AI in ESG for Financial Institutions: An Industrial Survey
- URL: http://arxiv.org/abs/2403.05541v1
- Date: Sat, 3 Feb 2024 02:14:47 GMT
- Title: AI in ESG for Financial Institutions: An Industrial Survey
- Authors: Jun Xu,
- Abstract summary: The paper surveys the industrial landscape to delineate the necessity and impact of AI in bolstering ESG frameworks.
Our survey categorizes AI applications across three main pillars of ESG, illustrating how AI enhances analytical capabilities, risk assessment, customer engagement, reporting accuracy and more.
The paper also addresses the imperative of responsible and sustainable AI, emphasizing the ethical dimensions of AI deployment in ESG-related banking processes.
- Score: 4.893954917947095
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: The burgeoning integration of Artificial Intelligence (AI) into Environmental, Social, and Governance (ESG) initiatives within the financial sector represents a paradigm shift towards more sus-tainable and equitable financial practices. This paper surveys the industrial landscape to delineate the necessity and impact of AI in bolstering ESG frameworks. With the advent of stringent regulatory requirements and heightened stakeholder awareness, financial institutions (FIs) are increasingly compelled to adopt ESG criteria. AI emerges as a pivotal tool in navigating the complex in-terplay of financial activities and sustainability goals. Our survey categorizes AI applications across three main pillars of ESG, illustrating how AI enhances analytical capabilities, risk assessment, customer engagement, reporting accuracy and more. Further, we delve into the critical con-siderations surrounding the use of data and the development of models, underscoring the importance of data quality, privacy, and model robustness. The paper also addresses the imperative of responsible and sustainable AI, emphasizing the ethical dimensions of AI deployment in ESG-related banking processes. Conclusively, our findings suggest that while AI offers transformative potential for ESG in banking, it also poses significant challenges that necessitate careful consideration. The final part of the paper synthesizes the survey's insights, proposing a forward-looking stance on the adoption of AI in ESG practices. We conclude with recommendations with a reference architecture for future research and development, advocating for a balanced approach that leverages AI's strengths while mitigating its risks within the ESG domain.
Related papers
- Ethical and Scalable Automation: A Governance and Compliance Framework for Business Applications [0.0]
This paper introduces a framework ensuring that AI must be ethical, controllable, viable, and desirable.
Different case studies validate this framework by integrating AI in both academic and practical environments.
arXiv Detail & Related papers (2024-09-25T12:39:28Z) - Integrating ESG and AI: A Comprehensive Responsible AI Assessment Framework [15.544366555353262]
ESG-AI framework was developed based on insights from engagements with 28 companies.
It provides an overview of the environmental and social impacts of AI applications, helping users such as investors assess the materiality of AI use.
It enables investors to evaluate a company's commitment to responsible AI through structured engagements and thorough assessment of specific risk areas.
arXiv Detail & Related papers (2024-08-02T00:58:01Z) - Particip-AI: A Democratic Surveying Framework for Anticipating Future AI Use Cases, Harms and Benefits [54.648819983899614]
General purpose AI seems to have lowered the barriers for the public to use AI and harness its power.
We introduce PARTICIP-AI, a framework for laypeople to speculate and assess AI use cases and their impacts.
arXiv Detail & Related papers (2024-03-21T19:12:37Z) - Testing autonomous vehicles and AI: perspectives and challenges from cybersecurity, transparency, robustness and fairness [53.91018508439669]
The study explores the complexities of integrating Artificial Intelligence into Autonomous Vehicles (AVs)
It examines the challenges introduced by AI components and the impact on testing procedures.
The paper identifies significant challenges and suggests future directions for research and development of AI in AV technology.
arXiv Detail & Related papers (2024-02-21T08:29:42Z) - Towards Responsible AI in Banking: Addressing Bias for Fair
Decision-Making [69.44075077934914]
"Responsible AI" emphasizes the critical nature of addressing biases within the development of a corporate culture.
This thesis is structured around three fundamental pillars: understanding bias, mitigating bias, and accounting for bias.
In line with open-source principles, we have released Bias On Demand and FairView as accessible Python packages.
arXiv Detail & Related papers (2024-01-13T14:07:09Z) - Investigating Responsible AI for Scientific Research: An Empirical Study [4.597781832707524]
The push for Responsible AI (RAI) in such institutions underscores the increasing emphasis on integrating ethical considerations within AI design and development.
This paper aims to assess the awareness and preparedness regarding the ethical risks inherent in AI design and development.
Our results have revealed certain knowledge gaps concerning ethical, responsible, and inclusive AI, with limitations in awareness of the available AI ethics frameworks.
arXiv Detail & Related papers (2023-12-15T06:40:27Z) - A Vision for Operationalising Diversity and Inclusion in AI [5.4897262701261225]
This study seeks to envision the operationalization of the ethical imperatives of diversity and inclusion (D&I) within AI ecosystems.
A significant challenge in AI development is the effective operationalization of D&I principles.
This paper proposes a vision of a framework for developing a tool utilizing persona-based simulation by Generative AI (GenAI)
arXiv Detail & Related papers (2023-12-11T02:44:39Z) - The AI Revolution: Opportunities and Challenges for the Finance Sector [12.486180180030964]
The application of AI in the financial sector is transforming the industry.
However, along with these benefits, AI also presents several challenges.
These include issues related to transparency, interpretability, fairness, accountability, and trustworthiness.
The use of AI in the financial sector further raises critical questions about data privacy and security.
Despite the global recognition of this need, there remains a lack of clear guidelines or legislation for AI use in finance.
arXiv Detail & Related papers (2023-08-31T08:30:09Z) - Fairness in Agreement With European Values: An Interdisciplinary
Perspective on AI Regulation [61.77881142275982]
This interdisciplinary position paper considers various concerns surrounding fairness and discrimination in AI, and discusses how AI regulations address them.
We first look at AI and fairness through the lenses of law, (AI) industry, sociotechnology, and (moral) philosophy, and present various perspectives.
We identify and propose the roles AI Regulation should take to make the endeavor of the AI Act a success in terms of AI fairness concerns.
arXiv Detail & Related papers (2022-06-08T12:32:08Z) - An interdisciplinary conceptual study of Artificial Intelligence (AI)
for helping benefit-risk assessment practices: Towards a comprehensive
qualification matrix of AI programs and devices (pre-print 2020) [55.41644538483948]
This paper proposes a comprehensive analysis of existing concepts coming from different disciplines tackling the notion of intelligence.
The aim is to identify shared notions or discrepancies to consider for qualifying AI systems.
arXiv Detail & Related papers (2021-05-07T12:01:31Z) - Toward Trustworthy AI Development: Mechanisms for Supporting Verifiable
Claims [59.64274607533249]
AI developers need to make verifiable claims to which they can be held accountable.
This report suggests various steps that different stakeholders can take to improve the verifiability of claims made about AI systems.
We analyze ten mechanisms for this purpose--spanning institutions, software, and hardware--and make recommendations aimed at implementing, exploring, or improving those mechanisms.
arXiv Detail & Related papers (2020-04-15T17:15:35Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.