## 4 Discussion  
Finding the irregularities in financial documents such as ledger books of companies is a tricky and complicated task. The complexity of the task is mainly caused by the fact that ledger books and sometimes other financial documents contain very many records, which makes it very difficult to analyze them manually. Efficient checking of these records by an auditor or other financial specialist requires finding a way to reduce the number of records. Additionally, it is very important to do this as soon as possible. The majority of attempts to automate this process were concentrated on using the numeric information from the financial records (sums of money, codes of operation, etc.). Such source of information as accountant comments was essentially left untouched. The reason for this is quite obvious: being informal and usually short comments are not seen as an easy source of information. Attempts to introduce accountant comments as the source of information to identify irregular financial operations together with NLP techniques is one of the main contributions of this study. The comments have obvious advantages: they are typically introduced when making the records and hence have the potential for rapid analysis and likely for the fast finding of probable errors--deliberate or accidental. The first group of experiments aimed at recognizing the type of financial operation associated with the comment. This could be one of the steps to identify potential errors in the ledger book. A preliminary evaluation showed that vocabularies associated with different financial operations are essentially different. This was proved by the frequency analysis of words used in comments with different operations and by evaluating the cosine similarity between the vocabularies (usually, cosine similarity was below 0.1 except in two cases where those values were 0.3 and 0.28). This suggested applying a tf-idf-based recognizer to recognize the type of financial operation using the comments. The cross-validation using recognition showed that operation could be identified with very high accuracy (for different types of operations, the accuracy was in the range of 93-98 percent). The average accuracy was 96.4 percent. Even higher accuracy has been achieved using the GloVe word embedding method. In this case, recognition accuracy varied between 93 and 100 percent, while the average accuracy was 97.4 percent. Most often, the biggest number of financial operations are sales and purchase operations. Checking the correctness of all such operations is especially tricky due to large numbers. One of the potential ways to reduce the number of them is to use comments too. The main purpose of these comments is to help the accountants understand what the operation was aimed at, and this fact needs to be exploited. The premise of the study was that similar comments would likely be associated with similar financial parameters. Many operations performed and registered in the ledgersas sale and purchase operations could be called routine operations. This is especially true for big companies where similar sales and purchases or some other operations are performed on a regular basis. The hypothesis that similar financial operations will be associated with similar text comments was formulated and tested. The similarity in this context could mean similar amounts of sum related to the comment. Finding semantically similar comments associated with entirely different sums of money could indicate the irregularities--deliberate or intentional--in financial records. It is hard to expect to find all irregularities, but it could be expected to reduce substantially the number of records that need to be checked manually by human specialists. Relatively simple comment processing--finding the routine operations or unique comments--reduced the number of operations that may need to be checked by more than 90 percent.