DeepPaper

Financial Statement Audit :: Audit Data Analytics :: Paper Collection

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.

<p align="center"> <img src="https://raw.githubusercontent.com/GitiHubi/deepPaper/master/data/ais_systems.png?raw=true" alt="AIS image" height="300"/> </p>

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 :)

List of Selected Papers:

<br> <table> <tbody> <tr> <td align="left" width=250> <a href="https://scholarspace.manoa.hawaii.edu/bitstream/10125/71317/0562.pdf"><img src="teasers/Nonnenmacher2021.png"/></a></td> <td align="left" width=550><b>'Using Autoencoders for Data-Driven Analysis in Internal Auditing'</b><br> Jakob Nonnenmacher, Felix Kruse, Gerrit Schumann, and Jorge Marx Gomez<br> In 54th Hawaii International Conference on System Sciences (HICSS),<br> Manoa, USA, 2021<br> <a href="https://scholarspace.manoa.hawaii.edu/bitstream/10125/71317/0562.pdf">[Paper]</a> </td></tr></tbody></table> <table> <tbody> <tr> <td align="left" width=250> <a href="https://hsg-aiml.github.io/audit.html"><img src="teasers/Schreyer2021.png"/></a></td> <td align="left" width=550><b>'Leaking Sensitive Financial Accounting Data in Plain Sight using Deep Autoencoder Neural Networks'</b><br> Marco Schreyer, Christian Schulze, and Damian Borth<br> AAAI Workshop on Knowledge Discovery from Unstructured Data in Financial Services,<br> Virtual, Virtual, 2021<br> <a href="https://www.alexandria.unisg.ch/261665/1/AAAI_2021_preprint.pdf">[Paper]</a> <a href="https://hsg-aiml.github.io/audit.html">[Project]</a> </td></tr></tbody></table> <table> <tbody> <tr> <td align="left" width=250> <a href="https://hsg-aiml.github.io/audit.html"><img src="teasers/Schreyer2020.png"/></a></td> <td align="left" width=550><b>'Learning Sampling in Financial Statement Audits using Vector Quantised Autoencoder Neural Networks'</b><br> Marco Schreyer, Timur Sattarov, Anita Gierbl, Bernd Reimer, and Damian Borth<br> International Conference on Artificial Intelligence in Finance (ICAIF),<br> New York, USA, 2020<br> <a href="https://www.alexandria.unisg.ch/260768/1/ICAIF_2020_finale.pdf">[Paper]</a> <a href="https://hsg-aiml.github.io/audit.html">[Project]</a> </td></tr></tbody></table> <table> <tbody> <tr> <td align="left" width=250> <a href="https://scholarspace.manoa.hawaii.edu/bitstream/10125/64408/1/0536.pdf"><img src="teasers/Schultz2020.png"/></a></td> <td align="left" width=550><b>'Autoencoder Neural Networks versus External Auditors: Detecting Unusual Journal Entries in Financial Statement Audits'</b><br> Martin Schultz, and Marina Tropmann-Frick<br> In 53rd Hawaii International Conference on System Sciences (HICSS),<br> Manoa, USA, 2020<br> <a href="https://scholarspace.manoa.hawaii.edu/bitstream/10125/64408/1/0536.pdf">[Paper]</a> </td></tr></tbody></table> <table> <tbody> <tr> <td align="left" width=250> <a href="https://aisel.aisnet.org/cgi/viewcontent.cgi?article=1099&context=ecis2020_rp"><img src="teasers/Bhattacharya2020.png"/></a></td> <td align="left" width=550><b>'A Semi-Supervised Machine Learning Approach to Detect Anomalies in Big Accounting Data'</b><br> Indranil Bhattacharya, and Edo Roos Lindgreen<br> European Conference on Information Systems (ECIS),<br> Marrakech, Morocco, 2020<br> <a href="https://aisel.aisnet.org/cgi/viewcontent.cgi?article=1099&context=ecis2020_rp">[Paper]</a> </td></tr></tbody></table> <table> <tbody> <tr> <td align="left" width=250> <a href="https://sebd2020.unica.it/proceedings/05-paper.pdf"><img src="teasers/Zupan2020.png"/></a></td> <td align="left" width=550><b>'Accounting Journal Reconstruction with Variational Autoencoders and Long Short-term Memory Architecture'</b><br> Mario Zupan, Svjetlana Letinic, and Verica Budimir<br> 28th Symposium on Advanced Database Systems (SEBD),<br> Villasimius, Italy, 2020<br> <a href="https://sebd2020.unica.it/proceedings/05-paper.pdf">[Paper]</a> </td></tr></tbody></table> <table> <tbody> <tr> <td align="left" width=250> <a href="https://www.dfki.de/en/web/research/projects-and-publications/projects/project/spotted/"><img src="teasers/Lahann2019.png"/></a></td> <td align="left" width=550><b>'Utilizing Machine Learning Techniques to Reveal VAT Compliance Violations in Accounting Data'</b><br> Johannes Lahann, Martin Scheid, and Peter Fettke<br> IEEE 21st Conference on Business Informatics (CBI),<br> Moscow, Russia, 2019<br> <a href="https://ieeexplore.ieee.org/document/8808015">[Paper]</a> <a href="https://www.dfki.de/en/web/research/projects-and-publications/projects/project/spotted/">[Project]</a> </td></tr></tbody></table> <table> <tbody> <tr> <td align="left" width=250> <a href="https://hsg-aiml.github.io/audit.html"><img src="teasers/Schreyer2019b.png"/></a></td> <td align="left" width=550><b>'Adversarial Learning of Deepfakes in Accounting'</b><br> Marco Schreyer, Timur Sattarov, Bernd Reimer, and Damian Borth<br> Advances in Neural Information Processing Systems 32 (NeurIPS) - Workshop on Robust AI in Financial Services,<br> Vancouver, Canada, 2019<br> <a href="https://www.alexandria.unisg.ch/258090/1/NeurIPS_2019_RAIFS_final.pdf">[Paper]</a> <a href="https://hsg-aiml.github.io/audit.html">[Project]</a> </td></tr></tbody></table> <table> <tbody> <tr> <td align="left" width=250> <a href="https://github.com/GitiHubi/deepAD"><img src="teasers/Schreyer2019a.png"/></a></td> <td align="left" width=550><b>'Detection of Accounting Anomalies in the Latent Space using Adversarial Autoencoder Neural Networks'</b><br> Marco Schreyer, Timur Sattarov, Christian Schulze, Bernd Reimer, and Damian Borth<br> 25th Conference on Knowledge Discovery and Data Mining (KDD) - 2nd Workshop on Anomaly Detection in Finance,<br> Anchorage, USA, 2019<br> <a href="https://www.alexandria.unisg.ch/257633/4/KDD_2019_ADF_final.pdf">[Paper]</a> <a href="https://github.com/GitiHubi/deepAD">[Project]</a> </td></tr></tbody></table> <table> <tbody> <tr> <td align="left" width=250> <a href="https://github.com/GitiHubi/deepAI"><img src="teasers/Schreyer2017.png"/></a></td> <td align="left" width=550><b>'Detection of Anomalies in Large-Scale Accounting Data using Deep Autoencoder Networks '</b><br> Marco Schreyer, Timur Sattarov, Damian Borth, Andreas Dengel, and Bernd Reimer<br> Nvidias GPU Technology Conference (GTC) - Financial Services Track,<br> San Jose, USA, 2017<br> <a href="https://www.alexandria.unisg.ch/258111/1/GTC_2018_final.pdf">[Paper]</a> <a href="https://github.com/GitiHubi/deepAI">[Project]</a> </td></tr></tbody></table> <table> <tbody> <tr> <td align="left" width=250> <a href="https://www.alexandria.unisg.ch/260689/1/businessvis2014-tatu.pdf"><img src="teasers/Tatu2014.png"/></a></td> <td align="left" width=550><b>'Visual Exploration of Journal Entries to Detect Accounting Irregularities and Fraud'</b><br> Andrada Tatu, Marco Schreyer, Jan Hagelauer, and Jixuan Wang<br> IEEE VIS 2014 Workshop - Information Visualization and Visual Analytics in Business,<br> Paris, France, 2014<br> <a href="https://www.alexandria.unisg.ch/260689/1/businessvis2014-tatu.pdf">[Paper]</a> </td></tr></tbody></table> <table> <tbody> <tr> <td align="left" width=250> <a href="https://pdfs.semanticscholar.org/191d/09d88c0013fbd5d3cf6f608bc3a4363b38db.pdf"><img src="teasers/Argyrou2013.png"/></a></td> <td align="left" width=550><b>'Auditing Journal Entries Using Extreme Vale Theory'</b><br> Argyris Argyrou<br> 21st European Conference on Information Systems (ECIS),<br> Utrecht, Netherlands, 2013<br> <a href="https://pdfs.semanticscholar.org/191d/09d88c0013fbd5d3cf6f608bc3a4363b38db.pdf">[Paper]</a> </td></tr></tbody></table> <table> <tbody> <tr> <td align="left" width=250> <a href="https://pdfs.semanticscholar.org/898c/d58614cdadd5e6e2507cad79e8e37c6ba5e5.pdf"><img src="teasers/Argyrou2012.png"/></a></td> <td align="left" width=550><b>'Auditing Journal Entries Using Self-Organizing Map'</b><br> Argyris Argyrou<br> 18th Americas Conference on Information Systems (AMCIS),<br> Seattle, USA, 2012<br> <a href="https://pdfs.semanticscholar.org/898c/d58614cdadd5e6e2507cad79e8e37c6ba5e5.pdf">[Paper]</a> </td></tr></tbody></table> <table> <tbody> <tr> <td align="left" width=250> <a href="http://isiarticles.com/bundles/Article/pre/pdf/9350.pdf"><img src="teasers/Jans2011.png"/></a></td> <td align="left" width=550><b>'A Business Process Mining Application for Internal Transaction Fraud Mitigation'</b><br> Mike Jans, Jan Martijn Van Der Werf, Nadine Lybaert, and Koen Vanhoof<br> In Expert Systems with Applications 38, 2011<br> <a href="http://isiarticles.com/bundles/Article/pre/pdf/9350.pdf">[Paper]</a> </td></tr></tbody></table> <table> <tbody> <tr> <td align="left" width=250> <a href="https://www.sciencedirect.com/science/article/pii/S1467089510000540"><img src="teasers/Debreceny2010.png"/></a></td> <td align="left" width=550><b>'Data Mining Journal Entries for Fraud Detection: An Exploratory Study'</b><br> Roger S. Debreceny, and Glen L. Gray <br> In International Journal of Accounting Information Systems, 2010<br> <a href="https://www.sciencedirect.com/science/article/pii/S1467089510000540">[Paper]</a> </td></tr></tbody></table> <table> <tbody> <tr> <td align="left" width=250> <a href="https://pdfs.semanticscholar.org/7718/5ac9267f211981dd484452c14d9042804b80.pdf"><img src="teasers/Khan2010.png"/>