- MSc thesis
- Διοίκηση Επιχειρήσεων (MBA)
- 14 Σεπτεμβρίου 2024
- Αγγλικά
- 102
- Adamides, Emmanouil
- Artificial Intelligence (AI) | Machine Learning (ML) | Open Data | Technology Management | FinTech Industry | Business Models | Innovation in FinTech | Case Studies | Utilization of ML and Open Data | Regulatory Frameworks | Data Analytics in FinTech
- Master in Business Administration
- 4
- 5
- 67
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Within the dynamic FinTech industry, the fast development of technologies like ML and open data provides exciting opportunities for new services and products, innovation, and expansion. Nevertheless, the acceptance and application of these technologies are not without challenges. This study examines FinTech organizations' challenges when integrating ML and open data into their business models and strategies. This study offers a first investigation of these obstacles by doing a brief literature review, conducting interviews with FinTech executives, and analyzing a case study of a Greek FinTech business.
The analysis of interviews revealed a range of difficulties and possibilities associated with using Machine Learning and Open Data. Organizations recognize the significance of these technologies in improving business models and attaining a competitive advantage. Nevertheless, the adoption of technical infrastructure differed across enterprises, with some companies opting for internal models while others opted for third-party suppliers. The main factors influencing the adoption were the need for top-notch, all-encompassing data to enhance company intelligence and consumer insights.
Several primary obstacles were discovered, including legislative limitations, apprehensions around data protection, and the lack of transparency and interpretability in particular machine-learning algorithms. Enduring challenges included overseeing data accuracy, revising machine-learning models, and incorporating new technologies, particularly in response to post-COVID-19 behavior shifts requiring ongoing retraining. Financial rules substantially impacted the use of technology, namely data security procedures, which resulted in restrictions on the usage of cloud services. Although there are challenges to overcome, possibilities are available, such as using natural language processing (NLP) to automate customer support and creating "internal LLM copilots" to make operations more efficient.
The case study further verified the themes explored using thematic analysis to analyze the interviews. The challenges identified in the FinTech case study of this thesis include the unpredictable changes in format and delivery of open data by volatile open sources, the absence of clear communication about impending changes to open data consumers (companies), inadequate documentation in open data APIs, technical complexities in extracting text from financial statements published by open sources, the absence of standardized open data from various open government sources, and the difficulties in constructing reliable data pipelines that support high-quality data and machine-learning components.
This research highlights the need for strategic planning, responsible innovation, and a comprehensive awareness of emerging trends in helping FinTech organizations navigate the difficulties of integrating ML and open data.
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- Hellenic Open University
- Αναφορά Δημιουργού - Μη Εμπορική Χρήση - Παρόμοια Διανομή 4.0 Διεθνές