Ausgewählte Publikationen

Meine Forschungsarbeit konzentriert sich auf die Anwendung von Künstlicher Intelligenz im Bankwesen. Durch die Untersuchung von state-of-the-art Machine Learning Algorithmen und wie sie im Finanzsektor eingesetzt werden können, arbeite ich daran, den Nutzen von fortschrittlichen Technologien in diesem wichtigen Wirtschaftsbereich zu verstehen und zu fördern.

Die nachfolgenden Publikationen repräsentieren eine Auswahl meiner Forschungsergebnisse. 

Published in: International Journal of Information Technology & Decision Making (2023). DOI:10.1142/S0219622023500694.

With Company2Vec, the paper proposes a novel application in representation learning. The model analyzes business activities from unstructured company website data using Word2Vec and dimensionality reduction. Company2Vec maintains semantic language structures and thus creates efficient company embeddings in fine-granular industries. These semantic embeddings can be used for various applications in banking.

Direct relations between companies and words allow semantic business analytics (e.g., top-n words for a company). Furthermore, industry prediction is presented as a supervised learning application and evaluation method. The vectorized structure of the embeddings allows measuring companies’ similarities with the cosine distance. Company2Vec hence offers a more fine-grained comparison of companies than the standard industry labels (NACE). This property is relevant for unsupervised learning tasks, such as clustering. An alternative industry segmentation is shown with k-means clustering on the company embeddings. Finally, this paper proposes three algorithms for (1) firm-centric, (2) industry-centric and (3) portfolio-centric peer-firm identification.

doi.org/10.48550/arXiv.2411.14463

This paper explores the growing impact of AI and NLP in bank marketing, highlighting their evolving roles in enhancing marketing strategies, improving customer engagement, and creating value within this sector. While AI and NLP have been widely studied in general marketing, there is a notable gap in understanding their specific applications and potential within the banking sector. This research addresses this specific gap by providing a systematic review and strategic analysis of AI and NLP applications in bank marketing, focusing on their integration across the customer journey and operational excellence. Employing the PRISMA methodology, this study systematically reviews existing literature to assess the current landscape of AI and NLP in bank marketing. Additionally, it incorporates semantic mapping using Sentence Transformers and UMAP for strategic gap analysis to identify underexplored areas and opportunities for future research.
The systematic review reveals limited research specifically focused on NLP applications in bank marketing. The strategic gap analysis identifies key areas where NLP can further enhance marketing strategies, including customer-centric applications like acquisition, retention, and personalized engagement, offering valuable insights for both academic research and practical implementation. This research contributes to the field of bank marketing by mapping the current state of AI and NLP applications and identifying strategic gaps. The findings provide actionable insights for developing NLP-driven growth and innovation frameworks and highlight the role of NLP in improving operational efficiency and regulatory compliance. This work has broader implications for enhancing customer experience, profitability, and innovation in the banking industry.

 

Published in: Information Fusion (2025). doi.org/10.1016/j.inffus.2025.102973

Traditional banks are increasingly challenged by FinTechs, particularly in leveraging advanced technologies to enhance operational efficiency. Our study addresses this by focusing on improving the efficiency of document-intensive business processes in banking. We review the landscape of business documents in the customer banking segment, which often includes text, layout, and visuals, indicating that more than plain NLP is required for effective document analytics and process automation.
We evaluate multimodal models that integrate these elements, analyzing the interplay of text, layout, and visual components. Our comparative analysis features multimodal, cross-lingual models such as LayoutXLM and includes comparisons with large language models like GPT-4, contrasting their capabilities with traditional unimodal models like BERT and GPT-3.5. The results confirm that LayoutXLM significantly outperforms both unimodal classifiers and other large multimodal models in processing complex banking documents, leveraging integrated visual and layout cues effectively. Remarkably, with as little as 250 document pages covering 10 different token classes used for fine-tuning, LayoutXLM achieves an F1 score exceeding 75%, demonstrating its efficient few-shot learning capabilities.
In our ablation studies, we demonstrate that multimodal systems can achieve significant performance improvements with limited labeled data, effectively managing class imbalance. This enhances their deployment and scalability, highlighting their transformative potential in document-intensive processes. Overall, this paper contributes original empirical evidence on the superior efficiency and effectiveness of layout-aware multimodal models like LayoutXLM, along with practical banking document analytics insights.

Forschungsinteressen

Als Data Scientist und Forscher vereine ich die Erkenntnisse aus der akademischen Welt mit praxisnahen Anwendungen im Bankwesen. Mein Forschungsschwerpunkt auf unstrukturierten Daten, NLP und speziellen neuronalen Netzen fließt direkt in meine Arbeit im Bereich Sales Analytics ein. Diese Kombination ermöglicht mir, innovative Lösungen für das Firmenkundengeschäft zu entwickeln und Theorie in die Praxis umzusetzen, um realen Geschäftsanforderungen gerecht zu werden.

  • Multimodale Modelle
  • NLP (Natural Language Processing)
  • Unstrukturierte Daten
  • Big Data Technologien
  • Banking
  • Embedding Techniques
  • Company Similarities
  • Sales Analytics
  • Image Processing
  • Large Language Models
  • KI Anwendungen
  • Sentiment Analysis
  • Text Mining
  • Image Filter Learning
  • Representation Learning
  • Document Analytics