Research Staff

Research Staff

Noh, Sungho Research Fellow Financial Services Industry
Member of the Center, Digital Finance Research Center


Ph.D Economics, University of Maryland, College Park, MD, 2018
MA Economics, University of Maryland, College Park, MD, 2014
BA Economics, Seoul National University, Seoul, Republic of Korea, 2011
Professional Experience
Research Fellow, Korea Capital Market Institute (2022. 12. ~ present)
Senior Financial Economist, Public Company Accounting Oversight Board (2022. 3. ~ 2022. 11.)
Financial Economist, Public Company Accounting Oversight Board (2018. 7. ~ 2022. 2.)
Lecturer, University of Maryland (2020. 1. ~ 2021. 6.)



Significance of Structured Corporate Financial Disclosures in the Era of AI / Apr. 02, 2024
Financial disclosures are the primary communication channel between public companies and their investors. They are vital for providing comprehensive and reliable insights into corporate financial status through institutional verification procedures. Given the quantitative expansion and growing complexity of disclosure data, investors face challenges in efficiently processing extensive information for decision-making. In response to this issue, there has been a rapid advancement in automating the collection and analysis of disclosure data, driven by AI technologies such as natural language processing models in recent years. The push to structure and digitize financial disclosures is partly driven by the increasing presence of “machine” readers. The adoption of XBRL has enhanced the machine readability of disclosure data by systematically organizing intricate financial accounts and footnotes into a standardized framework. Additionally, in advanced capital markets, continuous efforts are dedicated to developing financial reporting infrastructure such as archives to enrich access to digital disclosure data. Such investments would further promote structured financial disclosures and improve digital accessibility, enabling investors to efficiently and timely utilize financial information. Achieving this goal necessitates improving online accessibility and elevating the significance of financial disclosures as well as understanding the concept of machine readability. Moreover, it is crucial for public companies and their investors to proactively communicate with each other and for regulators to support such behavior as to minimize the costs of adopting structured digital disclosures and addressing the issue of asymmetric dissemination of information.
Generative AI-driven Productivity Innovation and the Financial Services Industry’s Responses / Sep. 19, 2023
Amid heated discussions on generative AI-based innovation for productivity, the financial services industry is also examining how the use of generative AI can help increase profitability and reduce operational cost. Generative AI differs qualitatively from previous technological innovations in that it serves as a tool to assist human cognitive functions. In the past, new technologies such as automated machines raised production efficiency by replacing low-skilled workers, while generative AI has been primarily used to improve the skill level of professional and high-paying occupations. Such a difference highlights the need for organizational restructuring as a part of the longer-term strategy of the financial sector since the industry typically involves a higher portion of professionals performing analytical tasks.Compared to advanced economies, there seems to be room for Korea’s financial services industry to enhance IT capabilities. Korea has a relatively small share of employees in the financial sector with AI skills than global front runners in the field, such as the U.S. and India. Furthermore, Korea’s financial investment firms represent a small proportion of IT-related human resources and budget compared to global leading firms. Thus, it is important to raise awareness of new technologies such as generative AI and expand investment in intangible assets such as research capabilities across the entire industry. Throughout the process, managerial support with a long-term strategy and effective communication between subject matter experts and AI technicians are key factors in successfully realizing AI-driven innovation.

Other Activities


"Nonparametric identification and estimation of heterogeneous causal effects under conditional independence," Econometric Reviews, 2023 (https://doi.org/10.1080/07474938.2023.2178140)