J. Nonnenmacher, J. Marx Gómez, Tell Me Why – A Systematic Literature Review on Outlier Explanation for Tabular Data, 3rd International Conference on Pattern Recognition and Machine Learning (PRML 2022) | Accepted! Publication follows

G. Schumann, K. Meyer, J. Marx Gómez, Query-Based Retrieval of German Regulatory Documents for Internal Auditing Purposes, 5th International Conference on Data Science and Information Technology (DSIT 2022) | Accepted! Publication follows

J. Nonnenmacher, J. Marx Gómez, Beyond Numerical – MIXATON for outlier explanation on mixed-type data, 34th International Conference on Software Engineering & Knowledge Engineering (SEKE 2022) | Accepted! Publication follows

J. Nonnenmacher, J. Marx Gómez, Unsupervised anomaly detection for internal auditing: Literature review and research agenda, The International Journal of Digital Accounting Research 2021-01-17 | journal-article

G. Schumann, J. Marx Gómez, Natural Language Processing in Internal Auditing – a Structured Literature Review. Proceedings of the Twenty-Seventh Americas Conference on Information Systems (AMCIS 2021) | conference-paper

J. Nonnenmacher, F. Kruse, G. Schumann, Jorge Marx Gómez, Using Autoencoders for Data-Driven Analysis in Internal Auditing. Proceedings of the 54th Hawaii International Conference on System Sciences (HICSS 2021) | conference-paper

G. Schumann, F. Kruse, J. Nonnenmacher, A Practice-Oriented, Control-Flow-Based Anomaly Detection Approach for Internal Process Audits. 18th International Conference on Service-Oriented Computing (ICSOC 2020) | conference-paper