Hybrid Quantum-Classical Fraud Detection with Feature Selection
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Solution Overview
Problem
Current fraud detection systems face challenges in balancing false positives and false negatives due to imbalanced datasets, leading to monetary losses and customer loss, as classical machine learning methods struggle to accurately predict fraudulent transactions in high-volume financial transactions.
Innovation Solution
A hybrid classical-quantum ensemble method is developed, combining classical machine learning with quantum machine learning using a feed-forward feature selection algorithm and quantum kernel estimates to enhance the accuracy of fraud prediction models by leveraging entanglement for feature importance extraction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If classical machine learning methods are used for fraud detection, then the system can process high-volume transactions, but the accuracy of fraud prediction deteriorates due to imbalanced datasets leading to false positives and false negatives
Solution Approach 1:
The patent combines quantum machine learning with classical machine learning to create a hybrid system. The quantum component processes the imbalanced fraud detection task while the classical component handles general transaction processing, allowing the system to leverage quantum advantages for specific subtasks without sacrificing overall processing capability.
Solution Approach 2:
The patent changes the computational parameters by introducing quantum computing resources to process the imbalanced dataset. This parameter change enables the system to achieve better accuracy on fraud detection by utilizing quantum algorithms that can handle class imbalance more effectively than classical methods.
2Object-generated harmful factors
If the sensitivity threshold is set high to reduce false positives, then genuine transactions are protected, but fraudulent transactions may be missed increasing false negatives
Solution Approach 1:
The patent creates multiple copies of the fraud detection model with different sensitivity thresholds. By running parallel quantum and classical models, the system can evaluate transactions through multiple lenses and aggregate results, effectively copying the detection process to overcome the trade-off between false positives and false negatives.
3Measurement precision
If quantum machine learning is used to improve fraud detection accuracy, then prediction precision improves, but system complexity increases
Solution Approach 1:
The patent segments the fraud detection system into distinct quantum and classical components. Each component handles specific aspects of the detection task, allowing the complex quantum processing to be isolated and managed separately from the classical transaction processing infrastructure.
Solution Approach 2:
The patent introduces an intermediary layer that bridges quantum and classical computing systems. This mediator handles data transformation, result aggregation, and coordination between the two different computational paradigms, managing the complexity of integrating quantum resources into existing classical systems.
Data Source
AI summary
A computer-implemented system, platform, method and computer program product for optimizing a data analytics fraud prediction/detection pipeline that includes a combination of a classical machine learned classifier model with a quantum machine learned model to optimize the performance of the fraud prevention model. The feature selection uses different feature maps: one determined by the classic classifier and the other determined by the quantum model implementation that exploits the entanglement quantum property. The quantum method can include a quantum support vector machine implementing a built feature forward algorithm that uses a quantum kernel estimate for feature mapping. This quantum model can be run on a quantum computer or quantum simulator that can run a quantum algorithm built for extracting feature importance. A decision classifier is further developed to decide which model output prediction is more correct in the instance there is a disagreement in each of the ensemble model's activity determination.