The rapid advancement of AI and machine learning holds tremendous promise for transforming the financial services industry. However, smaller financial institutions often lack sufficient high-quality data to fully leverage these advancements, especially for fraud detection, risk management, and operational efficiency (Altman et al., 2024; Jensen et al., 2023). Synthetic data has emerged as an innovative solution to this challenge, enabling institutions to securely share data while maintaining privacy, thus overcoming limitations inherent in traditional approaches such as federated learning and open banking (Baabdullah et al., 2024; Chatterjee et al., 2024; He et al., 2023).

Building upon insights from our recent research paper SynDEc: A Synthetic Data Ecosystem, your thesis will contribute to shaping the future of data ecosystems through rigorous scientific investigation and practical application. Specifically, you will explore crucial aspects such as the development of robust back-testing mechanisms to verify the quality of synthetic data, strategies for effectively incentivizing institutional participation, and methods to ensure trust and interoperability across the ecosystem (Gelhaar & Otto, 2020; Oliveira & Lóscio, 2018).

• Design and evaluation of robust back-testing mechanisms to ensure synthetic data accurately reflects real-world financial transactions, safeguarding the ecosystem against performance degradation due to poor data quality.
• Development of incentive frameworks for financial institutions to encourage active participation in synthetic data sharing, considering the varying needs of small vs. large institutions.
• Empirical assessment of network effects within synthetic data ecosystems, determining optimal configurations for data sharing and integration to maximize benefits for all participants.
• While quantitative methodologies are preferred, qualitative approaches that provide deep insights into incentive design, trust-building, and regulatory frameworks are also encouraged.

• Structured and independent research, driven by curiosity and practical relevance.
• Commitment to producing a high-quality thesis within approximately six months.

• Intensive supervision and regular feedback sessions with experienced researchers.
• Access to industry partners, domain experts, and unique datasets.
• Opportunities to contribute to leading-edge research with potential for publication. 
   

If you are interested, please reach out via email to mahei.li@unisg.ch. I look forward to discussing your thesis ideas and supporting you in achieving exceptional results.