The present work aims to fill the gaps in existing research on the application of machine learning in fraud detection and management in the banking sector. It provides a theoretical perspective on the evolution of algorithms, highlights practical aspects, and derives relevant metrics for evaluating their performance on unbalanced data. In the growing context of artificial intelligence, the adoption of an innovative, systematic approach to studying fraud in banking transactions through advanced machine learning algorithms is completely positive for the overall accuracy and effectiveness of risk management and has really practical and applied significance. The proven methodology (Classification and Regression Trees, Gradient Boosting, and Extreme Gradient Boosting) was tested on nearly 1.5 million in the banking sector, confirming the observations related to the application of fundamental assessments and specialized statistical methods through machine learning algorithms, demonstrating superior discriminatory power compared to classical models. The development provides valuable insights for managers, researchers, and policymakers aiming to strengthen the security and resilience of banking systems in times of evolving financial fraud challenges.

The article highlightsthe importance and added value of some machine learning algorithms in assessing default probability. The results of the research highlight the discriminative ability added to many other essential aspects of machine learning in assessing credit risk. These aspects can be identified as specific opportunities and challenges. As for the discriminative ability regarding the analysed sample, the results prove the superiority of machine learning over the traditionally established and known models. For individual business organizations with exposures to credit risk, machine learning could contribute to reducing the credit losses with larger volumes of business transactions.

The article highlightsthe importance and added value of some machine learning algorithms in assessing default probability. The results of the research highlight the discriminative ability added to many other essential aspects of machine learning in assessing credit risk. These aspects can be identified as specific opportunities and challenges. As for the discriminative ability regarding the analysed sample, the results prove the superiority of machine learning over the traditionally established and known models. For individual business organizations with exposures to credit risk, machine learning could contribute to reducing the credit losses with larger volumes of business transactions.