Machine Learning Fraud Detection via Activity Normalization
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Solution Overview
Problem
Detecting first party fraud instances is challenging due to their subtle nature, as fraudulent activities may appear legitimate and are often difficult to identify without clear reports from victims.
Innovation Solution
A computing device aggregates and normalizes activity instances from different sources using an API, then inputs these normalized attributes into a machine learning model trained to predict the likelihood of future fraud instances, sending alerts and taking remedial actions when the likelihood exceeds a threshold.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If traditional fraud detection methods are used to monitor user activities, then fraud can be detected after losses become apparent, but early detection of subtle fraudulent activities remains difficult
Solution Approach 1:
The system performs preliminary actions by continuously collecting and analyzing user activity data before fraud losses become substantial. The machine learning model predicts future fraud instances by examining patterns in historical activity data, enabling early intervention before traditional detection methods would trigger.
Solution Approach 2:
The system implements feedback mechanisms where the machine learning model continuously learns from detected fraud instances and adjusts its predictions. The model uses feedback from confirmed fraud cases to improve its ability to detect subtle fraudulent patterns earlier, creating a self-improving detection system.
2Measurement precision
If multiple data sources are aggregated to improve fraud detection accuracy, then detection capability improves, but system complexity increases
Solution Approach 1:
The machine learning model serves as a universal component that handles multiple data sources and fraud detection tasks simultaneously. Rather than creating separate detection systems for different data types, the model processes diverse activity data through a unified framework, reducing overall system complexity while maintaining high detection accuracy.
Solution Approach 2:
The system introduces an intermediary layer (the machine learning model) that sits between multiple data sources and the fraud detection decision-making process. This intermediary normalizes and integrates data from various sources, simplifying the complexity of handling multiple inputs while improving detection accuracy through comprehensive analysis.
3Reliability
If machine learning models are trained on normalized activity data to predict future fraud, then early fraud prediction is enabled, but data processing complexity increases
Solution Approach 1:
The system applies parameter changes by normalizing activity data into standardized formats suitable for machine learning processing. This normalization transforms raw activity data into consistent parameters that the model can reliably analyze, improving prediction reliability while managing processing complexity through systematic data transformation.
Data Source
AI summary
Aspects described herein may automatically detect first-person fraud. A computing device may receive activity instances associated with a first user at different times, and aggregate, via an application programming interface (API), the instances by normalizing attributes associated with the activity instances that indicate fraud. The computing device may input the normalized attributes into a machine model to predict a likelihood of a future fraud instance. The computing device may send, based on the likelihood exceeding a threshold, an alert. In this way, fraud instances may be detected promptly.