Towards Secure Finance: Harnessing Ensemble Learning Techniques for Fraud Detection in Financial Statements

In this study, we will give you a detailed overview of various machine learning algorithms for fraud detection. We will discuss the following methods, including neural networks, logistic regression, decision trees, random pastures, support vector machines, and ensemble methods like begging and bragging. Looking at the applications of these algorithms in various fraud detraction situations, such as online transaction fraud, credit card fraud, and assurance fraud. Furthermore, utilizing feature engineering techniques has a big influence on raising the fraud detection models' accuracy. Practical applications of feature choices, dimensionality reductions, and anomaly detection techniques are covered in detail. tackling the challenges associated with fraud detections as well, such skewed datasets, idea drift, and interpretability of models talking about strategies to overcome these challenges, such as explainable AI approaches, online learning, and resampling techniques. In summary, it is clear that machine learning is essential to improving fraud detection skills across many domains. Organizations may greatly enhance their capacity to thwart fraudulent operations and protect their assets by grasping the subtleties of machine learning algorithms and putting them into practice.