Knowledge Distillation for Fraud Detection Models
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Solution Overview
Problem
Existing risk or fraud detection models in the payment industry face limitations in generalization ability and interpretability, with simpler models struggling to capture complex patterns while maintaining low latency and computational resource efficiency, and more complex models lacking real-time decision-making capabilities and interpretability.
Innovation Solution
A method that incorporates knowledge from more complex models into simpler models by training a second model using a loss function dependent on the output of an intermediate layer of a first model, allowing for improved generalization and interpretability while maintaining ease of deployment, using a combination of deep neural networks and logistic regression models.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If simpler models are used for fraud detection, then deployment ease and interpretability are improved, but generalization ability and capacity to capture complex patterns deteriorate
Solution Approach 1:
The model is divided into two distinct parts: a complex teacher model that captures sophisticated patterns and a simpler student model that is deployed for real-time predictions. The teacher model handles complex feature learning while the student model handles deployment, separating the capabilities needed for pattern recognition from those needed for efficient operation.
Solution Approach 2:
Training data generated by the teacher model serves as an intermediary that transfers knowledge from the complex model to the simpler model. This intermediate representation allows the student model to learn from the teacher's expertise without directly inheriting its complexity, enabling knowledge transfer while maintaining deployment simplicity.
2Use of energy by moving object
If simpler models are used for fraud detection, then computational resource usage and latency are improved, but generalization ability and pattern recognition capacity deteriorate
Solution Approach 1:
The complex teacher model performs computationally intensive work during the training phase to generate enriched training data. This preliminary action transfers complex pattern recognition capabilities to the simpler student model, which then handles real-time predictions with minimal computational resources, separating heavy lifting from runtime operations.
Solution Approach 2:
The student model creates a simplified copy of the teacher model's knowledge through training on teacher-generated data. Rather than deploying the full complex model, a streamlined version is created that replicates the essential predictive capabilities while consuming fewer computational resources during inference.
3Adaptability or versatility
If more complex models are used for fraud detection, then generalization ability is improved, but interpretability and ease of explanation deteriorate
Solution Approach 1:
The system segments the modeling function into two roles: the complex teacher model that achieves high generalization through sophisticated architectures, and the simpler student model that provides interpretability for deployment. This segmentation allows each component to optimize for its specific strength while working together as a unified system.
Solution Approach 2:
Training data acts as an intermediary that carries knowledge from the complex teacher model to the simpler student model. This intermediate representation preserves the generalization capabilities learned by the teacher while enabling the student to maintain interpretability and ease of explanation required for regulatory compliance and user understanding.
4Adaptability or versatility
If more complex models are used for fraud detection, then capacity to capture sophisticated patterns is improved, but real-time decision-making capability deteriorates
Solution Approach 1:
The complex pattern recognition work is performed in advance during the training phase by the teacher model. The resulting training data encapsulates sophisticated pattern knowledge that the simpler student model can quickly apply during real-time transactions, separating heavy pattern learning from fast runtime decision-making.
Solution Approach 2:
The student model creates a streamlined copy of the teacher's pattern recognition capabilities. Rather than executing the full complex model architecture during real-time transactions, a simplified version is deployed that replicates the essential pattern recognition skills while operating at the speed required for real-time payment processing.
Data Source
AI summary
A system, method, and computer program product for incorporating knowledge from more complex models in simpler models. A method may include obtaining first training data associated with a first set of features and second training data associated with a second set of features different than the first set of features; training a first model based on the first training data and the second training data; and training a second model, using a loss function that depends on an output of an intermediate layer of the first model and an output of the second model, based on the second training data.