automatically analyzing transaction data to identify fraudulent patterns without requiring manual investigation. The machine learning engine continuously trains on transaction data, enabling the system to autonomously improve fraud detection accuracy while eliminating the time-consuming manual pattern identification process

Inventive Principle:

Principle #25Self-service

Solution Approach 2:

The patent replaces the manual mechanical process of fraud pattern identification with an automated machine learning system. The machine learning engine uses algorithms to automatically detect patterns in transaction data, substituting human analysts and manual investigation processes with computational methods that are both faster and more scalable

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

2Reliability

If traditional risk-based authentication systems are used, then fraud detection capability is provided, but the systems lack adaptability to emerging fraud patterns

Engineering Contradiction:

Improvefraud detection capabilityVSAvoidadaptability to emerging fraud patterns

Core Design Contradiction:

ReliabilityVSAdaptability or versatility

Solution Approach 1:

The system transitions from static risk rules to dynamic adaptive authentication. The machine learning engine continuously learns from new transaction data and automatically updates authentication decisions in real-time, allowing the system to adapt to emerging fraud patterns while maintaining reliable fraud detection capability

Inventive Principle:

Principle #15Dynamics

Solution Approach 2:

The system implements continuous feedback loops where authentication outcomes and transaction data are fed back into the machine learning engine. This feedback mechanism enables the system to learn from both successful and unsuccessful authentication attempts, continuously improving its ability to detect emerging fraud patterns while maintaining detection reliability

Inventive Principle:

Principle #23Feedback

Data Source

PatentUS8973096B1Fraud detection in adaptive authentication systems

Publication Date: 2015.03.03 EMC IP HLDG CO LLC

AI summary

There is disclosed some techniques for processing an authentication request which includes a user identifier and current user data. In one example, the technique comprises receiving the authentication request at an adaptive authentication system which includes a database having a set of entries with each entry of the set of entries including an identifier and previous user data in connection with previous authentication requests. The adaptive authentication system constructed and arranged to perform an adaptive authentication operation on the authentication request as well as an unsupervised machine learning operation on the authentication request.