Machine learning (ML) is a branch of artificial intelligence that uses data and algorithms to imitate human behaviour (Brown, 2021). It is used in a variety of financial applications such as fraud detection, automatic trading, robo-advisors, loan underwriting, and targeted advertising. Machine learning revolutionizes how we invest, trade, advertise, and do business more broadly.
Machine learning also transforms how we conduct research and generate business insights. It offers unprecedented opportunities to use big data to identify patterns and extend our understanding of mechanisms. For example, creditors increasingly use ESG information to assess default risks. Currently, this assessment is predominantly of qualitative nature meaning that the analyst screens available material and incorporates the resulting impression into their assessment. Research involving machine learning could allow us to systematize the interrelations and generate tangible and actionable insights, including quantitative prediction of credit default probability.
A key question that arises upon this new opportunity is how to integrate machine learning in research design. Is it an add-on? Or a replacement? Or does using machine learning in research require a completely new way of designing studies altogether? This article discusses different ways to integrate machine learning into research design and their implication for knowledge generation and product creation. We use ESG and credit default as an illustrative case study. Specifically, we demonstrate the applicability of an ML-driven research design approach to determine the inter-relationships between ESG factors and credit default probability.
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