Sparse Data Augmentation for Hedge Fund Fraud Model Training
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Solution Overview
Problem
Conventional systems face challenges in detecting hedge fund fraud due to the opaque nature of operations, scarcity of labeled data, and intricate fraud schemes, leading to manual review inefficiencies and automated systems prone to overfitting.
Innovation Solution
A system that aggregates and augments hedge fund data from various sources, including regulatory and litigation data, to generate additional training data for a machine learning model, enhancing the model's ability to identify fraudulent activity by improving data granularity and reducing overfitting.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual review is used to detect hedge fund fraud, then detection accuracy can be maintained, but productivity and efficiency deteriorate
Solution Approach 1:
The system creates synthetic copies of fraud data through data augmentation techniques, generating artificial training data that mirrors real fraud patterns. This allows the machine learning model to learn from multiple virtual examples without requiring manual review of each, thereby maintaining detection accuracy while improving productivity
Solution Approach 2:
The patent replaces manual mechanical review processes with an automated machine learning system. The model automatically analyzes hedge fund data, identifies fraud patterns, and generates detections without human intervention, thus maintaining accuracy while dramatically improving efficiency
2Productivity
If automated machine learning systems are used to detect fraud, then productivity improves, but reliability deteriorates due to overfitting
Solution Approach 1:
The system dynamically adjusts data augmentation parameters and model training parameters to prevent overfitting. By varying the intensity and type of data transformation during training, the model learns robust fraud detection capabilities that generalize well to new data while maintaining high productivity
Solution Approach 2:
The patent implements feedback mechanisms where model performance is continuously monitored and used to adjust training parameters. This feedback loop prevents overfitting by identifying when the model is memorizing training data rather than learning general patterns, thus maintaining reliability while preserving efficiency
3Reliability
If more training data is used to improve model accuracy, then reliability improves, but device complexity and computing resources increase
Solution Approach 1:
Instead of collecting more real-world fraud data, the system creates virtual copies through data augmentation. This generates additional training data that expands the effective dataset size without requiring proportional increases in computing resources or data collection infrastructure
Solution Approach 2:
The patent transforms existing training data through parameter changes such as noise injection, data transformation, and synthetic generation. This multiplies the effective information content from existing data without requiring additional data collection, thus improving accuracy while controlling computing resource requirements
Data Source
AI summary
A system may access a set of training data and determine a timeframe associated with a positively labeled data item of the training data. A system may generate at least two new positively labeled data items based on the positively labeled data item to generate augmented training data. A system may train a machine learning model by applying the augmented training data as input to a machine learning model, and modifying a weight of the machine learning model.