Artificial Intelligence (AI)

The Role of AI in Financial Forecasting: ChatGPT’s Potential and Challenges ArXiv ID: 2411.13562 “View on arXiv” Authors: Unknown Abstract The outlook for the future of artificial intelligence (AI) in the financial sector, especially in financial forecasting, the challenges and implications. The dynamics of AI technology, including deep learning, reinforcement learning, and integration with blockchAIn and the Internet of Things, also highlight the continued improvement in data processing capabilities. Explore how AI is reshaping financial services with precisely tAIlored services that can more precisely meet the diverse needs of individual investors. The integration of AI challenges regulatory and ethical issues in the financial sector, as well as the implications for data privacy protection. Analyze the limitations of current AI technology in financial forecasting and its potential impact on the future financial industry landscape, including changes in the job market, the emergence of new financial institutions, and user interface innovations. Emphasizing the importance of increasing investor understanding and awareness of AI and looking ahead to future trends in AI tools for user experience to drive wider adoption of AI in financial decision making. The huge potential, challenges, and future directions of AI in the financial sector highlight the critical role of AI technology in driving transformation and innovation in the financial sector ...

Assessing the Potential of AI for Spatially Sensitive Nature-Related Financial Risks ArXiv ID: 2404.17369 “View on arXiv” Authors: Unknown Abstract There is growing recognition among financial institutions, financial regulators and policy makers of the importance of addressing nature-related risks and opportunities. Evaluating and assessing nature-related risks for financial institutions is challenging due to the large volume of heterogeneous data available on nature and the complexity of investment value chains and the various components’ relationship to nature. The dual problem of scaling data analytics and analysing complex systems can be addressed using Artificial Intelligence (AI). We address issues such as plugging existing data gaps with discovered data, data estimation under uncertainty, time series analysis and (near) real-time updates. This report presents potential AI solutions for models of two distinct use cases, the Brazil Beef Supply Use Case and the Water Utility Use Case. Our two use cases cover a broad perspective within sustainable finance. The Brazilian cattle farming use case is an example of greening finance - integrating nature-related considerations into mainstream financial decision-making to transition investments away from sectors with poor historical track records and unsustainable operations. The deployment of nature-based solutions in the UK water utility use case is an example of financing green - driving investment to nature-positive outcomes. The two use cases also cover different sectors, geographies, financial assets and AI modelling techniques, providing an overview on how AI could be applied to different challenges relating to nature’s integration into finance. This report is primarily aimed at financial institutions but is also of interest to ESG data providers, TNFD, systems modellers, and, of course, AI practitioners. ...

AI in ESG for Financial Institutions: An Industrial Survey ArXiv ID: 2403.05541 “View on arXiv” Authors: Unknown 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. ...

Banking 4.0: ‘The Influence of Artificial Intelligence on the Banking Industry & How AI Is Changing the Face of Modern Day Banks’ ArXiv ID: ssrn-3661469 “View on arXiv” Authors: Unknown Abstract Artificial intelligence (AI), from time to time called machine intelligence is simulation of human intelligence in machines. It is the intellect exhibited by ma Keywords: Artificial Intelligence (AI), Neural Networks, Natural Language Processing (NLP), Deep Learning, Equities Complexity vs Empirical Score Math Complexity: 1.0/10 Empirical Rigor: 2.0/10 Quadrant: Philosophers Why: The paper is a conceptual literature review discussing AI applications in banking with no mathematical formulas or statistical models, and its empirical backing is limited to citing other studies without original data analysis or backtesting. flowchart TD A["Research Question:<br>How is AI changing modern banks?"] --> B["Methodology:<br>Review of Neural Networks, NLP, Deep Learning"] B --> C["Inputs:<br>Banking data & AI Equities"] C --> D["Computational Process:<br>AI Simulation of Human Intelligence"] D --> E["Key Findings:<br>Banking 4.0 Transformation"]

AI inFinance: A Review ArXiv ID: ssrn-3647625 “View on arXiv” Authors: Unknown Abstract The recent booming of AI in FinTech evidences the significant developments and potential of AI for making smart FinTech, economy, finance and society. AI-empowe Keywords: Artificial Intelligence (AI), FinTech, Machine Learning in Finance, Smart Economy, Multi-Asset / Technology Complexity vs Empirical Score Math Complexity: 1.5/10 Empirical Rigor: 1.0/10 Quadrant: Philosophers Why: The excerpt is a literature review summarizing broad trends in AI and finance using high-level concepts and Google search data, with no advanced mathematical formulas or empirical backtesting details presented. flowchart TD A["Research Goal: Review AI in FinTech developments and potential"] --> B["Methodology: Systematic literature review"] B --> C["Data: Academic papers, industry reports, 2010-2024"] C --> D["Computational Process: Taxonomy analysis & synthesis"] D --> E{"Findings"} E --> F["AI for Smart Finance"] E --> G["Multi-Asset / Technology Integration"] E --> H["Machine Learning Applications"]