Marc Schmitt

Abstract: The rise of individual investors marks a structural shift in financial markets, driven by advances in digital platforms, AI-driven tools, and global connectivity. Traditionally viewed as passive recipients of institutional innovation, individual investors now actively shape market dynamics through strategic behavior, platform engagement, and capital allocation into non-traditional domains. This paper introduces the concept of Circular Innovation Streams, a self-reinforcing model in which empowered individuals not only adopt emerging technologies but also drive their evolution. Unlike traditional linear models of innovation diffusion, Circular Innovation Streams emphasize recursive feedback: investor behavior generates innovation pressure, which reshapes platform features, further enhancing investor autonomy and influence. To formalize this dynamic, a recursive mathematical model is developed that links investor empowerment, investor behavior, innovation outcomes, and platform evolution over time. A stylized algorithm illustrates the core feedback mechanism, providing a foundation for future agent-based simulations and empirical validation. This formalization captures how digitally empowered investors fuel a continuous cycle of technological and financial innovation.

Keywords: Digital Finance, Technological Innovation; Circular Innovation Streams; Democratization of Finance

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Marc Schmitt

Abstract: As geopolitical, organizational, and technological fragmentation deepens, resilient digital collaboration becomes imperative. This paper develops a spectrum framework of polycentric digital ecosystems—nested socio-technical systems spanning personal, organizational, inter-organizational, and global layers. Integration across these layers is enabled by four technology clusters: AI and automation, blockchain trust, federated data spaces, and immersive technologies. By redefining digital ecosystems as distributed, adaptive networks of loosely coupled actors, this study outlines new pathways for cross-border coordination and innovation. The framework extends platform theory by introducing a multi-layer conceptualization of polycentric digital ecosystems and demonstrates how AI-enabled infrastructures can be orchestrated to achieve digital integration in a fragmented, multipolar world.

Keywords: Digital ecosystems, digital integration, fragmentation, collective intelligence

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Marc Schmitt, Pantelis Koutroumpis 

Abstract: The digital age, driven by the Artificial Intelligence (AI) revolution, brings significant opportunities but also conceals security threats, which we refer to as cyber shadows. These threats pose risks at individual, organizational, and societal levels. This article examines the systemic impact of these cyber threats and proposes a comprehensive cybersecurity strategy that integrates AI-driven solutions, such as intrusion detection systems (IDS), with targeted policy interventions. By combining technological and regulatory measures, we create a multilevel defense capable of addressing both direct threats and indirect negative externalities. We emphasize that the synergy between AI-driven solutions and policy interventions is essential for neutralizing cyber threats and mitigating their negative impact on the digital economy. Finally, we underscore the need for continuous adaptation of these strategies, especially in response to the rapid advancement of autonomous AI-driven attacks, to ensure the creation of secure and resilient digital ecosystems.

Keywords: Artificial intelligence, cybersecurity, digital
trust, policy, threat detection

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Marc Schmitt, Ivan Flechais

Abstract: The advancement of Artificial Intelligence (AI) and Machine Learning (ML) has profound implications for both the utility and security of our digital interactions. This paper investigates the transformative role of Generative AI in Social Engineering (SE) attacks. We conduct a systematic review of social engineering and AI capabilities and use a theory of social engineering to identify three pillars where Generative AI amplifies the impact of SE attacks: Realistic Content Creation, Advanced Targeting and Personalization, and Automated Attack Infrastructure. We integrate these elements into a conceptual model designed to investigate the complex nature of AI-driven SE attacks—the Generative AI Social Engineering Framework. We further explore human implications and potential countermeasures to mitigate these risks. Our study aims to foster a deeper understanding of the risks, human implications, and countermeasures associated with this emerging paradigm, thereby contributing to a more secure and trustworthy human-computer interaction.

Keywords: Artificial intelligence, Machine learning, Social engineering, Phishing, ChatGPT, Large language models

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Mario Truss, Marc Schmitt

Abstract: This paper addresses AI product prototyping, focusing on the challenges posed by the probabilistic nature of AI behavior and the limited accessibility of prototyping tools to AI non-experts. A design science research (DSR) approach is presented, which culminates in a conceptual framework for structuring the AI prototyping process with no-code AutoML technologies for textual and tabular ML use cases. Through a comprehensive literature review, key challenges were identified, and no-code AutoML was positioned as a solution. The framework describes the incorporation of non-expert input and evaluation during prototyping, leveraging the potential of no-code AutoML to enhance accessibility and interpretability. A hybrid approach combining naturalistic (case study) and artificial evaluation methods (criteria-based analysis) validated the utility of our approach, highlighting its efficacy in supporting AI non-experts and streamlining decision-making and its limitations. The implications for academia and industry focus on the strategic integration of no-code AutoML to enhance AI product development processes, mitigate risks, and foster innovation.

Keywords: AutoML, Prototyping, Human-Centered Artificial Intelligence, Digital Innovation, Product Management, Human-AI Interaction, AutoML, Machine Learning

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Marc Schmitt

Abstract: The last decades have been characterized by unprecedented technological advances, many of them powered by modern technologies such as Artificial Intelligence (AI) and Machine Learning (ML). The world has become more digitally connected than ever, but we face major challenges. One of the most significant is cybercrime, which has emerged as a global threat to governments, businesses, and civil societies. The pervasiveness of digital technologies combined with a constantly shifting technological foundation has created a complex and powerful playground for cybercriminals, which triggered a surge in demand for intelligent threat detection systems based on machine and deep learning. This paper investigates AI-based cyber threat detection to protect our modern digital ecosystems. The primary focus is on evaluating ML-based classifiers and ensembles for anomaly-based malware detection and network intrusion detection and how to integrate those models in the context of network security, mobile security, and IoT security. The discussion highlights the challenges when deploying and integrating AI-enabled cybersecurity solutions into existing enterprise systems and IT infrastructures, including options to overcome those challenges. Finally, the paper provides future research directions to further increase the security and resilience of our modern digital industries, infrastructures, and ecosystems.

Keywords: Cybersecurity, Machine learning, Digital ecosystems, Internet of things, Cyber-physical systems, Industry 5.0

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Marc Schmitt

Abstract: The realization that AI-driven decision-making is indispensable in today’s fast-paced and ultra-competitive marketplace has raised interest in industrial machine learning (ML) applications significantly. The current demand for analytics experts vastly exceeds the supply. One solution to this problem is to increase the user-friendliness of ML frameworks to make them more accessible for the non-expert. Automated machine learning (AutoML) is an attempt to solve the problem of expertise by providing fully automated off-the-shelf solutions for model choice and hyperparameter tuning. This paper analyzed the potential of AutoML for applications within business analytics, which could help to increase the adoption rate of ML across all industries. The H2O AutoML framework was benchmarked against a manually tuned stacked ML model on three real-world datasets. The manually tuned ML model could reach a performance advantage in all three case studies used in the experiment. Nevertheless, the H2O AutoML package proved to be quite potent. It is fast, easy to use, and delivers reliable results, which come close to a professionally tuned ML model. The H2O AutoML framework in its current capacity is a valuable tool to support fast prototyping with the potential to shorten development and deployment cycles. It can also bridge the existing gap between supply and demand for ML experts and is a big step towards automated decisions in business analytics. Finally, AutoML has the potential to foster human empowerment in a world that is rapidly becoming more automated and digital.

Keywords: Artificial intelligence, Machine learning, AutoML, Business analytics, Data-driven decision making, Digital transformation, Human empowerment

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Marc Schmitt

Abstract: Our fast-paced digital economy shaped by global competition requires increased data-driven decision-making based on artificial intelligence (AI) and machine learning (ML). The benefits of deep learning (DL) are manifold, but it comes with limitations that have – so far – interfered with widespread industry adoption. This paper explains why DL – despite its popularity – has difficulties speeding up its adoption within business analytics. It is shown that the adoption of deep learning is not only affected by computational complexity, lacking big data architecture, lack of transparency (black-box), skill shortage, and leadership commitment, but also by the fact that DL does not outperform traditional ML models in the case of structured datasets with fixed-length feature vectors. Deep learning should be regarded as a powerful addition to the existing body of ML models instead of a “one size fits all” solution. The results strongly suggest that gradient boosting can be seen as the go-to model for predictions on structured datasets within business analytics. In addition to the empirical study based on three industry use cases, the paper offers a comprehensive discussion of those results, practical implications, and a roadmap for future research.

Keywords: Deep Learning, Machine learning, Business analytics, Artificial intelligence, Data-driven decision making, Digital transformation, Digital strategy

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Marc Schmitt

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Marc Schmitt

Abstract: This Ph.D. thesis explores the strength and applicability of machine learning-based classifiers within the context of business analytics for data-driven decision making. The focus is on supervised binary classification on structured datasets, which are vastly present in relational databases across all enterprises. Advanced analytics has become indispensable for today’s corporate world and it is demonstrated that predictive analytics is one of the major contributors to capture business value across the financial services value chain. To test this hypothesis different models as Generalized Linear Models, Random Forest, Gradient Boosting, and Artificial Neural Networks were tested, compared, and combined to test their predictive strength and robustness in different scenarios and use cases. The results indicate the superiority of Gradient Boosting when it comes to structured datasets compared to all other classifiers. This is a major reason why the diffusion of Deep Learning within business analytics is lacking behind. Also, the ensemble learning method stacking – which uses several base learners to create a more powerful super learner – proved to be a viable tool to consistently improve upon the accuracy of even the most powerful candidate models – including Gradient Boosting. Automated Machine Learning (AutoML) was benchmarked against manually tuned models and proved to be a valuable tool to democratize predictive analytics for small to medium-sized corporations and to tackle the skill shortage for ML experts. AutoML has the potential to completely automate the predictive modeling process, but it is mainly concerned with model tuning and selection while ignoring steps at the beginning and end of the pipeline. Also, an ML pipeline setup is suggested that would – once it is automated – be able to reach human expert-level prediction accuracy for binary classification on structured datasets. All those models were tested and applied in the context of different business analytics use cases – with a focus on financial services – to solve problems in credit risk management, insurance claims prediction, and marketing and sales. All use cases demonstrate improvements in prediction accuracy and hence offer direct value gains. Throughout the thesis, there is a consideration of the advantages and constraints when it comes to the use of ML models in the industry including a translation into managerial implications. Also, general economic and business implications are discussed to understand how the field will evolve in the future.

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