In this article we present an approach to financial data analysis and anomaly discovery. In our view, the assessment of performance management requires the monitoring of financial performance indicators (KPIs) and the characteristics of changes in KPIs over time. Based on this assumption, behavioral change indicators (BCIs) are introduced to detect and evaluate the changes in traditional KPIs in time series. Three types of BCIs are defined: absolute change indicators (BCI-A), relative change indicators (ratio indicators BCI-RE), and delta change indicators (D-BCI). The technique and advantages of using BCIs to identify unexpected deviations and assess the nature of KPI value changes in time series are discussed and illustrated in case studies. The architecture of the financial data analysis system for financial data anomaly detection is presented. The system prototype uses the Camunda business rules engine to specify KPIs and BCI thresholds. The prototype was successfully put into practice for an analysis of actual financial records (historical data).

Finding not typical financial operations is a complicated task. The difficulties arise not only due to the sophisticated actions of fraudsters but also because of the large number of financial operations performed by business companies. This is especially true for large companies. It is highly desirable to have a tool to reduce the number of potentially irregular operations significantly. This paper presents an implementation of NLP-based algorithms to identify irregular financial operations using comments left by accountants. The comments are freely written and usually very short remarks used by accountants for personal information. Implementation of content analysis using cosine similarity showed that identification of the type of operation using the comments of accountants is very likely. Further comment content analysis and financial data analysis showed that it could be expected to reduce the number of potentially suspicious operations significantly: analysis of more than half a million financial records of Dutch companies enabled the identification of 0.3% operations that may be potentially suspicious. This could make human financial auditing easier and more robust task.

This study systematically evaluates the vulnerabilities of modern web browsers using developed indices derived from the CVE database, including I_CVE, I_CVSS, I_R and I_T. These indices incorporate metrics such as vulnerability severity and risks, along with browser popularity, to enable a balanced comparison of browser security. The results highlight significant differences in browser security: while Google Chrome and Samsung Internet exhibited lower threat indices, Mozilla Firefox demonstrated consistently higher scores, indicating greater exposure to risks. These observations a slightly contradict widespread opinion. The findings emphasize the importance of timely software updates in mitigating vulnerabilities, as many incidents were linked to outdated browser versions. This study also introduces a robust methodology for assessing browser threats, providing a framework for future research. Potential applications include developing browser-based penetration testing systems to simulate phishing and data extraction scenarios, offering insights into user-specific risks and broader organizational impacts. By combining theoretical analysis with practical implications, this work contributes to advancing browser security and lays the foundation for future applied research in cybersecurity.

This article describes the use of “Voyant Tools”, an open access text analysis application, to examine a corpus of articles from open access journals, dealing with the topic of digital humanities. The corpus consisted of 404 articles recorded in the “Clarivate Analytics Web of Science” and “Scopus ScienceDirect” databases. The authors discuss how “Voyant Tools” aids to identify the dominant fields of research through quantitative methods and to reveal the main discourse themes using distant reading and interactive reading capabilities. They also identify some problems encountered during the analyses, and also discuss the usefulness of data visualization for research and interpretation. Computer tools can be useful for experienced researchers who are interested in quantitative text analysis, as well as for beginners, as it provides an opportunity to acquire basic knowledge that will lead to a deeper interest in textual analysis methods.

Multidimensional enterprise performance characteristics (enterprise operational data, financial transactions records) are stored in the company’s database (warehouse), and their volume and variety are huge. Financial transaction data are directly and indirectly related to value chain processes, various physical objects of activity, and their attributes. There are data mining (DM) and process mining (PM) methods for analyzing enterprise operational data and identifying deficiencies in business process management. There is a need to find new user experience (UX)-driven methods for user interface with the specification of DM and PM tools on the level of business process management concepts. The paper presents the UX design-based approach to designing the user interface (UI) of process mining and data mining systems and is based on a conceptual semantic model named financial data space (FDS). The peculiarity of FDS is that it can include the characteristics of financial data and other UX-related characteristics (events, environmental and internal changes, business location) that may have an impact on changes in the values of financial objects (FO). The presented multidimensional financial data model helps increase the possibility of uncovering management weaknesses by identifying anomalies in large amounts of financial data. The prototypes of components of the financial data analysis system are described and developed using the process mining tool. The presented method of a multidimensional representation of financial data and transformation into a PM project is a user-friendly solution that allows to increase the analytical capabilities of the auditor’s work with large amounts of data, providing a more flexible view of the financial indicators of the company’s activity.