Unveiling Deceptive Patterns: AI-Driven Fraud Detection in Healthcare Finance
Keywords:
Healthcare, Financial Analytics, Machine Learning, Deep Learning, Surveys, ModelingAbstract
Background: The healthcare sector gathers a significant amount of health and financial data, and with the increasing prevalence of electronic payments, credit card fraud monitoring has become a financial burden for service providers. Continuous improvement of fraud detection systems is necessary to mitigate the financial losses caused by ongoing fraudulent activities. Phishing and virus-like Trojans are commonly employed to steal credit card data, highlighting the need for effective fraud detection systems.
Aim: This paper aims to enhance credit card fraud detection systems using machine learning and deep learning algorithms, including Naive Bayes, Logistic Regression, K-Nearest Neighbour (KNN), Random Forest, and Sequential Convolutional Neural Networks. These algorithms are applied to train on both standard and abnormal transaction features, enabling credit card fraud detection. The effectiveness of these methods is evaluated using public data.
Methodology: The study employs machine learning and deep learning algorithms to train on various transaction features, including standard and abnormal patterns. The algorithms utilized are Naive Bayes, Logistic Regression, K-Nearest Neighbour (KNN), Random Forest, and Sequential Convolutional Neural Networks. These algorithms are trained using the collected data and tuned to identify fraudulent credit card transactions.
Results: The accuracy of the different algorithms in detecting credit card fraud was evaluated. The results indicate that Naive Bayes achieved an accuracy of 96%, Logistic Regression achieved 94.9%, K-Nearest Neighbour (KNN) achieved 95.89%, Random Forest achieved 98.58%, and Sequential Convolutional Neural Networks achieved 95.8%.
Conclusion: Comparative analysis of the various algorithms revealed that the K-Nearest Neighbour (KNN) method outperformed the others in terms of accuracy in detecting credit card fraud. This finding highlights the effectiveness of KNN in identifying fraudulent transactions within the healthcare sector. By implementing these improved fraud detection systems, healthcare service providers can better safeguard against credit card fraud, minimizing financial losses and protecting the financial security of the organizations and their patients.
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