NSBE - Business Analytics
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- Machine learning-based insolvency prediction for Siemens’ extended payment termsPublication . Otter, Felix; Obermeier, DanielThis thesis investigates short-term insolvency prediction using solely behavioral payment data and explainable AI in the context of Siemens' Extended Payment Terms program. A dual modeling approach combines interpretable baselines (logistic regression, random forest, and XGBoost) with a sequence-based Long Short-Term Memory (LSTM) model. The LSTM achieves moderate discrimination at natural default prevalence, while SHAP-based explanations and interviews with risk managers show that cross-model consistency and local explanations strengthen trust in the model’s outputs.
- Large language models (LLMs) for legal analysis: RAG and beyond for optimizing domain adaptation in Portuguese legal domainPublication . Barros, Tiago Mendonça Alencar; Han, QiweiThis study explores RAG systems tailored to the Portuguese legal domain, highlighting challenges in underrepresented languages. Fixed-size chunking strategies, particularly Token Text Splitter, were found to be most effective, while more advanced techniques like Recursive and Semantic splitting showed little benefits. Larger chunk sizes improved retrieval accuracy and answer quality, though the impact of chunk overlap remains inconclusive. Self-reflection techniques show promising results, particularly for weaker LLMs. Techniques such as adding a pre-post translation proved to be an efficient technique for mitigating language bias.
- Large language models (LLMS) for legal analysis rag and beyond for optimizing domain adaptation in Portuguese legal domain-advanced retrieval and prompt engineering techniques for retrieval augmented generation in the Portuguese legal domainPublication . Thaçi, Rita; Han, QiweiThis study explores RAG systems tailored to the Portuguese legal domain, highlighting challenges in underrepresented languages. Fixed-size chunking strategies, particularly Token Text Splitter, were found to be most effective, while more advanced techniques like Recursive and Semantic splitting showed little benefits. Larger chunk sizes improved retrieval accuracy and answer quality, though the impact of chunk overlap remains inconclusive. Self reflection techniques show promising results, particularly for weaker LLMs. GraphRAG shows promise with faster results than traditional RAG approaches. Reranking techniques can improve retrieval but require larger, diverse datasets. Reasoning-based and zero-shot prompting improve accuracy in multi-hop scenarios. Pre-post translation proved to be an efficient technique for mitigating language bias.
- Enhancing trust, fairness, and performance in the sharing economy: impact of airbnb recommendation system on small and large hostsPublication . Correia, Salvador Alves Pereira Soares; Guha, SreyaaThis work project explores the factors shaping Airbnb host performance, visibility, and guest sentiment, focusing on algorithmic dynamics and predictive analytics. Using diverse datasets and Machine Learning models, it uncovers how Airbnb’s recommendation system impacts trust, competitiveness, and guest engagement. Key findings reveal that review volume and host responsiveness drive performance, with smaller hosts facing challenges in visibility and pricing. Sentiment analysis highlights the outsized impact of negative reviews on reputations. The study identifies biases in Airbnb’s algorithms, emphasizing the need for fairness, transparency, and user feedback to ensure equitable opportunities for all hosts in the sharing economy.
- Enhancing trust, fairness, and performance in the sharing economy: predicting high occupancy rates in new listings of airbnb with ML modelsPublication . Peças, Duarte Nuno Ferreira Cotovio de Félix; Guha, SreyaaThis work project explores the factors shaping Airbnb host performance, visibility, and guest sentiment, focusing on algorithmic dynamics and predictive analytics. Using diverse datasets and Machine Learning models, it uncovers how Airbnb’s recommendation system impacts trust, competitiveness, and guest engagement. Key findings reveal that review volume and host responsiveness drive performance, with smaller hosts facing challenges in visibility and pricing. Sentiment analysis highlights the outsized impact of negative reviews on reputations. The study identifies biases in Airbnb’s algorithms, emphasizing the need for fairness, transparency, and user feedback to ensure equitable opportunities for all hosts in the sharing economy.
- Enhancing trust, fairness, and performance in the sharing economy: measuring the impact of review volume on airbnb listingsPublication . Bensimon, Tomás Valentim Barbosa Droznik; Guha, SreyaaThis work project explores the factors shaping Airbnb host performance, visibility, and guest sentiment, focusing on algorithmic dynamics and predictive analytics. Using diverse datasets and Machine Learning models, it uncovers how Airbnb’s recommendation system impacts trust, competitiveness, and guest engagement. Key findings reveal that review volume and host responsiveness drive performance, with smaller hosts facing challenges in visibility and pricing. Sentiment analysis highlights the outsized impact of negative reviews on reputations. The study identifies biases in Airbnb’s algorithms, emphasizing the need for fairness, transparency, and user feedback to ensure equitable opportunities for all hosts in the sharing economy.
- Examining political polarization in the German bundestag using large language models: historical trends and a contemporary analysis - bert modelPublication . Amenda, Anna Charlotte; Shen, YufeiAnalyzing political polarization has become increasingly relevant, particularly in light of the recent government crisis in Germany. This research investigates how political polarization in Germany has evolved over time and identifies factors influencing polarization in the currente lectoral term (2021-2025). We utilize an ensemble of three Large Language Models, BERT, GPT-4o-mini, and LLaMA, to classify speeches in the German Bundestag as polarizing. This approach is complemented by sentiment and structural analysis. Our results show a significant increase in political polarization across the last two electoral terms, with the entry of the right-wing party Alternative für Deutschland (AfD) into the Bundestag occurring concurrently. Political parties, followed by topics discussed, have emerged as the most influential factors in polarization. Meanwhile, the recent dissolution of the governing coalition was only subtly indicated by a reduction of applause among governing parties.
- The impact of video assistant referee (VAR) technology on refereeing performance and fairness in football: Betting on certainty: how video assistant referee has altered football odds accuracyPublication . Oliveira, Luís Filipe Ribeiro de; Batikas, MichailIn recent years, the introduction of new regulations and technologies across various sectors has prompted critical examination of their broader implications. This phenomenon is no different in the realm of professional football with the implementation of the Video Assistant Referee (VAR). This work seeks to analyse VAR’s impact on various metrics such as referee behaviour, teams’ performance, players behaviour and football bets. Through the use of a data-driven approach, utilizing methods such as Difference-in-Differences (DiD) approach, Event studies and general descriptive statistics which help paint a picture of results. The study was conducted on both the English Premier League and the English Championship, using the latter as a control group.
- Examining political polarization in the German bundestag using large language models: historical trends and a contemporary analysis - Llama modelPublication . Greiner, Antonius Jonas; Shen, YufeiAnalyzing political polarization has become increasingly relevant, particularly in light of the recent government crisis in Germany. This research investigates how political polarization in Germany has evolved over time and identifies factors influencing polarization in the current electoral term (2021-2025). We utilize an ensemble of three Large Language Models, BERT, GPT-4o-mini, and LLaMA, to classify speeches in the German Bundestag as polarizing. This approach is complemented by sentiment and structural analysis. Our results show a significant increase in political polarization across the last two electoral terms, with the entry of the right-wing party Alternative für Deutschland (AfD) into the Bundestag occurring concurrently. Political parties, followed by topics discussed, have emerged as the most influential factors in polarization. Meanwhile, the recent dissolution of the governing coalition was only subtly indicated by a reduction of applause among governing parties.
- Examining political polarization in the German bundestag using large language models: historical trends and a contemporary analysis - sentiment and structural analysisPublication . Bienert, Silja Sophie; Shen, YufeiAnalyzing political polarization has become increasingly relevant, particularly in light of the recent government crisis in Germany. This research investigates how political polarization in Germany has evolved over time and identifies factors influencing polarization in the current electoral term (2021-2025). We utilize an ensemble of three Large Language Models, BERT, GPT-4o-mini, and LLaMA, to classify speeches in the German Bundestag as polarizing. This approach is complemented by sentiment and structural analysis. Our results show a significant increase in political polarization across the last two electoral terms, with the entry of the right-wing party Alternative für Deutschland (AfD) into the Bundestag occurring concurrently. Political parties, followed by topics discussed, have emerged as the most influential factors in polarization. Meanwhile, the recent dissolution of the governing coalition was only subtly indicated by a reduction of applause among governing parties