AI Fraud Detection Policy Development
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Solution Overview
Problem
Current fraud detection policies are costly, generalized, and prone to update delays, often neglecting isolated instances of missed fraud, with underlying rules being difficult to derive and modify, leading to inefficiencies and ineffective detection.
Innovation Solution
An AI-based method for automatic fraud detection policy development that extracts features from client data, classifies fraudulent activity, and develops policy rules to create and modify fraud detection models without the need for manual analysis by fraud analysts, allowing for better understanding and updating of policy rules based on missed fraud.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If fraud detection policy is developed by fraud analysts through exhaustive analysis, then detection accuracy is improved, but development cost and time increase
Solution Approach 1:
The system enables self-service by automatically extracting policy rules from data using AI algorithms, allowing the fraud detection model to self-upgrade without requiring continuous manual intervention from fraud analysts for rule extraction
Solution Approach 2:
The patent replaces the mechanical manual analysis process with an AI-based automated system that uses machine learning algorithms to extract policy rules from data, substituting human analysts' manual work with automated computational processes
2Adaptability or versatility
If manual policy rule creation is used, then policy rules can be customized, but the underlying rules become difficult to derive and understand
Solution Approach 1:
The system provides feedback by automatically generating explanations of the policy rules it extracts from data, allowing stakeholders to understand the rationale behind detected fraud patterns and the criteria used in decision-making
Solution Approach 2:
The AI system automatically documents and explains its own extracted policy rules, providing self-explanatory capabilities without requiring external interpretation from analysts
3Reliability
If comprehensive fraud analysis is performed, then detection coverage is improved, but processing cost increases
Solution Approach 1:
The patent replaces expensive manual fraud analysis with automated AI-based processing that uses machine learning algorithms to analyze data at scale, reducing the cost per analysis while maintaining comprehensive coverage
Solution Approach 2:
The system changes the parameters of analysis by using AI algorithms that can efficiently process large volumes of data with varying complexity levels, adjusting computational resources based on the specific fraud patterns being detected
4Reliability
If fraud detection policy is continuously updated, then detection effectiveness is improved, but system complexity increases
Solution Approach 1:
The system performs self-updates by automatically extracting new policy rules from incoming data and integrating them into the fraud detection model, maintaining effectiveness without requiring complex manual update procedures
Solution Approach 2:
The patent implements dynamic rule extraction where the AI system continuously adapts to new fraud patterns by automatically learning and incorporating new policy rules, making the system flexible and responsive to changing threats
Data Source
AI summary
A method of AI based automatic fraud detection policy development is provided. The method includes obtaining client data associated with a plurality of digital accounts. The obtained client data for each of the plurality of digital accounts includes at least one of legitimate activity and fraudulent activity. Features are extracted from the obtained client data. Fraudulent activity is classified in the obtained data. Policy rules associated with the classified fraudulent activity are extracted based on the extracted features. A policy model is developed based on the extracted policy rules.