Daniel Dao, PhD, CFA, MBA

Policy & Industry Publications

2024

“ESG Greenwashing and Applications of AI in Measurement”
(with Ngoc Chu, James Bowden, & Mark Cummins)
Affiliation: Financial Regulation Innovation Lab, University of Strathclyde
Financial Regulation Innovation Lab White Paper Series, Fintech Scotland (Commissioning body)

“ESG greenwashing” refers to the strategic communication tactics firms use to selectively disclose their ESG conduct to stakeholders. ESG greenwashing strategy, while it may attract and satisfy stakeholders at the beginning, may cause different issues for firms later, such as adverse publicity, lobbying, or boycott campaigns by consumer or pressure groups or divestment by socially responsible investors. The complex impacts of ESG greenwashing underscore the imperative of discerning and quantifying instances of such practices. We aim to consolidate recent literature reviews of ESG greenwashing, methodologies to measure ESG greenwashing and developing applications of AI, text analysis and machine learning models to advance such measurement. This white paper makes significant contributions to policy developments, such as the greenwashing regulations of the UK FCA and the European Parliament.
Research Themes: Fintech, AI, Sustainable Finance (ESG)

“Critique of the UK’s Pro-innovation Approach to AI Regulation and Implications for Financial Regulation Innovation”
(solo-authored)
Affiliation: Financial Regulation Innovation Lab, University of Strathclyde
Blog Post, Fintech Scotland

Recently, artificial intelligence (AI) is widely recognised as a pivotal technological advancement with the capacity to profoundly reshape societal dynamics. It is celebrated for its potential to enhance public services, create high-quality employment opportunities, and power the future. However, there remains a notable opacity regarding the potential threats it poses to life, security, and related domains, thus requiring a pro-active approach to regulation. To address this gap, the UK Government has released an AI white paper outlining its pro-innovation approach to regulating AI. While this white paper symbolises the contributions and endeavours aimed at providing innovative and dynamic solutions to tackle the significant challenge posed by AI, it is important to acknowledge that there are still certain limitations which the white paper may refine in subsequent iterations.
Research Themes: Fintech, AI, Financial Regulation

“Explainable AI for Financial Risk Management”
(with James Bowden, Mark Cummins, & Kushagra Jain)
Affiliation: Financial Regulation Innovation Lab, University of Strathclyde
Financial Regulation Innovation Lab White Paper Series, Fintech Scotland (Commissioning body)

We overview the opportunities that Explainable AI (XAI) offer to enhance financial risk management practice, which feeds into the objective of simplifying compliance for banking and financial services organisations. We provide a clear problem statement, which makes the case for explainability around AI systems from the business and the regulatory perspective. A comprehensive literature review positions the study and informs the solution framework proposed. The solution framework sets out the key considerations of an organisation in terms of setting strategic priorities around the explainability of AI systems, the institution of appropriate model governance structures, the technical considerations in XAI analytics, and the imperative to evaluate explanations. The use case demonstration brings the XAI discussion to life through an application to AI based credit risk management, with focus on credit default prediction.
Research Themes: Fintech, AI, Financial Risk

“Simplifying Compliance through Explainable Intelligent Automation”
(with James Bowden, Mark Cummins, & Kushagra Jain)
Affiliation: Financial Regulation Innovation Lab, University of Strathclyde
Financial Regulation Innovation Lab White Paper Series, Fintech Scotland (Commissioning body)

We discuss how explainability in AI-systems can deliver transparency and build trust towards greater adoption of automation to support financial regulation compliance among banks and financial services firms. We uniquely propose the concept of Explainable Intelligent Automation as the next generation of Intelligent Automation. Explainable Intelligent Automation seeks to leverage emerging innovations in the area of Explainable Artificial Intelligence. AI systems underlying Intelligent Automation bring considerable advantages to the task of automating compliance processes. A barrier to AI adoption though is the black-box nature of the machine learning techniques delivering the outcomes, which is exacerbated by the pursuit of increasingly complex frameworks, such as deep learning, in the delivery of performance accuracy. Through articulating the business value of Robotic Process Automation and Intelligent Automation, we consider the potential for Explainable Intelligent Automation to add value. The solution framework sets out the Explainable Intelligent Automation framework, as the interface of Robotic Process Automation, Business Process Management and Explainable Artificial Intelligence. We discuss key considerations of an organisation in terms of setting strategic priorities around the explainability of AI systems, the technical considerations in Explainable Artificial Intelligence analytics, and the imperative to evaluate explanations.
Research Themes: Fintech, AI, Financial Regulation

2023

“Reversing the Productivity Decline – Dominican Republic Country Economic Memorandum – Sustaining Economic Growth”
(with Alexis Cruz-Rodriguez‬ & James Sampi)
Affiliation: International Bank for Reconstruction and Development (IBRD), The World Bank
World Bank Publications 

The Dominican per capita income has rapidly converged towards US per capita income but slowing productivity growth poses risks to sustain economic growth. Despite the rapid GDP growth in the last two decades, productivity growth has continuedly declined. The Dominican’s total factor productivity (TFP) level represented 70 percent of US levels in 1990, but in the absence of further reforms it declined by 10 percentage points by 2019.
Therefore, this chapter seeks to understand the productivity dynamics in the Dominican Republic by accounting for sectoral differences. There are three channels through which productivity can be affected, the within-firm (capabilities, innovation, etc.), the between (allocation of resources across firms in a sector) and net entry components (entry of productive and exiting of unproductive firms in the market). The importance of those channels varies across sectors. While the between component in the construction and transport sectors negatively contributed to productivity growth, a negative contribution emerges from the net entry component in the manufacturing and hospitality sectors, which offset the benefits from innovation.
Research Themes: Financial Economics, Productivity