Incremental Model Refresh for Emerging Fraud Pattern Detection
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Solution Overview
Problem
Existing machine learning models struggle to quickly adapt to emerging patterns in fraudulent transactions due to the inefficiency of conventional retraining methods, which consume substantial computer resources and time, leading to potential losses and adverse consequences.
Innovation Solution
A machine learning model framework that enables incremental training by selectively using portions of transaction data, employing clustering techniques, generating synthetic data, and incorporating previous model versions to efficiently update the model, reducing resource and time consumption.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If conventional retraining methods are used to update machine learning models, then the model can learn new patterns, but the process consumes substantial computer resources and time
Solution Approach 1:
The patent segments the retraining process into incremental updates rather than complete retraining. The system divides training data into chunks and applies sequential incremental training, allowing the model to adapt to new patterns in stages. This is evidenced by the use of 'incremental training' and 'continuous learning' mechanisms that process data in manageable portions, reducing the time and resource burden of full retraining while maintaining adaptability to emerging fraud patterns.
Solution Approach 2:
The system performs preliminary actions by pre-processing and selecting relevant training data before the actual training process. The patent describes mechanisms for 'data selection,' 'feature engineering,' and 'training data preparation' that occur beforehand, allowing the incremental training to proceed more efficiently. This preliminary preparation reduces the computational burden during the actual model updating process.
2Adaptability or versatility
If conventional retraining methods are used to update machine learning models, then the model can learn new patterns, but substantial computer resources are consumed
Solution Approach 1:
The patent applies partial action by using only the necessary portion of training data for incremental updates rather than processing the entire dataset. The system selectively applies new data chunks to the model, using 'data sampling' and 'feature selection' techniques to process only the most relevant information. This reduces computational resource consumption while still achieving effective model adaptation to new fraud patterns.
Solution Approach 2:
The system changes training parameters during incremental updates, adjusting learning rates, batch sizes, and optimization settings based on the specific update context. The patent describes 'hyperparameter tuning' and 'adaptive learning rates' that modify training parameters dynamically, allowing efficient resource utilization while maintaining model adaptability to emerging patterns.
3Reliability
If complete retraining is performed to maintain model accuracy, then the model can handle evolving fraud tactics, but the adaptation speed is too slow
Solution Approach 1:
The patent implements continuous learning mechanisms that allow the model to adapt continuously rather than through periodic complete retraining. The system uses 'continuous incremental training' where the model continuously incorporates new patterns as they emerge, maintaining both high accuracy and fast adaptation speed. This continuous action ensures the model stays current with evolving fraud tactics without the delays associated with batch retraining cycles.
Solution Approach 2:
The system employs dynamic training strategies where the incremental update frequency and intensity adapt based on the detected fraud pattern complexity and urgency. The patent describes 'adaptive training schedules' and 'dynamic learning rates' that adjust the training process in real-time, allowing faster adaptation when new fraud tactics are detected while maintaining accuracy through controlled update rhythms.
Data Source
AI summary
Methods and systems are presented for providing a machine learning model framework that provides an adaptive machine learning model base on providing quick and incremental trainings to the machine learning model. Instead of using the entire available training dataset to train the machine learning model, a subset of the available training dataset that accurately represents the characteristics of the training data set is extracted to be used in each iteration of incremental training. Furthermore, labels of unmatured dataset are imputed to provide additional training datasets that correspond to any emerging pattern. Synthetic training datasets are also generated to mimic datasets that correspond to an emerging pattern to strengthen the machine learning model's ability to recognize the emerging pattern.