The AI Revolution: Opportunities and Challenges for the Finance Sector

• URL: http://arxiv.org/abs/2308.16538v1
• Date: Thu, 31 Aug 2023 08:30:09 GMT
• Title: The AI Revolution: Opportunities and Challenges for the Finance Sector
• Authors: Carsten Maple, Lukasz Szpruch, Gregory Epiphaniou, Kalina Staykova, Simran Singh, William Penwarden, Yisi Wen, Zijian Wang, Jagdish Hariharan, Pavle Avramovic
• Abstract summary: 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.
• Score: 12.486180180030964
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This report examines Artificial Intelligence (AI) in the financial sector, outlining its potential to revolutionise the industry and identify its challenges. It underscores the criticality of a well-rounded understanding of AI, its capabilities, and its implications to effectively leverage its potential while mitigating associated risks. The potential of AI potential extends from augmenting existing operations to paving the way for novel applications in the finance sector. The application of AI in the financial sector is transforming the industry. Its use spans areas from customer service enhancements, fraud detection, and risk management to credit assessments and high-frequency trading. 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. A further issue identified in this report is the systemic risk that AI can introduce to the financial sector. Being prone to errors, AI can exacerbate existing systemic risks, potentially leading to financial crises. Regulation is crucial to harnessing the benefits of AI while mitigating its potential risks. Despite the global recognition of this need, there remains a lack of clear guidelines or legislation for AI use in finance. This report discusses key principles that could guide the formation of effective AI regulation in the financial sector, including the need for a risk-based approach, the inclusion of ethical considerations, and the importance of maintaining a balance between innovation and consumer protection. The report provides recommendations for academia, the finance industry, and regulators.

Related papers

• Redefining Finance: The Influence of Artificial Intelligence (AI) and Machine Learning (ML) [2.3931689873603594]
  With rapid transformation of technologies, the fusion of Artificial Intelligence (AI) and Machine Learning (ML) in finance is disrupting the entire ecosystem. The segments of financial institutions which are getting heavily influenced are retail banking, wealth management, corporate banking & payment ecosystem.
  arXiv  Detail & Related papers  (2024-10-21T12:32:17Z)
• Responsible AI Question Bank: A Comprehensive Tool for AI Risk Assessment [17.026921603767722]
  The study introduces our Responsible AI (RAI) Question Bank, a comprehensive framework and tool designed to support diverse AI initiatives. By integrating AI ethics principles such as fairness, transparency, and accountability into a structured question format, the RAI Question Bank aids in identifying potential risks.
  arXiv  Detail & Related papers  (2024-08-02T22:40:20Z)
• Open Problems in Technical AI Governance [93.89102632003996]
  Technical AI governance refers to technical analysis and tools for supporting the effective governance of AI. This paper is intended as a resource for technical researchers or research funders looking to contribute to AI governance.
  arXiv  Detail & Related papers  (2024-07-20T21:13:56Z)
• Computing Power and the Governance of Artificial Intelligence [51.967584623262674]
  Governments and companies have started to leverage compute as a means to govern AI. compute-based policies and technologies have the potential to assist in these areas, but there is significant variation in their readiness for implementation. naive or poorly scoped approaches to compute governance carry significant risks in areas like privacy, economic impacts, and centralization of power.
  arXiv  Detail & Related papers  (2024-02-13T21:10:21Z)
• AI in ESG for Financial Institutions: An Industrial Survey [4.893954917947095]
  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.
  arXiv  Detail & Related papers  (2024-02-03T02:14:47Z)
• The risks of risk-based AI regulation: taking liability seriously [46.90451304069951]
  The development and regulation of AI seems to have reached a critical stage. Some experts are calling for a moratorium on the training of AI systems more powerful than GPT-4. This paper analyses the most advanced legal proposal, the European Union's AI Act.
  arXiv  Detail & Related papers  (2023-11-03T12:51:37Z)
• Managing extreme AI risks amid rapid progress [171.05448842016125]
  We describe risks that include large-scale social harms, malicious uses, and irreversible loss of human control over autonomous AI systems. There is a lack of consensus about how exactly such risks arise, and how to manage them. Present governance initiatives lack the mechanisms and institutions to prevent misuse and recklessness, and barely address autonomous systems.
  arXiv  Detail & Related papers  (2023-10-26T17:59:06Z)
• A Brief Overview of AI Governance for Responsible Machine Learning Systems [3.222802562733787]
  This position paper seeks to present a brief introduction to AI governance, which is a framework designed to oversee the responsible use of AI. Due to the probabilistic nature of AI, the risks associated with it are far greater than traditional technologies.
  arXiv  Detail & Related papers  (2022-11-21T23:48:51Z)
• Exploring Explainable AI in the Financial Sector: Perspectives of Banks and Supervisory Authorities [0.3670422696827526]
  The aim of this study was to investigate the perspectives of supervisory authorities and regulated entities regarding the application of xAI in the financial sector. We found that for the investigated use cases a disparity exists between supervisory authorities and banks regarding the desired scope of explainability of AI systems. We argue that the financial sector could benefit from clear differentiation between technical AI (model) ex-plainability requirements and explainability requirements of the broader AI system in relation to applicable laws and regulations.
  arXiv  Detail & Related papers  (2021-11-03T14:11:37Z)
• 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.