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The Application of Machine Learning to Dispute Regulation
This article addresses the potential benefits of using machine learning in resolving and settling financial fraud disputes. More specifically, this paper posits that applying machine learning to the dispute resolution and settlement process can increase the efficiency and accuracy of detecting and resolving financial fraud. Financial institutions may spot patterns and trends suggesting fraudulent conduct and resolve disputes more efficiently by analyzing massive amounts of data with machine learning algorithms. The article also emphasizes the necessity for human control and transparency in applying machine learning to finance.
Lokanan, M.E. Incorporating machine learning in dispute resolution and settlement process for financial fraud. J Comput Soc Sc (2023).
The Application of Machine Learning to Money Laundering Sanctions
This paper looks at how machine learning techniques and artificial neural networks can be used to forecast money laundering sanctions. Machine learning algorithms can uncover patterns and trends that may signal potential money laundering behaviour by evaluating massive amounts of data, including financial transactions and other pertinent elements. The paper also examines the difficulties associated with using machine learning in the financial sector, such as data quality and privacy problems. Notwithstanding these obstacles, the article contends that machine learning AI can potentially increase the accuracy in predicting money laundering sanctions.
Lokanan, M. E. (2023). Predicting money laundering sanctions using machine learning algorithms and artificial neural networks. Applied Economics Letters, https://doi.org/10.1080/13504851.2023.2176435
The Determinants of Investment Fraud: A Machine Learning and Artificial Intelligence Approach
The article explores the application of machine learning and artificial intelligence to identify indicators of investment fraud. By examining data on multiple criteria, such as the characteristics of the fraudsters and their victims, the tactics used to perpetrate the fraud, and the regulatory and enforcement contexts, machine learning and AI algorithms can uncover patterns and trends that may indicate investment fraud. The article concludes by arguing that machine learning and artificial intelligence can improve the detection and prevention of investment fraud by identifying important risk variables and facilitating more effective enforcement and regulatory action.
Lokanan, M. (2022). The determinants of investment fraud: A machine learning and artificial intelligence approach. Frontiers in Big Data, 5, 91, https://doi.org/10.3389/fdata.2022.961039
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