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In today's advanced economy, there is a broader presence of information revolution, such as artificial intelligence (AI). AI primarily drives modern banking, leading to innovative banking channels, services and solutions disruptions. Thus, this chapter intends to determine AI's place in contemporary banking and stock market trading.

Stock market forecasting is hampered by the inherently noisy environments and significant volatility surrounding market trends. There needs to be more research on the mantle of AI in revolutionising banking and stock market trading. Attempting to bridge this gap, the present research study looks at the function of AI in banking and stock market trading.

The researchers have synthesised the literature pool. They undertook a systematic review and meta-synthesis method by identifying the major themes and a systematic literature review aided in the critical analysis, synthesis and mapping of the body of existing material.

The study's conclusions demonstrated the efficacy of AI, which has played a robust role in banking and finance by reducing risk and operational costs, enabling better customer experience, improving regulatory complaints and fraud detection and improving credit and loan decisions. AI has revolutionised stock market trading by forecasting future prices or trends in financial assets, optimising financial portfolios and analysing news or social media comments on the assets or firms.

AI's debut in banking and finance has brought sea changes in banking and stock market trading. AI in the banking industry and capital market can provide timely and apt information to its customers and customise the products as per their requirements.

This chapter highlights the key areas for the leadership and innovation research agenda in 2035. This agenda will direct researchers’ focus to the core transversal skills that individuals must have amidst the shift toward a greener and digitalized economy. Such skills include leadership, management, creativity, communication, and adaptability. The role of macro governmental policies and micro organizational policies is of great significance to ensure the implementation (if any) of these changes and of core to the research agenda. This chapter will also guide researchers to the challenges at the higher education level that need to be addressed to ensure the balance between the skills and knowledge acquired by workers through education and the needs of businesses in order to increase productivity and innovation.

In particular, it is worth mentoring new and more efficient solutions that can meet the increasingly specific needs of each company, especially in food management. A business intelligence (BI) solution can help your food company better understand and manage business processes more effectively. Management information is essential for all levels of an organisation to make quick and correct decisions. However, what exactly is BI, and what can it mean for a food company?

The PRISMA stands for (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) and content analysis strategy used the SLR (systematic literature review) methodology to examine 151 papers published in peer-reviewed academic journals and industry reports between 2016 and 2023.

The findings show that artificial intelligence and digitalisation are linked to the UN 2030 Agenda. BI management ranks first (66%), followed by crop and land mapping systems (40%), agricultural machinery monitoring tools (39%) and decision support systems (31%). The road to digital transformation remains extended, with the main impediments being more compatibility between enterprise systems and a shortage of expertise.

The section relating to methodological perspective adopts the PRISMA methodology for systematic review. Interoperability is easily managed by assigning qualified teams to projects. The added value of a consulting firm with extensive project management experience in the food industry is closely related to the results achieved.

BI: What exactly is it, and why a data-driven culture is essential in the food and beverage industry?

The purpose of this research is to examine the connections between liquidity risk, credit risk, and bank profitability in India.

In order to examine the interlinkage between liquidity risk, credit risk, and profitability of banks in India, the researcher has gathered data from all commercial banks in India from 2004–2005 to 2020–2021. The data sources included in this study encompass the International Country Risk Guide, World Development Indicators and Reserve Bank of India (RBI). Seemingly Unrelated Regression (SUR) has been utilised for the study.

Findings of this research identified that liquidity risk is inversely proportional to credit risk. Return on assets (ROA) and return on equity (ROE) are both impacted negatively by liquidity risk. ROA is impacted positively by credit risk, while ROE is impacted negatively by it. The profitability of banks is harmed by the interaction between liquidity risk and credit risk. It also shows that law and order, are beneficial to bank earnings and risk management. The capital risk-adjusted ratio has a negative relationship with bank profitability, indicating the need for better capital allocation.

The originality of this work lies in its unique contributions, It emphasises explicitly the Indian context, thereby providing insights tailored to this particular setting. It employs the SUR methodology, a statistical approach allowing for a more comprehensive data analysis. Additionally, it identifies and explores interaction effects, which can shed light on the complex relationships between variables.

In writing this article,¹ I have been guided by the questions of by what means the Xambioá deal with commerce and how it makes sense, and what part it plays in their attribution of meaning to the world. I attempt to demonstrate that the use of money, and internal commerce, among the Xambioá are not historical accidents. Money and merchandise are the objects of tireless experiences by the Xambioá. They appropriate meaningfully these allogenic elements and make them circulate in their own way. I suggest that the appropriation of a signifying element like money occurred not only because of its utility but because it is a highly meaningful element. People and things are introduced and are signified according to native logic.