Fraud detection using machine learning

computer engineering

One of the key problem faced by different industry domains is fraud detection, especially in finance, e-commerce and insurance where companies bears huge financial loss due to fraudulent activities. This research focuses on how machine learning can help in improving the accuracy and intelligence of fraud detection systems. We will introduce typical fraud detection and show the limitation of these methods, so that leads to the method update into machine learning algorithm. In our analysis, we show how models from different learning families (like decision trees, neural networks and anomaly detection) are trained to detect patterns that could help pinpoint fraudulent behaviour or risky transactions. Moreover, we look into what makes feature engineering, data preprocessing and model evaluation as important components in developing a strong fraud detection system. The real-world use-cases of machine-learning-helped fraud detection and the challenges that ruin the expectation, courtesy — case studies in dealing with imbalanced data and changing patterns in fraudulent activities. Such research highlights the capacity of machine learning to modernize fraud detection approaches, and offers implications for future study to better this detection capability and effectively tackle new threats. In this paper, we survey and compare the performance of different machine learning algorithms (such as decision tree, support vector machine (SVM), random forest, deep learning models) for Given fraud detections across various domains such as finance business transactions, healthcare domain activities & e-commerce portal. ML, powered by supervised and unsupervised learning techniques can find anomalies, predict or different types of fraud transactions, and even adjust to previously unidentified patterns of fraud. It delves deeper into problems such as imbalances in the datasets and interpretability of models explaining approaches such as oversampling, engineering features and explainable AI. The experiment results show that ML dramatically raised fraud detection accuracy and efficiency, even used to decrease false positives while detecting on time predictions.

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