## 2 Related Work  
There are numerous studies devoted to finding financial data irregularities or even fraud in the financial documents of business companies. Not surprisingly, the vast majority of these studies are devoted to the purely financial aspects of this problem, e.g., creating new models that can detect financial irregularities in companies' performance, but it must be noted that in recent years more and more often, studies that try to use various computer science-based approaches (e.g., using neural networks or other elements of artificial intelligence) are found. It is necessary to emphasize that most of the authors are using only numeric data available from records made by the company (sums of money, operation Ids, value of assets, and other accounting-related information). A significant class of studies are related to the implementation of machine learning techniques aiming to train them to recognize suspicious financial activities. Several such studies are worthy of mention. An influential, widely cited study has been presented by [PERSON] and [PERSON] [2]. They investigated the application of machine learning methods to detect fraud using financial statement audits. As a result of the study, the authors proposed a taxonomy that could be used as a guideline for research in this area. The taxonomy which found and combined known fraud scheme patterns with those areas that are beneficiaries of productive applications has been developed. [PERSON] et al. [3] explored the effectiveness of several techniques for data mining, to use them to classify firms' fraudulent and non-fraudulent financial statements. As a result, factors associated with fraudulent statements were identified. Neural networks, decision trees, and belief networks were used to assist the auditor and were able to achieve relatively high accuracy. [PERSON] et al. [4] used an even wider set of machine-learning techniques for similar purposes. In their study, such techniques as multilayer neural networks, support vector machines, genetic programming, group method of data handling, logistic regression, and probabilistic neural network were used to identify companies that had committed financial statement manipulations. The methods were tested on a dataset from 202 Chinese companies. This study was essentially concentrated on features that could be used to identify fraud. [PERSON] et al. [5] used most of the popular machine-learning techniques to discover fraudulent financial statements. The methods used were a decision tree, artificial neural network, naive Bayes, support vector machine, and Bayesian belief network classifiers. The study was designed in two stages. First, they mainly concentrated on finding financial fraud within a single enterprise with the help of machine learning. Then the focus was given to detecting such fraud within a whole business using diverse data. [PERSON] and colleagues [6] developed a neural network-based mathematical model for comparing risky and fair companies selected from among small market capitalization companies in Malaysia. Ten financial ratios were used as fraud risk indicators. They were applied to predict fraudulent financial reporting using neural networks. Their study showed that the proposed neural networks-based method works better than other statistical techniques often used to predict fraudulent financial reporting: higher accuracy was achieved. The work of [PERSON] and [PERSON] [7] is important since they well demonstrated the use of ensemble-based methods and that they are superior in true positive rate (fraudulent cases are recognized as fraudulent correctly). As was mentioned above, all these studies used only numeric financial parameters used in financial accounting to detect financial irregularities. The importance of the studies is in the fact that they showed that by using machine learning techniques, it is possible to achieve a relatively high detection rate of suspicious financial activities.