Machine Learning Fraud Detection Using Relational Graph Convolutional Networks
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Solution Overview
Problem
Existing methods fail to effectively identify and prevent fraudulent transactions in-store and online, leading to financial losses for both customers and retailers, as they lack efficient mechanisms to differentiate legitimate from fraudulent activities in real-time.
Innovation Solution
The implementation of a machine learning-based system that generates a trust score for transactions by analyzing customer and device data using relational-graph convolutional networks (R-GCN) and ensemble supervised learning processes, allowing for the real-time detection and prevention of fraudulent activities by determining whether a transaction should be allowed or denied based on this score.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If machine learning processes are implemented to detect fraudulent transactions, then fraud detection accuracy is improved, but system complexity increases
Solution Approach 1:
The fraud detection system is segmented into multiple specialized machine learning models: R-GCN for analyzing customer-device relationship graphs, ensemble supervised learning models for transaction pattern recognition, and trust score generation components. Each model handles specific aspects of fraud detection, improving overall accuracy while managing complexity through modular architecture.
Solution Approach 2:
A trust score mechanism is introduced as an intermediary that synthesizes outputs from multiple complex machine learning models into a single interpretable metric. This trust score serves as a mediator between the complex analytical processes and the final fraud determination, simplifying the decision-making process while maintaining high detection accuracy.
2Speed
If real-time analysis of customer and device data is performed, then fraud detection speed is improved, but computational resource consumption increases
Solution Approach 1:
The system performs preliminary actions by pre-processing customer and device data into structured formats suitable for machine learning analysis. Relationship graphs are pre-computed and stored, allowing the R-GCN model to quickly analyze fraud patterns without performing heavy computations during real-time transaction processing, thus reducing computational resource consumption while maintaining detection speed.
3Reliability
If comprehensive customer and device data is analyzed, then fraud detection reliability is improved, but data processing time increases
Solution Approach 1:
The system extracts only the most relevant features from comprehensive customer and device data for fraud analysis. The R-GCN model focuses on extracting relationship patterns between customers and devices, while the ensemble models extract transaction behavior features. This selective extraction maintains detection reliability by focusing on critical fraud indicators while significantly reducing data processing time by excluding irrelevant information.
Data Source
AI summary
This application relates to apparatus and methods for identifying fraudulent transactions. The embodiments may employ machine learning processes to detect fraudulent activity. In some examples, a computing device determines customer data and device data for a customer and device involved in a transaction. The customer data may include previous transactions by the customer, and the device data may include previous transactions involving the device. The computing device generates features based on the customer data and the device data, and applies one or more machine learning models to the generated features to generate a trust score. The trust score is indicative of how likely a transaction is to be fraudulent. In some examples, the transaction is not allowed if the trust score is beyond a threshold. In some examples, the computing device trains the machine learning models based on customer data and device data for a plurality of customers and devices.