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The purpose of this paper is to investigate and analyze the adoption of digital technologies in the banking industry and its impact on the rise of digital fraudulent activities, specifically focusing on online banking frauds. This paper aims to provide insights into the current technologies implemented by banks to secure their online banking systems and explores the methods used by cybercriminals to exploit security vulnerabilities in these systems.

In order to understand how digital technologies in banking can be secured against online fraud, this research conducted a systematic literature review (SLR) on digital banking, online banking fraud, and security measurements. The review encompasses a variety of sources from online databases such as Emerald Insight, Google Scholar, IEEE, JSTOR, Springer and Science Direct.

The key finding of the paper is that the adoption of digital technologies in the banking industry has led to a significant increase in digital fraudulent activities, particularly in the form of online banking frauds. This paper emphasizes that these frauds have become a global concern and have evolved into an industry where cybercriminals use sophisticated tools such as phishing attacks, denial-of-service attacks, Trojan horses, malware infections, identity theft and computer viruses.

This study relies solely on a literature review without incorporating primary data or case studies; therefore, it might miss out on the firsthand experiences and perspectives of banks and cybersecurity professionals.

This study emphasizes the need for banks to adopt advanced security measures to safeguard their online banking systems.

This study underscores the importance of ongoing training and awareness programs for both bank employees and customers.

This study specifically addresses the adoption of digital technologies in the banking industry and its correlation with the increase in digital fraudulent activities. This focus on the intersection of technology and fraud in the banking sector is a distinctive aspect. This study conducts a SLR to examine the current technologies implemented by banks to safeguard their online banking systems. This comprehensive approach provides insights into the diverse security measures used by banks to protect against various types of cyber threats.

The purpose of this study is to analyse cyberfraud in the South African banking industry using a multiple regression approach and develop a predictive model for the estimation and prediction of financial losses due to cyberfraud.

To mitigate the occurrence of cyberfraud, this study uses the multiple regression approach to correlate the relationship between financial loss and cyberfraud activities. The cyberfraud activities in South Africa are classified into three, namely, digital banking application, online and mobile banking fraud. Secondary data that captures the rate of cyberfraud occurrences within these three major categories with their resulting financial losses were used for the multiple regression analysis that was carried out in the Statistical Package for Social Science (SPSS, 2022 environment).

The results obtained indicate that the South African financial institutions still incur significant financial losses due to cyberfraud perpetration. The two main independent variables used to estimate the magnitude of financial loss in the South Africa’s banking industry are online (internet) banking fraud (X2) and mobile banking fraud (X3). Furthermore, a multiple regression model equation was developed for the prediction of financial loss as a function of the two independent variables (X2 and X3).

This study adds to the literature on cyberfraud mitigation. The findings may promote the combat against cyberfraud in the South Africa’s financial institutions. It may also assist South Africa’s financial institutions to predict the financial loss that financial institutions can incur over time. It is recommended that South Africa’s financial institutions pay attention to these two key variables and mitigate any associated risks as they are crucial in determining their profitability.

Existing literature indicated significant financial losses to cyberfraud perpetration without establishing any relationship between the magnitude of losses incurred and the prevalent forms of cyberfraud. Thus, the novelty of this study lies in the analysis of cyberfraud in the South African banking industry using a multiple regression approach to link financial losses to the perpetration of the prevalent forms of cyberfraud. It also develops a predictive model for the estimation and projection of financial losses.

The purpose of this study is to examine the level of effectiveness of the anti-fraud technologies employed by the South African banking industry for cyberfraud mitigation.

This research employed a qualitative research design involving a purposive sampling method. Primary data was collected from the key organisational staff across the 17 licensed commercial banks in South Africa via the use of structured questionnaires. In particular, these were experts involved in combating fraud and taking managerial decisions regarding the use of anti-fraud technologies for cyberfraud mitigation. Non-parametric statistical analyses were carried out from the responses obtained.

The results obtained indicated that the combination of internal and external anti-fraud technologies such as filtering software, firewalls, encryption, continuous auditing, discovery sampling, virus protection, financial ratios, digital analysis and data mining may have a positive effect on cyberfraud mitigation. These technologies are employed mostly to ensure effective internal control systems capable of minimising cyberfraud. In addition, the anti-fraud technologies employed in the South African banking industry may also be effective in the mitigation of cyberfraud, although significant cases of cyberattacks were reported by the respondents.

The study recommends investment in more digital and emerging technologies and the development of human capacities to effectively deploy them in the combat against cybercrime.

The novelty of this study lies in the identification of the type of anti-fraud technologies/software employed by the South African banking industry and their level of effectiveness or success rate.

The purpose of this paper is to focus on managing the risk of fraud in commercial letters of credit (LC) in Bangladesh involving three parties: the seller, the buyer and the bank. It addresses the severity of LC fraud, the banks’ actions when detected and the preventive measures the relevant parties can adopt.

This research uses doctrinal and qualitative methods to propose strategic actions that benefit buyers, sellers, banks, legal professionals and judges. The study aims to explore the modus operandi used by fraudsters through thematic analysis.

The study’s findings reveal that LC fraud has escalated to a concerning level, posing a significant threat to the economic stability of Bangladesh. Measures must be taken to mitigate this risk and safeguard the country’s financial integrity. To effectively combat the risk of LC fraud, the updated version of UCP must include specific and detailed guidelines on LC fraud. This study recommends preventative measures that all parties involved must take to reduce the likelihood of fraud significantly.

Due to a lack of LC experts, the participant sample for the study in Bangladesh was limited. Nevertheless, most banking participants were highly distinguished and held the Head of Trade Finance Department position in commercial banks. A few academics and legal practitioners with LC expertise also participated in the study.

It provides cutting-edge solutions to effectively handle LC fraud risk and provides proactive measures to prevent it.

The purpose of this study is to assess the impact of cyberfraud in the South African banks with the aim to provide recommendations to effectively mitigate it.

The study uses a qualitative approach involving the use of structured questionnaires. The questionnaires were made available to the staff of 17 licensed banks in South Africa who deal with management, operation, administration and banking services. Two hypotheses were formulated and non-parametric statistical analyses involving the use of Chi-square test, Fischer’s Exact test and Spearman’s correlation were carried out. The two hypotheses formulated were tested to draw a conclusion.

The results obtained indicate that the impact of cyberfraud in the South African banking industry is highly significant and has affected the reputation of some of the banks. This calls for the need to review the diverse ways of curbing cyberfraud to lessen their impact and that of associated fraud risks on the banking operation.

This study provides an analysis on the relationship cyberfraud occurrences and the reputation of South African banks. The implementation of the recommendations may reinforce the existing security measures in the fight against cyberfraud.

The novelty of this study lies in the fact that the assessment of the impact of cyberfraud on the banking industry in South Africa has not been sufficiently highlighted by the existing literature.

The purpose of the study is to identify cybersecurity threats that hinder the adoption of digital banking and provide sustainable strategies to combat cybersecurity risks in the banking industry.

Systematic literature review guidelines were used to conduct a quantitative synthesis of empirical evidence regarding the impact of cybersecurity threats and risks on the adoption of digital banking.

A total of 84 studies were initially examined, and after applying the selection and eligibility criteria for this systematic review, 58 studies were included. These selected articles consistently identified identity theft, malware attacks, phishing and vishing as significant cybersecurity threats that hinder the adoption of digital banking.

With the country’s banking sector being new in this area, this study contributes to the scant literature on cyber security, which is mostly in need due to the myriad breaches that the industry has already suffered thus far.

The present work has proposed and employed an innovative hybrid method based on the combination of factor analysis and an artificial neural network (ANN) model to forecast customer satisfaction from the identified dimensions of service quality in India, a developing country.

The qualitative study is conducted with Internet banking users to understand e-banking clients' perceptions. The data is collected with the help of a questionnaire from randomly selected 208 customers in India. Firstly, factor analysis was performed to determine the influential factors of customer satisfaction, and four factors i.e. efficiency, reliability, security and privacy, and issue and problem handling were extracted accordingly. The neural network model is then applied to the factor scores to validate the key elements. Lastly, the comparative analysis of the actual ANN and the regression predicted result is done.

The success ability of the linear regression model is challenged when approximated to nonlinear problems such as customer satisfaction. It is concluded that the ANN model is a better fit than the linear regression model, and it can recognise the complex connections between the exogenous and endogenous variables. The results also show that reliability, security and privacy are the most influencing factors; however, problem handling and efficiency have the slightest effect on bank client satisfaction.

This research is conducted in India, and the sample is chosen from the urban area. The limitation of the purposeful sampling technique and the cross-sectional nature of the data may hamper the generalisation of the results.

The conclusions from the study will be helpful for policymakers, bankers and academicians. To our knowledge, few studies used ANN modelling to predict customer satisfaction in the service sector

This paper aims to investigate the impact of political stability on corruption by drawing upon a sample encompassing both developed and developing European and Asian countries.

The dataset, sourced from the Refinitiv database, spans from July 2014 to May 2022. Panel data techniques, specifically pooled estimation and dynamic panel data [generalized method of moments (GMM)] are employed. The analysis encompasses both fixed and random effects models to capture country-specific cross-sectional effects. To validate our findings, we perform a robustness test by including in the investigation four control variables, namely poverty, type of governance, economic freedom and inflation. To test heterogeneity, the dataset is further divided into two distinct subsamples based on the countries’ locations.

Empirical findings substantiate that political stability (viewed as the risk of government destabilization) has a positive and significant impact on corruption in all analyzed samples of European and Asian countries, though some differences are observed in various subsamples. When we take into account the control variables, these analysis results are robust.

This research provided a panel data analysis with GMM, while other empirical methodologies could also be used, like the difference-in-difference approach. However, our results should be validated by extending the time and the sample to a worldwide sample and using alternative measures of corruption and political stability. Moreover, our focus was on a linear and unidirectional relationship between the considered variables, but it would be interesting to test in our further research a non-linear and bidirectional correlation between them. Furthermore, we have introduced in the robustness test only four economic variables, but to consolidate our findings, we plan to include socioeconomic and demographic variables in future studies.

These outcomes imply that authorities should be aware of the necessity of implementing anti-corruption policies designed to establish effective agencies and enforcement structures for combating systemic corruption, to improve the political environment and the quality of institutions and to apply coherent economic strategies to accelerate economic growth because higher political stability and sustainable development determine a decrease in levels of corruption.

At the microeconomic level, the survival of organizations may be in danger from new types of corruption and money laundering. Therefore, in order to prevent financial harm, the top businesses worldwide should respond to instances of corruption through strengthened supervisory procedures. This calls for the creation of a mechanism inside the code of conduct where correct reporting of suspected situations of corruption would have a prompt procedure to be notified of. To avoid corruption in operational procedures, national plans and policies should be developed by government officials, executives and legislators on a national level, as well as by senior management and the board of directors on an organizational level. This might lower organizations' extra corruption-related expenses, assure economic growth and improve global welfare.

A novel feature of our research resides in its broad examination of a sizable sample of European and Asian countries regarding the nexus between corruption and political stability. The paper also investigates a less explored topic in economic literature, namely the impact of political stability on corruption. Furthermore, the study depicts policy recommendations, outlining effective and reasonable measures aimed at improving the political landscape and combating corruption.

The purpose of this study is to study the impact of relationship marketing orientation (RMO) and relationship quality on customers' commitment and pro-marketer behavior (positive word of mouth and external attribution) after negative brand publicity by using the combined lens of relationship marketing theory and the theory of cognitive dissonance.

A survey was conducted among banking customers in India using an online questionnaire. Data were analyzed using structural equation modeling and the bootstrapping procedure using the SPSS process macro.

Contrary to conventional wisdom, findings of this study suggest that RMO and relationship quality are positively correlated to commitment even after negative publicity. The path between RMO, relationship quality and pro-provider behavior is found to be mediated by commitment. This indirect path is moderated by customers' cognitive dissonance arising out of the negative publicity.

The study establishes the combined roles of RMO and relationship quality in pre-empting the detrimental effects of negative brand publicity. Further, it establishes interactions of cognitive dissonance with these relationship variables, thereby bringing together literature from relationship marketing theory and cognitive dissonance theory.

The current study focuses on many risk categories that have emerged in the digital ecosystem of the financial technology industry, which has dramatically changed traditional financial systems as a result of innovations in financial technology.

The Web of Science Core Collection database was used to find a data set of 719 pertinent papers on the subject encompassing the year 2015–2023. The sample procedure was carried out utilising the PRISMA approach. The keywords were first gathered relating to technological risks in banking sectors and after confirming the keywords, the authors performed the search by the “topic” which covers “title” in the search bar. On February 15, 2023, the Web of Science database was searched using the terms “Cyber security risk OR data theft OR financial crimes OR financial stability risk OR operational risk OR default risk OR money laundering OR financial terrorism AND FinTech AND banking sector”. Two-step approach is applied in this study. First, descriptive analysis is applied using RStudio to highlight prominent authors, countries and affiliations. Furthermore, relationship among authors, countries and keywords is shown by using three fields plot. Second, using VOSviewer, co-occurrence of keyword analysis is used to determine the most influential themes.

The findings show that 2,611 documents have been published from 2016 to 2023. Year 2021 is the most productive year in terms of number of publications. The results also show that WANG XC is tied for the position of most prolific contributing author. In a similar vein, the United States leads the world in publication output. Furthermore, Southwestern University of Finance and Economics in China is leading the list with 15 articles. The results from the co-occurrence of keywords reveal that “default risk”, “operational risk”, “money laundering”, “credit risk”, “corporate governance”, “systematic risk”, “financial stability risk”, “risk management” and “crises” are the frequently keywords.

The results of this study are beneficial to academia and industry in order to advance their current understanding of FinTech and associated concerns. This work expands the understanding of the technology hazards facing the banking industry from a broad perspective.