Studies in Economics and Business Relations

Artificial Intelligence-driven corporate finance: enhancing efficiency and decision-making through machine learning, natural language processing, and robotic process automation in corporate governance and sustainability

2024-05-05T00:23:43+00:00

This research paper delves into the transformative possibilities of Artificial Intelligence (AI) within corporate finance, specifically focusing on its role in improving efficiency and decision-making processes. Through the utilization of machine learning, natural language processing (NLP), and robotic process automation (RPA), AI introduces innovative methods for enhancing corporate governance and sustainability practices. In the contemporary business landscape, corporations encounter mounting pressure to streamline operations while simultaneously addressing concerns regarding environmental, social, and governance (ESG) issues. Conventional finance methodologies often struggle to efficiently handle large volumes of data and extract actionable insights promptly. However, AI presents a shift in paradigm by enabling automated data analysis, recognizing patterns, and conducting predictive modeling, thus enabling finance professionals to make data-informed decisions swiftly and accurately. Machine learning algorithms play a pivotal role in detecting patterns and correlations within financial data, facilitating proactive risk management and strategic planning. Additionally, NLP technologies facilitate the extraction of valuable insights from unstructured data sources like regulatory filings, news articles, and social media, thereby enabling informed decision-making in corporate governance and sustainability endeavors. Moreover, RPA simplifies repetitive tasks and workflows, thereby reducing operational expenses and freeing up human resources for more strategic pursuits. Through the automation of routine processes such as data entry, reconciliation, and reporting, RPA enhances operational efficiency and ensures adherence to regulatory standards. Through the adoption of AI technologies, corporations can unlock novel avenues for innovation, optimize resource allocation, and promote sustainable growth within today's dynamic business milieu.

Acceptance of artificial intelligence technologies in business management, finance, and e-commerce: factors, challenges, and strategies

2024-09-04T19:37:23+00:00

This research investigates the comprehensive acceptance of artificial intelligence (AI) in business management, finance, and e-commerce, focusing on the factors driving its adoption, the obstacles encountered, and strategies for enhancing integration. AI technologies have transformed these sectors, delivering exceptional efficiencies, predictive analytics, and personalized customer experiences. However, their acceptance is influenced by various factors, including technological readiness, organizational culture, and perceived benefits. In business management, AI improves decision-making processes, optimizes operations, and fosters innovation. Financial institutions utilize AI for risk management, fraud detection, and personalized banking services, while the e-commerce sector gains from AI through enhanced customer service, dynamic pricing, and inventory management. Despite these benefits, challenges such as data privacy concerns, high implementation costs, and resistance to change impede widespread adoption. Additionally, ethical considerations and the need for regulatory compliance add layers of complexity. This paper identifies key strategies to address these challenges, such as promoting a culture of innovation, investing in AI education and training, and developing robust data governance frameworks. Strategic partnerships and collaborations with AI experts and tech firms are also essential for navigating the AI landscape. By comprehensively addressing these factors and challenges, businesses can unlock AI's full potential, driving sustainable growth and competitive advantage. This study contributes to understanding AI acceptance in critical sectors, providing a roadmap for successful AI implementation and emphasizing the importance of strategic planning and stakeholder engagement.

A Review Study of Commodity Derivative Market with reference to Macroeconomic Determinants and Weather Conditions

2024-12-19T19:07:23+00:00

The present study aims to conduct a bibliometric analysis of the commodity derivative market from journals in the Scopus database from 2007 to 2024. The study uses bibliometrics, performance analysis, science mapping, content analysis, and text mining of 754 articles extracted from the Scopus database to discover the research gap and models for future research. The R-studio, VOS-viewer, and SDG mapper tools have been used to analyze the articles of the Scopus database. The result of the study states that the journal “Energy Economics” has the highest no. of Citation per publication (CPP) ratio, the Lippe D. has the prominent authors with the highest no. of documents in the commodity derivative market, and the United Nations has the highest no. of publications in the field of commodity derivative market. Based on TE plot and text mining techniques, the result also states Sustainable Risk Management Integration through the commodity derivative market that classifies the 17 SDGs into three categories: Fundamental Priorities (SDG 7, 13, and 2), Supportive Pillars (SDG 8, 12, 9, 15, 17, 4, 3) and Niche Concerns (SDG 6, 16, 11, 10, 1, 5, 14).