Julian Tritscher, M.Sc.

Chair of Data Science (Informatik X)
University of Würzburg
Am Hubland
97074 Würzburg
Germany

Email: tritscher@informatik.uni-wuerzburg.de

Phone: (+49 931) 31 - 84467

Office: Room 50.03.016 (Institutsgebäude Künstliche Intelligenz)

Projects and Research Interests

Explainable Artificial Intelligence - Anomaly Detection - Machine Learning

I am part of the Data Science Chair since I recieved my Master's Degree in Computer Science from the University of Würzburg in early 2019. I have worked in the past on investigating the explainable detection of anomalous and fraudulent behavior in ERP (Enterprise Resource Planning) systems in the DeepScan project. As part of the AI@Knauf project, I am currently working on integrating machine learning approaches into industrial manufacturing processes.

My research focus is the domain of explainable artificial intelligence (XAI). Here I work on providing intuitive explanations for decisions from complex anomaly detection approaches.

Teaching

Fundamentals of Algorithms and Data Structures (winter term 2020/21, winter term 2021/22, winter term 2022/23, winter term 2023/24)
Data Mining (summer term 2019, summer term 2020)
Seminar "Selected Chapters from Machine Learning" (summer term 2019)

Activities

PC member for the 8th Workshop on MIning DAta for financial applicationS (MIDAS 2023)

Publications

2023[ to top ]

Tritscher, J., Wolf, M., Hotho, A., Schlör, D. (2023) Evaluating feature relevance XAI in network intrusion detection, The World Conference on eXplainable Artificial Intelligence (xAI 2023) - to appear.
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As machine learning models become increasingly complex, there is a growing need for explainability to understand and trust the decision-making processes. In the domain of network intrusion detection, post-hoc feature relevance explanations have been widely used to provide insight into the factors driving model decisions. However, recent research has highlighted challenges with these methods when applied to anomaly detection, which can vary in importance and impact depending on the application domain. In this paper, we investigate the challenges of post-hoc feature relevance explanations for network intrusion detection, a critical area for ensuring the security and integrity of computer networks. To gain a deeper understanding of these challenges for the application domain, we quantitatively and qualitatively investigate the popular feature relevance approach SHAP when explaining different network intrusion detection approaches. We conduct experiments to jointly evaluate detection quality and explainability, and explore the impact of replacement data, a commonly overlooked hyperparameter of post-hoc feature relevance approaches. We find that post-hoc XAI can provide high quality explanations, but requires a careful choice of its replacement data as default settings and common choices do not transfer across different detection models. Our study showcases the viability of post-hoc XAI for network intrusion detection systems, but highlights the need for rigorous evaluations of produced explanations.

@article{tritscher2023evaluating, abstract = {As machine learning models become increasingly complex, there is a growing need for explainability to understand and trust the decision-making processes. In the domain of network intrusion detection, post-hoc feature relevance explanations have been widely used to provide insight into the factors driving model decisions. However, recent research has highlighted challenges with these methods when applied to anomaly detection, which can vary in importance and impact depending on the application domain. In this paper, we investigate the challenges of post-hoc feature relevance explanations for network intrusion detection, a critical area for ensuring the security and integrity of computer networks. To gain a deeper understanding of these challenges for the application domain, we quantitatively and qualitatively investigate the popular feature relevance approach SHAP when explaining different network intrusion detection approaches. We conduct experiments to jointly evaluate detection quality and explainability, and explore the impact of replacement data, a commonly overlooked hyperparameter of post-hoc feature relevance approaches. We find that post-hoc XAI can provide high quality explanations, but requires a careful choice of its replacement data as default settings and common choices do not transfer across different detection models. Our study showcases the viability of post-hoc XAI for network intrusion detection systems, but highlights the need for rigorous evaluations of produced explanations.}, author = {Tritscher, Julian and Wolf, Maximilian and Hotho, Andreas and Schlör, Daniel}, journal = {The World Conference on eXplainable Artificial Intelligence (xAI 2023) - to appear}, keywords = {xai}, title = {Evaluating feature relevance XAI in network intrusion detection}, year = 2023 }
Tritscher, J., Roos, A., Schlör, D., Hotho, A., Krause, A. (2023) Occupational Fraud Detection through Agent-based Data Generation, The 8th Workshop on MIning DAta for financial applicationS MIDAS 2023 - to appear.
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Occupational fraud is an increasing concern for enterprises that is estimated to cause losses of around 5% of company revenue each year. With the increasing data tracked by companies through enterprise resource planning systems, recent research has taken interest in the automated detection of occupational fraud. Automated detection is however hindered by the unavailability of labeled fraud cases which require known occupational frauds within company data and costly expert annotation. Even despite the existence of anomaly detection methods that can be trained on unsupervised data, selecting the ideal preprocessing techniques, the most suitable model, and the optimal hyperparameters necessitates the availability of labeled data for evaluation purposes. To alleviate this issue, we propose to use simulation through multi-agent systems for generating business processes according to best practices from economics and creating labeled synthetic data that closely matches a given unlabeled real-world dataset. We extend an existing simulation by incorporating functionality for including, tracking and automatic labeling of occupational fraud cases. Using this simulation, we propose a framework that decides on important design choices for fraud detection models in enterprise resource planning data and does not require labeled real-world data. We demonstrate in multiple experiments that the framework can aid automated occupational fraud detection through data generation.

@article{tritscher2023occupational, abstract = {Occupational fraud is an increasing concern for enterprises that is estimated to cause losses of around 5% of company revenue each year. With the increasing data tracked by companies through enterprise resource planning systems, recent research has taken interest in the automated detection of occupational fraud. Automated detection is however hindered by the unavailability of labeled fraud cases which require known occupational frauds within company data and costly expert annotation. Even despite the existence of anomaly detection methods that can be trained on unsupervised data, selecting the ideal preprocessing techniques, the most suitable model, and the optimal hyperparameters necessitates the availability of labeled data for evaluation purposes. To alleviate this issue, we propose to use simulation through multi-agent systems for generating business processes according to best practices from economics and creating labeled synthetic data that closely matches a given unlabeled real-world dataset. We extend an existing simulation by incorporating functionality for including, tracking and automatic labeling of occupational fraud cases. Using this simulation, we propose a framework that decides on important design choices for fraud detection models in enterprise resource planning data and does not require labeled real-world data. We demonstrate in multiple experiments that the framework can aid automated occupational fraud detection through data generation.}, author = {Tritscher, Julian and Roos, Alexander and Schlör, Daniel and Hotho, Andreas and Krause, Anna}, journal = {The 8th Workshop on MIning DAta for financial applicationS MIDAS 2023 - to appear}, keywords = {simulation}, title = {Occupational Fraud Detection through Agent-based Data Generation}, year = 2023 }
Tritscher, J., Krause, A., Hotho, A. (2023) Feature relevance XAI in anomaly detection: Reviewing approaches and challenges, Frontiers in Artificial Intelligence, 6, available: https://doi.org/10.3389/frai.2023.1099521.
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With complexity of artificial intelligence systems increasing continuously in past years, studies to explain these complex systems have grown in popularity. While much work has focused on explaining artificial intelligence systems in popular domains such as classification and regression, explanations in the area of anomaly detection have only recently received increasing attention from researchers. In particular, explaining singular model decisions of a complex anomaly detector by highlighting which inputs were responsible for a decision, commonly referred to as local post-hoc feature relevance, has lately been studied by several authors. In this paper, we systematically structure these works based on their access to training data and the anomaly detection model, and provide a detailed overview of their operation in the anomaly detection domain. We demonstrate their performance and highlight their limitations in multiple experimental showcases, discussing current challenges and opportunities for future work in feature relevance XAI for anomaly detection.

@article{tritscher2023feature, abstract = {With complexity of artificial intelligence systems increasing continuously in past years, studies to explain these complex systems have grown in popularity. While much work has focused on explaining artificial intelligence systems in popular domains such as classification and regression, explanations in the area of anomaly detection have only recently received increasing attention from researchers. In particular, explaining singular model decisions of a complex anomaly detector by highlighting which inputs were responsible for a decision, commonly referred to as local post-hoc feature relevance, has lately been studied by several authors. In this paper, we systematically structure these works based on their access to training data and the anomaly detection model, and provide a detailed overview of their operation in the anomaly detection domain. We demonstrate their performance and highlight their limitations in multiple experimental showcases, discussing current challenges and opportunities for future work in feature relevance XAI for anomaly detection.}, author = {Tritscher, Julian and Krause, Anna and Hotho, Andreas}, journal = {Frontiers in Artificial Intelligence}, keywords = {xai}, month = {feb}, title = {Feature relevance XAI in anomaly detection: Reviewing approaches and challenges}, volume = 6, year = 2023 }

2022[ to top ]

Tritscher, J., Schlör, D., Gwinner, F., Krause, A., Hotho, A. (2022) Towards Explainable Occupational Fraud Detection, Machine Learning and Principles and Practice of Knowledge Discovery in Databases. ECML PKDD 2022, Communications in Computer and Information Science(1753), 79–96, available: https://doi.org/https://doi.org/10.1007/978-3-031-23633-4_7.
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Occupational fraud within companies currently causes losses of around 5% of company revenue each year. While enterprise resource planning systems can enable automated detection of occupational fraud through recording large amounts of company data, the use of state-of-the-art machine learning approaches in this domain is limited by their untraceable decision process. In this study, we evaluate whether machine learning combined with explainable artificial intelligence can provide both strong performance and decision traceability in occupational fraud detection. We construct an evaluation setting that assesses the comprehensibility of machine learning-based occupational fraud detection approaches, and evaluate both performance and comprehensibility of multiple approaches with explainable artificial intelligence. Our study finds that high detection performance does not necessarily indicate good explanation quality, but specific approaches provide both satisfactory performance and decision traceability, highlighting the suitability of machine learning for practical application in occupational fraud detection and the importance of research evaluating both performance and comprehensibility together.

@article{tritscher2022towards, abstract = {Occupational fraud within companies currently causes losses of around 5% of company revenue each year. While enterprise resource planning systems can enable automated detection of occupational fraud through recording large amounts of company data, the use of state-of-the-art machine learning approaches in this domain is limited by their untraceable decision process. In this study, we evaluate whether machine learning combined with explainable artificial intelligence can provide both strong performance and decision traceability in occupational fraud detection. We construct an evaluation setting that assesses the comprehensibility of machine learning-based occupational fraud detection approaches, and evaluate both performance and comprehensibility of multiple approaches with explainable artificial intelligence. Our study finds that high detection performance does not necessarily indicate good explanation quality, but specific approaches provide both satisfactory performance and decision traceability, highlighting the suitability of machine learning for practical application in occupational fraud detection and the importance of research evaluating both performance and comprehensibility together.}, author = {Tritscher, Julian and Schlör, Daniel and Gwinner, Fabian and Krause, Anna and Hotho, Andreas}, journal = {Machine Learning and Principles and Practice of Knowledge Discovery in Databases. ECML PKDD 2022}, keywords = {xai}, number = 1753, pages = {79-96}, title = {Towards Explainable Occupational Fraud Detection}, volume = {Communications in Computer and Information Science}, year = 2022 }
Tritscher, J., Gwinner, F., Schlör, D., Krause, A., Hotho, A. (2022) Open ERP System Data For Occupational Fraud Detection, arxiv, available: https://arxiv.org/abs/2206.04460.
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Recent estimates report that companies lose 5% of their revenue to occupational fraud. Since most medium-sized and large companies employ Enterprise Resource Planning (ERP) systems to track vast amounts of information regarding their business process, researchers have in the past shown interest in automatically detecting fraud through ERP system data. Current research in this area, however, is hindered by the fact that ERP system data is not publicly available for the development and comparison of fraud detection methods. We therefore endeavour to generate public ERP system data that includes both normal business operation and fraud. We propose a strategy for generating ERP system data through a serious game, model a variety of fraud scenarios in cooperation with auditing experts, and generate data from a simulated make-to-stock production company with multiple research participants. We aggregate the generated data into ready to used datasets for fraud detection in ERP systems, and supply both the raw and aggregated data to the general public to allow for open development and comparison of fraud detection approaches on ERP system data.

@article{tritscher2022erpData, abstract = {Recent estimates report that companies lose 5% of their revenue to occupational fraud. Since most medium-sized and large companies employ Enterprise Resource Planning (ERP) systems to track vast amounts of information regarding their business process, researchers have in the past shown interest in automatically detecting fraud through ERP system data. Current research in this area, however, is hindered by the fact that ERP system data is not publicly available for the development and comparison of fraud detection methods. We therefore endeavour to generate public ERP system data that includes both normal business operation and fraud. We propose a strategy for generating ERP system data through a serious game, model a variety of fraud scenarios in cooperation with auditing experts, and generate data from a simulated make-to-stock production company with multiple research participants. We aggregate the generated data into ready to used datasets for fraud detection in ERP systems, and supply both the raw and aggregated data to the general public to allow for open development and comparison of fraud detection approaches on ERP system data.}, author = {Tritscher, Julian and Gwinner, Fabian and Schlör, Daniel and Krause, Anna and Hotho, Andreas}, journal = {arxiv}, keywords = {myown}, title = {Open ERP System Data For Occupational Fraud Detection}, year = 2022 }

2021[ to top ]

Tritscher, J., Krause, A., Schl{\"o}r, D., Gwinner, F., von Mammen, S., Hotho, A. (2021) A financial game with opportunities for fraud, IEE COG 2021, 2021, available: https://ieee-cog.org/2021/assets/papers/paper_273.pdf.
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Even though companies store large amounts of business data in enterprise resource planning (ERP) systems, obtaining data for financial fraud detection is prohibitively difficult due to privacy concerns and companies protecting trade secrets. One possible solution is game-based generation of synthetic ERP data, which poses the challenge of designing an environment that generates realistic ERP data and allows players to commit many different types of fraud. In this work, we design a multiplayer game that allows players to cooperatively run a fictional company, while simultaneously challenging them to maximize their personal gain. We introduce an approach for letting players explore fraud scenarios through emergent gameplay and present a prototype that may be primed with information from real world ERP systems to generate realistic data.

@article{tritscherfinancial, abstract = {Even though companies store large amounts of business data in enterprise resource planning (ERP) systems, obtaining data for financial fraud detection is prohibitively difficult due to privacy concerns and companies protecting trade secrets. One possible solution is game-based generation of synthetic ERP data, which poses the challenge of designing an environment that generates realistic ERP data and allows players to commit many different types of fraud. In this work, we design a multiplayer game that allows players to cooperatively run a fictional company, while simultaneously challenging them to maximize their personal gain. We introduce an approach for letting players explore fraud scenarios through emergent gameplay and present a prototype that may be primed with information from real world ERP systems to generate realistic data.}, author = {Tritscher, Julian and Krause, Anna and Schl{\"o}r, Daniel and Gwinner, Fabian and von Mammen, Sebastian and Hotho, Andreas}, journal = {IEE COG 2021}, keywords = {myown}, title = {A financial game with opportunities for fraud}, volume = 2021, year = 2021 }

2020[ to top ]

Kobs, K., Zehe, A., Bernstetter, A., Chibane, J., Pfister, J., Tritscher, J., Hotho, A. (2020) Emote-Controlled: Obtaining Implicit Viewer Feedback through Emote based Sentiment Analysis on Comments of Popular Twitch.tv Channels, ACM Transactions on Social Computing, available: https://doi.org/10.1145/3365523.
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@article{kobs2020emotecontrolled, author = {Kobs, Konstantin and Zehe, Albin and Bernstetter, Armin and Chibane, Julian and Pfister, Jan and Tritscher, Julian and Hotho, Andreas}, journal = {ACM Transactions on Social Computing}, keywords = {sentiment}, note = {accepted but not published}, title = {Emote-Controlled: Obtaining Implicit Viewer Feedback through Emote based Sentiment Analysis on Comments of Popular Twitch.tv Channels}, year = 2020 }
Tritscher, J., Ring, M., Schlör, D., Hettinger, L., Hotho, A. (2020) Evaluation of post-hoc XAI approaches through synthetic tabular data, International Symposium on Methodologies for Intelligent Systems.
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Evaluating the explanations given by post-hoc XAI approaches on tabular data is a challenging prospect, since the subjective judgement of explanations of tabular relations is non trivial in contrast to e.g. the judgement of image heatmap explanations. In order to quantify XAI performance on categorical tabular data, where feature relationships can often be described by Boolean functions, we propose an evaluation setting through generation of synthetic datasets. To create gold standard explanations, we present a definition of feature relevance in Boolean functions. In the proposed setting we evaluate eight state-of-the-art XAI approaches and gain novel insights into XAI performance on categorical tabular data. We find that the investigated approaches often fail to faithfully explain even basic relationships within categorical data.

@article{tritscher2020evaluation, abstract = {Evaluating the explanations given by post-hoc XAI approaches on tabular data is a challenging prospect, since the subjective judgement of explanations of tabular relations is non trivial in contrast to e.g. the judgement of image heatmap explanations. In order to quantify XAI performance on categorical tabular data, where feature relationships can often be described by Boolean functions, we propose an evaluation setting through generation of synthetic datasets. To create gold standard explanations, we present a definition of feature relevance in Boolean functions. In the proposed setting we evaluate eight state-of-the-art XAI approaches and gain novel insights into XAI performance on categorical tabular data. We find that the investigated approaches often fail to faithfully explain even basic relationships within categorical data.}, author = {Tritscher, Julian and Ring, Markus and Schlör, Daniel and Hettinger, Lena and Hotho, Andreas}, booktitle = {International Symposium on Methodologies for Intelligent Systems}, keywords = {xai}, note = {Accepted but not published}, organization = {Springer}, title = {Evaluation of post-hoc XAI approaches through synthetic tabular data}, year = 2020 }