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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.

The objective of this chapter is to analyse the performance of the UiPath (PATH) company on the New York Stock Exchange, in the context of the war between Russia and Ukraine, and to predict the closing price of the PATH stock using autoregressive integrated moving average with (ARIMAX) and without (ARIMA) exogenous variable methods and autoregressive neural networks (NNAR, NNARX).

UiPath has gained a significant reputation in the IT market and has become a point of interest in recent years. However, the current context is marked by an event of international impact, the war between Russia and Ukraine. In this context, this analysis will consider performance from two perspectives: forecasts of the closing price and forecasts of the closing price with an exogenous variable, namely the war between Russia and Ukraine.

In the analysis that follows, we will address a forecast of the stock closing price using ARIMA, ARIMAX, NNAR and NNARX, as well as analysis of changing points and structural breaks of the series.

The changing points in the mean and variance but also the breaks in the structure justify the course of the closing price. From the information extracted in the analysis, it can be concluded that market sentiment is currently pessimistic due to the downward trend in the price. Both the public and the shareholders are disappointed with the performance of PATH stock and are waiting for the next change point that will change the trend of the series.

Today's marketing is dominated by decision-making based on artificial intelligence and machine learning. This study focuses on one semi- and unsupervised machine learning technique, generative adversarial networks (GANs). GANs are a type of deep learning architecture capable of generating new data similar to the training data that were used to train it, and thus, it is designed to learn a generative model that can produce new samples. GANs have been used in multiple marketing areas, especially in creating images and video and providing customized consumer contents. Through providing a holistic picture of GANs, including its advantage, disadvantage, ethical considerations, and its current application, the study attempts to provide business some strategical orientations, including formulating strong marketing positioning, creating consumer lifetime values, and delivering desired marketing tactics in product, promotion, pricing, and distribution channel. Through using GANs, marketers will create unique experiences for consumers, build strategic focus, and gain competitive advantages. This study is an original endeavor in discussing GANs in marketing, offering fresh insights in this research topic.

Knowledge management (KM) is an important topic in the age of big data, and this study adds to the existing body of literature by providing a novel KM perspective on the technological phenomenon of artificial intelligence (AI). This study aims to discover how AI might facilitate knowledge-based business-to-business (B2B) marketing. In this chapter, the authors take a close look at the building blocks of AI and the relationships between them. Future research directions and also the effects of the various market information building components on B2B marketing are discussed. The study’s approach is theoretical; it tries to provide a framework for characterising the phenomenon of AI and its constituent parts. Additionally, this chapter provides a methodical analysis of the three categories of market information crucial to B2B marketing: knowledge of customers, knowledge of users, and knowledge of external markets. This research looks at AI through the lens of the conventional data processing framework, analysing the six pillars upon which AI systems are founded. It also explained how the framework’s components work together to transform data into actionable information. In this chapter, the authors will look at how AI works and how it can benefit B2B knowledge-based marketing. It’s not aimed at AI experts but rather at general marketing managers. In this chapter, the possible effects of AI on B2B marketing are discussed using examples from the real world.

The development of information technologies has led to a considerable transformation in human resource management from conventional or commonly known as personnel management to modern one. Data mining technology, which has been widely used in several applications, including those that function on the web, includes clustering algorithms as a key component. Web intelligence is a recent academic field that calls for sophisticated analytics and machine learning techniques to facilitate information discovery, particularly on the web. Human resource data gathered from the web are typically enormous, highly complex, dynamic, and unstructured. Traditional clustering methods need to be upgraded because they are ineffective. Standard clustering algorithms are enhanced and expanded with optimization capabilities to address this difficulty by swarm intelligence, a subset of nature-inspired computing. We collect the initial raw human resource data and preprocess the data wherein data cleaning, data normalization, and data integration takes place. The proposed K-C-means-data driven cuckoo bat optimization algorithm (KCM-DCBOA) is used for clustering of the human resource data. The feature extraction is done using principal component analysis (PCA) and the classification of human resource data is done using support vector machine (SVM). Other approaches from the literature were contrasted with the suggested approach. According to the experimental findings, the suggested technique has extremely promising features in terms of the quality of clustering and execution time.

The popularity of cryptocurrency and blockchain technology has been increasing in recent years. Thus, the study uses bibliometric analysis to examine the development of research on cryptocurrency and blockchain trends.

The very few researchers analyse the bibliometric trends in blockchain and cryptocurrency research to classify the articles according to research methodology and journal quality. Further, a complete study of citations or co-citations based on co-occurrence analysis needs to be added to the bibliometric research. Therefore, it is required to study this topic in detail.

The VOSviewer software and Scopus analysis are used to conduct a bibliometric study on the biographies of articles published on cryptocurrency and blockchain trends. A total of 1,186 papers from the Scopus database are retrieved to analyse the trends in this field of research.

The study examines the total citations, papers with the most citations, authors and journals, prominent institutions and country contributions. In addition to listing the top 10 most significant articles with their years of publication and total citations, this study provides insight into the top 10 prominent journals of cryptocurrency and blockchain trends. Additionally, during the past 15 years, the United States and the United Kingdom have received the most citations and publications on cryptocurrencies and blockchain trends. This study also identifies and critically investigates the top 10 journals in the specialised field with the highest Source Normalized Impact per Paper (SNIP), SCImago Journal Rank (SJR) and citation scores.

Estimation of (in)efficiency became a popular practice that witnessed applications in virtually any sector of the economy over the last few decades. Many different models were deployed for such endeavors, with Stochastic Frontier Analysis (SFA) models dominating the econometric literature. Among the most popular variants of SFA are Aigner, Lovell, and Schmidt (1977), which launched the literature, and Kumbhakar, Ghosh, and McGuckin (1991), which pioneered the branch taking account of the (in)efficiency term via the so-called environmental variables or determinants of inefficiency. Focusing on these two prominent approaches in SFA, the goal of this chapter is to try to understand the production inefficiency of public hospitals in Queensland. While doing so, a recognized yet often overlooked phenomenon emerges where possible dramatic differences (and consequently very different policy implications) can be derived from different models, even within one paradigm of SFA models. This emphasizes the importance of exploring many alternative models, and scrutinizing their assumptions, before drawing policy implications, especially when such implications may substantially affect people’s lives, as is the case in the hospital sector.