Dr Daniel Dao works as a Research Associate at the Oxford Sustainable Finance Group and an Associated Research Fellow at the Centre for Greening Finance and Investment, The University of Oxford; a Research Economist (Consultant) at the International Bank for Reconstruction and Development (IBRD), The World Bank, Washington DC Headquarters. He is a CFA Charterholder and an active member of the CFA UK. Previously, he worked for University of Strathclyde (Research Associate) and Queen Mary University of London (Associate Lecturer). He has earned PhD (2024) from University of Coventry, UK and MBA (2017) in Finance from Bangor University, UK. 

He has shown a strong commitment and passion for international development and high-impact policy research. His expertise lies in the fields of Sustainable Finance; Fintech; Entrepreneurial Finance, and Financial Economics. His proficiency extends to data science techniques and advanced analytics, with a specific focus on artificial intelligence, machine learning, and natural language processing (NLP). 

He has published in internationally leading academic journals, including British Journal of Management (ABS-4), Risk Analysis (ABS-4), International Journal of Finance and Economics (ABS-3), Information and Management (ABS-3), and various policy and industry research reports affiliated with The World Bank (Dominican Economic Memorandum, 2023; World Development Report, 2024; Labour and Policy Reform, 2024) and Fintech Scotland (White papers and Blog Posts in AI, Fintech, ESG, and Financial Regulation). He has acted as a reviewer for a number of highly-ranked journals, including The European Journal of Finance, Information and Management, Energy Economics, International Journal of Finance and Economics, etc.

He is actively engaged in teaching and supervising modules in financial economics at various prestigious universities, including: University College London – UCL, Regents’ University London, Queen Mary University of London (previously) and Vietnam National University (previously).