Machine Learning Patch Ranking for Transaction Fraud Prevention
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Solution Overview
Problem
As systems become larger and more complex, they are likely to have increasing security vulnerabilities that, if not mitigated, can lead to substantial losses, particularly in the context of transaction fraud.
Innovation Solution
A method and system utilizing a machine learning model to analyze completed transaction records to identify security vulnerabilities and their correlation with fraudulent activity, generating a vulnerability score, and recommending patches to mitigate these vulnerabilities, with a feedback mechanism to refine patch effectiveness.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If systems become larger and more complex to handle increasing transaction volumes, then productivity increases, but security vulnerabilities increase leading to higher risk of fraudulent activity
Solution Approach 1:
The system performs preliminary analysis by applying machine learning models to historical processed records before fraudulent transactions occur. This enables early identification of security vulnerabilities and correlation with fraudulent activity patterns, allowing preventive measures to be taken in advance rather than reacting after system complexity has already created vulnerabilities.
Solution Approach 2:
The system generates a security vulnerability score based on correlation values derived from analyzing processed records against the machine learning model. This feedback mechanism continuously monitors and quantifies the relationship between system operations and potential fraudulent activity, enabling dynamic adjustment of security measures as the system evolves in complexity.
2Measurement precision
If traditional security analysis methods are used, then device complexity remains low, but measurement precision of security vulnerabilities and their correlation with fraudulent activity is insufficient
Solution Approach 1:
The patent replaces traditional mechanical or manual security analysis methods with a machine learning model. This substitution enables precise measurement of correlation values between security vulnerabilities and fraudulent activity by automatically analyzing patterns in processed records, achieving high measurement precision without proportionally increasing device complexity through manual intervention.
Solution Approach 2:
The system transforms security analysis from qualitative assessments to quantitative measurements by generating correlation values and security vulnerability scores. This parameter change enables precise measurement of vulnerability-fraud relationships by converting complex security patterns into measurable numerical metrics that can be systematically analyzed.
Data Source
AI summary
Disclosed herein are system, method, and computer program product embodiments for process corruption prevention. An embodiment operates by determining security vulnerabilities for an entity and correlation values for the security vulnerabilities by applying completed processed records of the entity to a machine learning model. Each of the correlation values quantifies a relationship strength between a security vulnerability and fraudulent activity. The embodiment further operates by generating a security vulnerability score for the entity using the correlation values and identifying one or more patches for at least one of the security vulnerabilities. The one or more patches may be ranked and the ranking may be revised using a feedback mechanism after the one or more patches are implemented by the entity.