Analysis of SAP log data based on network community decomposition

Information systems support and ensure the practical running of the most critical business processes. There exists (or can be reconstructed) a record (log) of the process running in the information system. Computer methods of data mining can be used for analysis of process data utilizing support techniques of machine learning and a complex network analysis. The analysis is usually provided based on quantitative parameters of the running process of the information system. It is not so usual to analyze behavior of the participants of the running process from the process log. Here, we show how data and process mining methods can be used for analyzing the running process and how participants behavior can be analyzed from the process log using network (community or cluster) analyses in the constructed complex network from the SAP business process log. This approach constructs a complex network from the process log in a given context and then finds communities or patterns in this network. Found communities or patterns are analyzed using knowledge of the business process and the environment in which the process operates. The results demonstrate the possibility to cover up not only the quantitative but also the qualitative relations (e.g., hidden behavior of participants) using the process log and specific knowledge of the business case.Web of Science103art. no. 9

Basic Principles of Financial Process Mining A Journey through Financial Data in Accounting Information Systems

Auditors and process managers often face a huge amount of financial entries in accounting information systems. For many reasons like auditing the internal control system a process-oriented view would be more helpful to understand how a set of transactions produced financial entries. For this reason we present an algorithm capable to mine financial entries and open items to reconstruct the process instances which produced the financial entries. In this way, auditors can trace how balance sheet items have been produced in the system. Traditional process mining techniques only reconstruct processes but pay no regard to the financial dimension. The paper wants to close this gap and integrate the process view with the accounting view

Automating Vendor Fraud Detection in Enterprise Systems

Fraud is a multi-billion dollar industry that continues to grow annually. Many organizations are poorly prepared to prevent and detect fraud. Fraud detection strategies are intended to quickly and efficiently identify fraudulent activities that circumvent preventative measures. In this paper, we adopt a DesignScience methodological framework to develop a model for detection of vendor fraud based on analysis of patterns or signatures identified in enterprise system audit trails. The concept is demonstrated by developing prototype software. Verification of the prototype is achieved by performing a series of experiments. Validation is achieved by independent reviews from auditing practitioners. Key findings of this study are: (a) automating routine data analytics improves auditor productivity and reduces time taken to identify potential fraud; and (b) visualizations assist in promptly identifying potentially fraudulent user activities. The study makes the following contributions: (a) a model for proactive fraud detection; (b) methods for visualizing user activities in transaction data; and (c) a stand-alone Monitoring and Control Layer (MCL) based prototype

Automating Vendor Fraud Detection in Enterprise Systems

Fraud is a multi-billion dollar industry that continues to grow annually. Many organisations are poorly prepared to prevent and detect fraud. Fraud detection strategies are intended to quickly and efficiently identify fraudulent activities that circumvent preventative measures. In this paper we adopt a Design-Science methodological framework to develop a model for detection of vendor fraud based on analysis of patterns or signatures identified in enterprise system audit trails. The concept is demonstrated be developing prototype software. Verification of the prototype is achieved by performing a series of experiments. Validation is achieved by independent reviews from auditing practitioners. Key findings of this study are: i) automating routine data analytics improves auditor productivity and reduces time taken to identify potential fraud, and ii) visualisations assist in promptly identifying potentially fraudulent user activities. The study makes the following contributions: i) a model for proactive fraud detection, ii) methods for visualising user activities in transaction data, iii) a stand-alone MCL-based prototype.</p

Activity Prediction of Business Process Instances using Deep Learning Techniques

The ability to predict the next activity of an ongoing case is becoming increasingly important in today’s businesses. Processes need to be monitored in real-life time in order to predict the remaining time of an open case, or also to be able to detect and prevent anomalies before they have a chance to impact the performances. Moreover, financial regulations and laws are changing, requiring companies' processes to be increasingly transparent. Process mining, supported by deep learning techniques, can improve the results of internal audit activities. The task of predicting the next activity can be used in this context to point out traces at risk that need to be monitored. In this way, the business is aware of the situation and, if possible, can take resolution actions in time. In recent years, this problem has been tackled using deep learning techniques, such as Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN) and Long Short-Term Memory (LSTM) neural networks, achieving consistent results. The first contribution of this thesis consists of a generation of a real-life process mining dataset based on the Purchase-to-Pay (P2P) process. The SAP tables structure is taken into account since it is the most popular management software in today's companies. We exploit the simulated dataset to explore modeling techniques and to define the type and the quantity of anomalies. The second contribution of the thesis is an investigation of LSTM neural networks architectures that exploit information from both temporal data and static features, applied to the previously generated dataset. The neural networks are then used to predict future events characteristics of running traces. Finally, real-life application of the results are discussed and future work proposals are presented.The ability to predict the next activity of an ongoing case is becoming increasingly important in today’s businesses. Processes need to be monitored in real-life time in order to predict the remaining time of an open case, or also to be able to detect and prevent anomalies before they have a chance to impact the performances. Moreover, financial regulations and laws are changing, requiring companies' processes to be increasingly transparent. Process mining, supported by deep learning techniques, can improve the results of internal audit activities. The task of predicting the next activity can be used in this context to point out traces at risk that need to be monitored. In this way, the business is aware of the situation and, if possible, can take resolution actions in time. In recent years, this problem has been tackled using deep learning techniques, such as Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN) and Long Short-Term Memory (LSTM) neural networks, achieving consistent results. The first contribution of this thesis consists of a generation of a real-life process mining dataset based on the Purchase-to-Pay (P2P) process. The SAP tables structure is taken into account since it is the most popular management software in today's companies. We exploit the simulated dataset to explore modeling techniques and to define the type and the quantity of anomalies. The second contribution of the thesis is an investigation of LSTM neural networks architectures that exploit information from both temporal data and static features, applied to the previously generated dataset. The neural networks are then used to predict future events characteristics of running traces. Finally, real-life application of the results are discussed and future work proposals are presented