Application of ML-technologies for automation of financial monitoring and effective detection of anomalies in accounting

The article discusses the use of machine learning methods to prevent fraud and identify anomalies in accounting. Modern organizations face the need to process large amounts of data, which makes it more difficult to detect errors and fraud in traditional ways. Machine learning, in particular, the methods of the nearest neighbor (KNN) and isolation forests, make it possible to automate the process of data analysis and effectively detect suspicious transactions. Practical examples of the use of these methods in large companies are given, and the prospects and challenges of introducing machine learning technologies into accounting practice are discussed.