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.