Machine Learning Fraud Risk Assessment Without Manual Feature Crafting
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Solution Overview
Problem
Traditional fraud detection models rely on manually crafted features, which are time-consuming to create and maintain, rigid, and prone to interpretation biases, making them difficult to adapt to different time windows and scenarios.
Innovation Solution
A system utilizing machine learning models, such as recurrent neural networks and transformers, to analyze sequence transaction data, generating risk scores by tracking temporal features and detecting fraudulent patterns without manual feature crafting.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If traditional fraud detection models use hand-crafted features, then the model structure is simple and easy to interpret, but the feature creation is time-consuming and rigid
Solution Approach 1:
The system uses machine learning models to automatically generate and optimize features from transaction data, eliminating the need for manual feature crafting. The ML models self-adjust and optimize feature extraction based on patterns in the data, making the system self-sufficient and reducing time consumption associated with manual feature development and maintenance.
Solution Approach 2:
The patent replaces manual mechanical feature creation processes with automated machine learning algorithms. Instead of data scientists manually crafting features, the system uses ML models to automatically extract, transform, and optimize features from raw transaction data, substituting the mechanical manual process with an automated intelligent system.
2Ease of operation
If hand-crafted features are used, then the model is easier to control and interpret, but the features are specific and rigid making experimentation difficult
Solution Approach 1:
The system dynamically adjusts feature extraction and model parameters based on the data and task requirements. The machine learning models can adapt their behavior to different time windows, transaction types, and fraud scenarios automatically, providing versatility without requiring manual reconfiguration of features for each specific application.
Solution Approach 2:
The patent employs machine learning models that can change parameters automatically based on learned patterns from data. Instead of fixed hand-crafted features, the ML models adjust their internal parameters and feature representations to optimize performance across different time windows and fraud detection scenarios, enabling adaptability while maintaining interpretability through model explanations.
3Productivity
If machine learning models are used to analyze sequence transaction data, then the system becomes more adaptive and efficient, but the model complexity increases
Solution Approach 1:
The system segments the fraud detection process into distinct components: data collection, feature extraction by ML models, risk scoring, and decision-making. This segmentation allows each component to be optimized independently, managing overall system complexity while improving productivity. The ML models process transaction sequences in manageable segments, enhancing efficiency without overwhelming complexity.
4Ease of operation
If manually created features are used, then the feature engineering is more controllable, but the interpretation is heavily impacted by data scientist thinking biases
Solution Approach 1:
The machine learning models perform feature extraction and pattern recognition autonomously without human intervention in the feature engineering process. This self-service approach eliminates the influence of data scientists' thinking biases on feature selection and interpretation, as the models objectively analyze data patterns based solely on the transaction sequences and learned relationships, providing unbiased and more accurate interpretations.
Data Source
AI summary
Systems and methods for assessing fraud risk based on sequence transaction data using machine learning are disclosed. In some embodiments, a disclosed method includes: receiving, from a computing device, a risk assessment request regarding a user device; generating sequence data based on a time series of transactions associated with the user device; computing, using at least one machine learning model, risk score data of the user device based on the sequence data; and transmitting, in response to the risk assessment request, the risk score data of the user device to the computing device.