The Financial Audit Data Analytics Paper Collection is an academic paper collection that encompasses (i) data analytics, (ii) machine learning, and (iii) deep learning papers that produce experimental results related to the ** (internal) audit of financial accounting data**. The paper collection thereby focuses on "Journal Entry" data usually observable in Accounting Information Systems (AIS) or Enterprise Resource Planing (ERP) systems.
If you want me to add or remove a paper, please send me an email (marco dot schreyer at unisg dot ch). The collection is currently "work in progress". Please don't expect an all-encompassing list of papers at this point. Happy auditing :)
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'Using Autoencoders for Data-Driven Analysis in Internal Auditing' Jakob Nonnenmacher, Felix Kruse, Gerrit Schumann, and Jorge Marx Gomez In 54th Hawaii International Conference on System Sciences (HICSS), Manoa, USA, 2021 [Paper] |
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'Leaking Sensitive Financial Accounting Data in Plain Sight using Deep Autoencoder Neural Networks' Marco Schreyer, Christian Schulze, and Damian Borth AAAI Workshop on Knowledge Discovery from Unstructured Data in Financial Services, Virtual, Virtual, 2021 [Paper] [Project] |
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'Learning Sampling in Financial Statement Audits using Vector Quantised Autoencoder Neural Networks' Marco Schreyer, Timur Sattarov, Anita Gierbl, Bernd Reimer, and Damian Borth International Conference on Artificial Intelligence in Finance (ICAIF), New York, USA, 2020 [Paper] [Project] |
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'Autoencoder Neural Networks versus External Auditors: Detecting Unusual Journal Entries in Financial Statement Audits' Martin Schultz, and Marina Tropmann-Frick In 53rd Hawaii International Conference on System Sciences (HICSS), Manoa, USA, 2020 [Paper] |
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'A Semi-Supervised Machine Learning Approach to Detect Anomalies in Big Accounting Data' Indranil Bhattacharya, and Edo Roos Lindgreen European Conference on Information Systems (ECIS), Marrakech, Morocco, 2020 [Paper] |
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'Accounting Journal Reconstruction with Variational Autoencoders and Long Short-term Memory Architecture' Mario Zupan, Svjetlana Letinic, and Verica Budimir 28th Symposium on Advanced Database Systems (SEBD), Villasimius, Italy, 2020 [Paper] |
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'Utilizing Machine Learning Techniques to Reveal VAT Compliance Violations in Accounting Data' Johannes Lahann, Martin Scheid, and Peter Fettke IEEE 21st Conference on Business Informatics (CBI), Moscow, Russia, 2019 [Paper] [Project] |
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'Adversarial Learning of Deepfakes in Accounting' Marco Schreyer, Timur Sattarov, Bernd Reimer, and Damian Borth Advances in Neural Information Processing Systems 32 (NeurIPS) - Workshop on Robust AI in Financial Services, Vancouver, Canada, 2019 [Paper] [Project] |
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'Detection of Accounting Anomalies in the Latent Space using Adversarial Autoencoder Neural Networks' Marco Schreyer, Timur Sattarov, Christian Schulze, Bernd Reimer, and Damian Borth 25th Conference on Knowledge Discovery and Data Mining (KDD) - 2nd Workshop on Anomaly Detection in Finance, Anchorage, USA, 2019 [Paper] [Project] |
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'Detection of Anomalies in Large-Scale Accounting Data using Deep Autoencoder Networks ' Marco Schreyer, Timur Sattarov, Damian Borth, Andreas Dengel, and Bernd Reimer Nvidias GPU Technology Conference (GTC) - Financial Services Track, San Jose, USA, 2017 [Paper] [Project] |
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'Visual Exploration of Journal Entries to Detect Accounting Irregularities and Fraud' Andrada Tatu, Marco Schreyer, Jan Hagelauer, and Jixuan Wang IEEE VIS 2014 Workshop - Information Visualization and Visual Analytics in Business, Paris, France, 2014 [Paper] |
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'Auditing Journal Entries Using Extreme Vale Theory' Argyris Argyrou 21st European Conference on Information Systems (ECIS), Utrecht, Netherlands, 2013 [Paper] |
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'Auditing Journal Entries Using Self-Organizing Map' Argyris Argyrou 18th Americas Conference on Information Systems (AMCIS), Seattle, USA, 2012 [Paper] |
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'A Business Process Mining Application for Internal Transaction Fraud Mitigation' Mike Jans, Jan Martijn Van Der Werf, Nadine Lybaert, and Koen Vanhoof In Expert Systems with Applications 38, 2011 [Paper] |
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'Data Mining Journal Entries for Fraud Detection: An Exploratory Study' Roger S. Debreceny, and Glen L. Gray In International Journal of Accounting Information Systems, 2010 [Paper] |
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'Transaction Mining for Fraud Detection in ERP Systems ' Roheena Q. Khan, Malcolm Corney, Andrew J. Clark, and George M. Mohay In Industrial Engineering and Management Systems 9, 2010 [Paper] |
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'Fraud detection in ERP systems using Scenario matching ' Asadul K. Islam, Malcolm Corney, George Mohay, Andrew Clark, Shane Bracher, Tobias Raub, and Ulrich Flegel In IFIP Advances in Information and Communication Technology 330, 2010 [Paper] |
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'SNARE: A Link Analytic System for Graph Labeling and Risk Detection' Mary McGlohon, Stephan Bay, Markus G. Anderle, David M. Steier, and Christos Faloutsos 15th Conference on Knowledge Discovery and Data Mining (KDD) - Research Track, Paris, France, 2009 [Paper] |
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'A Role Mining Inspired Approach to Representing User Behaviour in ERP Systems' Roheena Q. Khan, and Malcolm Corney 10th Asia Pacific Industrial Engineering and Management Systems Conference (APIEMS), Kitakyushu, Japan, 2009 [Paper] |
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'Large Scale Detection of Irregularities in Accounting Data' Stephan Bay, Krishna Kumaraswamy, Markus G. Anderle, Rohlt Kumar, and David M. Steier 6th International Conference on Data Mining (ICDM), Hong Kong, China, 2006 [Paper] |
Last updated in Jan 2021, based on an idea of Jun-Yan Zhu.