Synthetic Fraud Data Generation for ML Model Training
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Solution Overview
Problem
Machine learning models in the retail space, particularly for detecting fraudulent activity, often suffer from insufficient training data, leading to higher error rates due to rare event scenarios with limited labeled data, making it difficult to recognize new patterns of fraudulent activity.
Innovation Solution
A system and method that involves clustering training data based on attributes, determining distance metrics, and generating sub-clusters to identify unlabeled samples using reward values and sampling rates, which are then labeled and used to train machine learning models, thereby enhancing the training data availability and model performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If machine learning models are trained with limited labeled data from rare event scenarios, then the model can be deployed for fraud detection, but the model suffers from insufficient training data leading to higher error rates
Solution Approach 1:
The system performs preliminary actions by generating synthetic fraudulent transaction samples before actual fraud detection is needed. It creates artificial training data that simulates rare fraud scenarios, allowing the model to be pre-trained on these synthesized examples. This preliminary data generation enables the model to learn fraud patterns even when real labeled fraud data is scarce, thereby improving model reliability without requiring large volumes of actual fraudulent transaction records.
2Reliability
If machine learning models are trained with more labeled data, then model performance improves, but it becomes difficult to obtain sufficient labeled data for rare fraudulent activities
Solution Approach 1:
The system creates copies of existing transaction data by generating synthetic fraudulent samples that mimic real fraud patterns. Instead of requiring additional real labeled fraud data, the system copies and transforms legitimate transaction data into artificial fraud scenarios by injecting fraudulent patterns. This copying approach enables abundant training data generation without the need for difficult manual labeling of rare fraud events, as the synthetic data automatically carries the fraud labels.
3Adaptability or versatility
If machine learning models use traditional training approaches with limited data, then the training process is simple, but the models cannot recognize new patterns of fraudulent activity
Solution Approach 1:
The system changes key parameters of transaction data by manipulating features such as transaction amount, timing, location, and merchant category to create synthetic fraud scenarios. It systematically varies these parameters to generate diverse fraud patterns that represent different types of fraudulent activities. This parameter transformation approach enables the model to learn multiple fraud patterns from a single set of legitimate transactions, enhancing adaptability without requiring complex external data sources.
4Reliability
If more labeled training data is generated for rare events, then false positive rates decrease, but the process of obtaining and labeling additional data becomes more complex
Solution Approach 1:
The system performs self-service by automatically generating synthetic labeled fraud data without requiring external human annotators or complex data collection processes. It uses automated algorithms to create fraudulent transaction samples with known labels, eliminating the need for manual labeling efforts. This self-service data generation capability reduces false positive rates by providing sufficient training examples while avoiding the complexity of human-in-the-loop labeling workflows.
Data Source
AI summary
Systems and methods for generating training data, and training machine learning models with the generated training data, are disclosed. In some examples, a computing device obtains, from a data repository, training data, wherein the training data comprises labelled samples and unlabeled samples. The computing device generates clusters of the training data based on one or more corresponding attributes of the training data. Further, the computing device determines a distance metric between positively labelled samples and unlabeled samples within each cluster, and generates, for each of the clusters, a plurality of sub-clusters based on the determined distance metrics. The computing device also determines, from each of the plurality of sub-clusters, one or more of the unlabeled samples based on a corresponding reward value and a corresponding sampling rate value. The computing device may train a machine learning model with the determined unlabeled samples from each of the plurality of sub-clusters.