Data Analytics, Control, and Risk Management
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ItemUsing Autoencoders for Data-Driven Analysis in Internal Auditing( 2021-01-05)New challenges in internal auditing are created as all areas of companies are digitalized. These challenges are forcing internal auditing to implement more and more data-driven procedures. Auditing is increasingly using artificial intelligence methods such as neural networks to overcome these challenges. Since in internal auditing labels are usually not available at the beginning of an audit engagement, unsupervised methods have to be used. We used autoencoders as an unsupervised method, which we evaluated for its use in auditing in a practical case study with an international automobile manufacturer. For the case study, two real-world, non-financial data sets from production-related processes were provided. The results of the case study show that the use of autoencoders can support auditors in the audit execution and in the audit planning process step to improve the quality of the internal audit engagement.
ItemRequirements Identification for Real-Time Anomaly Detection in Industrie 4.0 Machine Groups: A Structured Literature Review( 2021-01-05)Industrie 4.0 environments generate an unprecedented amount of production data. This is due to the rising number of sensors and interconnected devices capable of emitting data in millisecond frequencies. Streaming analytics offers promising methodologies that can support handling and analysis of data volume and variety. Transparency and control over real-time data can increase production efficiency in tightly connected machine environments. Data transparency may avoid time-consuming assessment of machines to detect anomalous machine behavior causing production inefficiencies or failures. This paper aims to identify requirements to implement streaming analytics for the detection of anomalies in Industrie 4.0 production machine groups through a structured literature review.
ItemData-Centric Risk Management for Business Processes( 2021-01-05)Data quality is a key factor enabling the digital transformation, as automated business processes demand it for value creation. This paper explains the relationship between data quality and business processes. In this context, the paper presents two Key Performance Indicators (KPIs) to analyze this relationship and to prioritize actions to improve data quality. The first KPI quantifies the risk of a single process belonging to a process hierarchy based on the quality of its data. The second KPI considers the risk for all analyzed processes based on the quality of a specific data type. Based on these results, the paper presents an approach for prioritizing measures to improve data quality. The applicability of the KPIs is demonstrated with a real application.
ItemA Case Study on the Application of Process Mining in Combination with Journal Entry Tests for Financial Auditing( 2021-01-05)The increasing complexity of data and processes within companies makes it increasingly difficult for auditors to ensure that annual audits are free of material misstatement. To cope with this complexity, a variety of analytical procedures have been developed in the last years. However, most of the existing procedures focus on conspicuous statements in the general ledger, and thus not consider behavioral aspects. In this paper, we show how journal entry tests can be effectively combined with process mining to capture a more comprehensive view within a company's audit. Therefore, the paper gives a comprehensive description of the purchase-to-pay-process and its realization in current SAP software as well as the required mechanism to extract event logs from raw SAP database tables. The conducted analysis is based on a dataset provided by a German medium-sized audit firm. The results suggest that we can discover anomalies that are not traceable through traditional analysis.