Agile risk management methodology for decision-making in startup projects based on stock price forecasting

The article is devoted to the topical problem of detecting fraudulent anomalies in financial transactions using machine learning methods. In the context of rapid digital transformation of financial systems and growth in transaction volumes, traditional methods of fraud detection are becoming ineffective, which highlights the urgent need to implement automated and adaptive solutions. The research is based on a step-by-step approach that includes data preparation and processing, building and training classification models, and evaluating their effectiveness. A comparative analysis of seven popular machine learning algorithms was conducted: linear regression, decision trees, random forest, neural networks, gradient boosting, XGBoost, and SVC. The key findings of the study showed that ensemble methods demonstrate the highest effectiveness in detecting fraud: Random Forest, Gradient Boosting, and XGBoost proved to be the most suitable for fraud detection tasks, demonstrating consistently high results. This is especially important given the typical class imbalance (a small number of fraudulent transactions compared to legitimate ones) in real financial data. The effectiveness of the models significantly outperforms the other algorithms considered, indicating their ability to detect complex, non-obvious patterns in the data. The critical importance of correctly configuring model hyperparameters and accounting for class imbalance to achieve maximum accuracy and completeness in detecting fraudulent transactions has been confirmed. This avoids overfitting on the dominant class and increases the system's sensitivity to rare but important fraudulent cases. The practical significance of the study lies in the fact that the proposed approach allows financial institutions to significantly improve operational efficiency, minimize financial losses, and strengthen customer trust. The implementation of such systems provides comprehensive and adaptive protection of the financial system in today's dynamic digital environment. The results of the study confirm the effectiveness of machine learning as a powerful tool for combating financial fraud.