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Abstract:
This paper assess and contrast various algorithms in terms of their effectiveness and efficiency when applied towards identifying fraudulent activities. Our study encompasses several popular algorithms such as Logistic Regression, Decision Trees, Random Forests, Gradient Boosting s GBMs, Neural Networks, and Deep Learning.
We begin by defining the scope and the criteria for evaluating these algorithms based on parameters like precision, recall, F1-score, false positive rate, and overall accuracy. Subsequently, we conduct comprehensive experiments using a large dataset of historical transactions that are labeled as either fraudulent or legitimate, enabling us to test our algorithms under realistic conditions.
s demonstrate that Gradient Boosting s GBMs exhibit superior performance compared to other algorithms in terms of both precision and recall, while also mntning an acceptable false positive rate. GBMs were found particularly effective for detecting complex patterns indicative of fraud, showcasing their robustness agnst noisy data.
On the other hand, while Neural Networks could detect subtle features that may be missed by simpler, they required significantly more computational resources and trning time compared to GBMs or Random Forests. This highlights a trade-off between model complexity, interpretability, and efficiency.
In , our analysis suggests that Gradient Boosting s GBMs are the most efficient choice for fraud detection tasks given their balance of performance metrics and computational efficiency. However, deping on specific business needs, Neural Networks might still have an edge in scenarios where distinguishing among similar fraudulent patterns is critical despite higher costs.
Future research could focus on integrating ensemble methods that combine strengths of differentor exploring novel architectures specifically designed to handle large volumes of high-dimensional transactional data for fraud detection applications. This would potentially lead to even more robust and efficient systems capable of real-time monitoring and proactive risk management in financial services sectors.
Reference:
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Efficient Machine Learning Fraud Detection Algorithms Gradient Boosting Machines for Fraud Prevention Neural Networks vs Traditional ML in Finance Real Time Financial Risk Management Strategies Comparative Study on ML Techniques in Banking Optimization of Computational Resources in Fraud Analysis