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This study addresses the ever-increasing cyber risks confronting the global banking sector, particularly in India, amid rapid technological advancements. The purpose of this study is to de velop an innovative cyber fraud (CF) response system that effectively controls cyber threats, prioritizes fraud, detects early warning signs (EWS) and suggests mitigation measures.

The methodology involves a detailed literature review on fraud identification, assessment methods, prevention techniques and a theoretical model for fraud prevention. Machine learning-based data analysis, using self-organizing maps, is used to assess the severity of CF dynamically and in real-time.

Findings reveal the multifaceted nature of CF, emphasizing the need for tailored control measures and a shift from reactive to proactive mitigation. The study introduces a paradigm shift by viewing each CF as a unique “fraud event,” incorporating EWS as a proactive intervention. This innovative approach distinguishes the study, allowing for the efficient prioritization of CFs.

The practical implications of such a study lie in its potential to enhance the banking sector’s resilience to cyber threats, safeguarding stability, reputation and overall risk management.

The originality stems from proposing a comprehensive framework that combines machine learning, EWS and a proactive mitigation model, addressing critical gaps in existing cyber security systems.

The global financial crisis (GFC) of 2007–2008 had far-reaching consequences for the global economy, triggering widespread economic turmoil. We use the event-study method to investigate whether investors priced the effect of significant events before the Lehman Brothers' bankruptcy in European and Asia-Pacific banks. Abnormal returns on the event days range from −4.32% to 5.03% in Europe and −5.13% to 6.57% in Asia-Pacific countries. When Lehman Brothers went bankrupt on September 15, 2008, abnormal returns averaged the lowest at −4.32% in Europe and −5.13% in Asia-Pacific countries. The significant abnormal returns show that Lehman Brothers' collapse was a turning point, and investors paid attention to the precrisis events as warning signs of the oncoming crisis.

Due to its high leverage nature, a bank suffers vitally from the credit risk it inherently bears. As a result, managing credit is the ultimate responsibility of a bank. In this chapter, we examine how efficiently banks manage their credit risk via a powerful tool used widely in the decision/management science area called data envelopment analysis (DEA). Among various existing versions, our DEA is a two-stage, dynamic model that captures how each bank performs relative to its peer banks in terms of value creation and credit risk control. Using data from the largest 22 banks in the United States over the period of 1996 till 2013, we have identified leading banks such as First Bank systems and Bank of New York Mellon before and after mergers and acquisitions, respectively. With the goal of preventing financial crises such as the one that occurred in 2008, a conceptual model of credit risk reduction and management (CRR&M) is proposed in the final section of this study. Discussions on strategy formulations at both the individual bank level and the national level are provided. With the help of our two-stage DEA-based decision support systems and CRR&M-driven strategies, policy/decision-makers in a banking sector can identify improvement opportunities regarding value creation and risk mitigation. The effective tool and procedures presented in this work will help banks worldwide manage the unknown and become more resilient to potential credit crises in the 21st century.

This study aims to investigate the dynamics between exogenous shocks, financial stress and economic performance in the USA from January 1995 to August 2023.

Granger-causality tests and impulse response analyses are used to examine causal relationships and dynamic responses among crude oil prices, real M2 money supply, financial stress and key economic indicators.

This study reveals a significant correlation between elevated financial stress and reduced real output, along with disruptions in the labor market, potentially leading to economic recessionary trends. Failure to address these challenges could perpetuate labor market difficulties, weaken capital accumulation within the loanable funds market and ultimately hinder long-term economic growth prospects in the USA.

This study offers insights for policymakers to mitigate financial stress. Recommendations include enhancing financial surveillance, strengthening regulatory frameworks, promoting economic diversification and implementing countercyclical policies to stabilize the economy and support labor markets. In addition, proactive monitoring of financial stress indicators can serve as early warning signals, aiding in timely interventions and effective risk management strategies.

This research provides a comprehensive analysis of how the financial stress index (FSI) mediates the effects of external shocks on the US economy, addressing a gap in existing literature. The integration of the FSI into the analysis enhances the understanding of the transmission channels through which external shocks influence the economy.

This paper analyses J1 League and J2 League clubs during the period 2011–2020 to anticipate financial distress.

Data were collected for 29 professional football clubs competing in the J1 and J2 League for the financial years ending 2011–2020. Analysis was conducted using Altman’s Z-score methodology and additional statistical tests were conducted to measure differences between groups.

The results show significant cases of financial distress amongst clubs in both divisions and that clubs that have played predominantly in the J1 League are in significantly poorer financial health than clubs that have played predominantly in the J2 League. Overall, the financial situation in Japanese professional football needs to be monitored, a position that could be exacerbated by the economic crisis, caused by the coronavirus disease 2019 (COVID-19).

While the financial situation for a majority of the clubs in the J-League presents an austere picture, comparison with clubs in other leagues across Asia and Europe and understanding the different policies set by these leagues would enable us to understand whether the phenomenon of financial distress is common to other clubs and leagues across different countries and continents.

The paper recommends that J-League visit the existing club licensing criteria and implement equitable cost-control measures, such as implementing a cap on acceptable losses over a specified period or restricting overall expenditures as a percentage of the club’s revenue.

The paper extends the evidence base of measuring financial distress in professional team sports and is also the first paper of its kind to examine this in relation to Asian professional football.

Travel decreased dramatically during the initial wave of the COVID-19 pandemic, only to return rapidly to prepandemic levels once the degree of fear toward the virus began to diminish among potential travelers. This USA-based 16-month repeated-measure cross-sectional survey study aims to explore the degree to which fear of COVID affected people’s decisions to stay home rather than to travel during the pandemic.

The research used survey data. An extensive data set, composed of over 9,500 respondents, collected through Mechanical Turk over a 16-month time period, was used to compare respondent fear of the pandemic both with their attitudes toward future travel and with Smith Travel Research data reflecting actual pandemic travel patterns.

The results demonstrate how fear of COVID was closely and negatively linked to both travel intentions and travel behavior.

Data were collected from US respondents only.

The findings significantly extend earlier studies and provide guidance for those studying travel consumer behavior regarding trends that should be monitored in the case of a future pandemic or other fear-inducing crisis. For hospitality and tourism managers and marketers, understanding fear as a leading indicator of future travel behavior can result in more timely promotional efforts and staffing and training decisions.

Measuring and understanding consumer fear levels as this relates to travel decisions can help in the future to adjust the message that is sent to the public, perhaps reducing the amount of travel taken during periods when this is unwise and or unsafe.

This paper extends previous work that had been based upon cross-sectional reviews, providing a broader and more valuable study of an important and timely consumer behavior travel topic.

This chapter focuses on how alternative data can change the nature of financial forecasting through improved short-term forecasting techniques and decreased informativeness from longer term sources. Increased use of social media data leads the charge in transforming this transition. Alternative data are data not from standard financial statements or formal reports. This chapter looks at alternative data from new sources (e.g., social media, Internet of Things [IoT], and digital footprints) and alternative data from new collection methods like web scraping for textual analysis, image analysis, and vocal analysis). It first discusses standard data in financial forecasting. Next, this chapter examines alternative data in financial forecasting. Finally, it discusses alternative data used in studying finance more broadly.

This study aimed to evaluate the potential of artificial intelligence (AI) as a tool for knowledge synthesis, the production of written content and the delivery of coaching conversations.

The research employed the use of experts to evaluate the outputs from ChatGPT's AI tool in blind tests to review the accuracy and value of outcomes for written content and for coaching conversations.

The results from these tasks indicate that there is a significant gap between comparative search tools such as Google Scholar, specialist online discovery tools (EBSCO and PsycNet) and GPT-4's performance. GPT-4 lacks the accuracy and detail which can be found through other tools, although the material produced has strong face validity. It argues organisations, academic institutions and training providers should put in place policies regarding the use of such tools, and professional bodies should amend ethical codes of practice to reduce the risks of false claims being used in published work.

This is the first research paper to evaluate the current potential of generative AI tools for research, knowledge curation and coaching conversations.

This study seeks to examine the relationship between macroeconomic shock indicators, namely geopolitical risk (GPR), global economic policy uncertainty (GEPU) and financial stress (FS), and returns as well as volatilities on seven carefully selected stock markets in Africa. Specifically, the study intends to unravel the co-movement and interdependence between the respective macroeconomic shock indicators and each of the stock markets under consideration across time and frequency.

This study employed wavelet coherence approach to examine the strength and stability of the relationships across different time scales and frequency components, thereby providing valuable insights into specific periods and frequency ranges where the relationships are particularly pronounced.

The study found that GEPU, Financial Stress (FS) and GPR failed to induce significant influence on African stock market returns in the short term (0–4 months band), but tend to intensify in the long-term band (after 6th month). On the contrary, stock market volatilities exhibited strong coherence and interdependence with GEPU, FSI and GPR in the short-term band.

This study happens to be the first of its kind to comprehensively consider how the aforementioned macro-economic shock indicators impact stock markets returns and volatilities over time and frequency. Further, none of the earlier studies has attempted to examine the relationship between macro-economic shocks, stock returns and volatilities in different crisis periods. This study is the first of its kind in to employ data spanning from May 2007 to April 2023, thereby covering notable crisis periods such as global financial crisis (GFC) and the COVID-19 pandemic episodes.

The author, who is Chair of Norfolk’s Safeguarding Adults’ Board (SAB) reflects on the impact of a Safeguarding Adults Review (SAR) and the actions that resulted. The purpose of the paper is to provide an insight into a significant SAR and the resulting actions desinged to change practice.

The actions illuminate the power of hospitals and politicians over people’s lives. It took the deaths of three young adults with learning disabilities, in a hospital, to move beyond the status quo and organise some very different service responses.

“Progress summits” have considered local and national actions. There have been important gains and setbacks, including the delayed reform of the Mental Health Act. Despite uncertainties, SAB can be detonators to listening and taking action.

This is a unique insight into the impact of a Safeguarding Adults Review and the actions that resulted from this.