Search results

This study aims to investigate the extent of Shariah compliance in wakalah sukuk and Shariah non-compliant risk disclosure in the sukuk documents and to analyse the risk management techniques associated with the disclosed risks.

This study uses qualitative document analysis as both data collection and analysis methods. The document analysis acts as a data collection method for 23 wakalah sukuk documents selected from 32 issuances of wakalah sukuk from 2017 to 2021. These sukuk documents were selected based on their availability from relevant websites. Document analysis, both content analysis and thematic analysis, were used to analyse the data. Codes were grounded from that data through keywords search of Shariah noncompliant risk and its risk management. Besides these, interviews were also conducted with four active industry players, i.e. two legal advisors of wakalah sukuk, a wakalah sukuk trustee and a sukuk institutional issuer. These interview data were analysed based on categorical themes, on the aspects of the extent of Shariah compliance in sukuk, and the participant’s views on the risk management techniques associated with the risks or used in the sukuk documents.

Overall, the findings reveal three types of Shariah non-compliant risks disclosed in the sukuk documents and seven risk management techniques associated with them. However, the disclosure and the risk management techniques can be considered minimal in contrast to the extent of Shariah compliance in a sukuk, i.e. Shariah compliance at the pre-issuance stage, ongoing stage and post-issuance stage. On top of these, it was also found from the interviews that not all risk management techniques are workable to manage Shariah non-compliant risk in sukuk. As a result, these findings suggest rigorous reviews of the existing Shariah non-compliance risk (SNCR) disclosures and risk management techniques by the relevant parties.

Sukuk documents used in the study are limited to corporate wakalah sukuk issued in Malaysia. Out of 32 issuances from 2015 to 2021, only 23 documents are available in relevant website. Thus, Shariah non-compliant risk disclosure and its risk management techniques analysed in this study are only limited in those documents.

The findings of this study suggest rigorous reviews on the existing Shariah non-compliance disclosures and risk management techniques. Other than these, future research in relation to uncommon risk management clauses, i.e. assurance, Shariah waiver and transfer of risk, are needed.

The insights presented in the analysis are of importance to sukuk issuers and the sukuk due diligence working group in enhancing the sukuk Shariah compliance and Shariah non-compliant risks disclosure and towards sukuk investors, in capturing and assessing Shariah non-compliant risks in a sukuk and to assist them to make informed investment decisions. More importantly, this study has found few areas of future study in relation to SNCR disclosures and SNCR risk management techniques.

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.

Although risk culture is a key determinant for an effective risk management, identifying the risk culture of a firm can be challenging due to the abstract concept of culture. This paper proposes a novel approach that uses unsupervised machine learning techniques to identify significant features needed to assess and differentiate between different forms of risk culture.

To convert the unstructured text in our sample of banks' 10K reports into structured data, a two-dimensional dictionary for text mining is built to capture risk culture characteristics and the bank's attitude towards the risk culture characteristics. A principal component analysis (PCA) reduction technique is applied to extract the significant features that define risk culture, before using a K-means unsupervised learning to cluster the reports into distinct risk culture groups.

The PCA identifies uncertainty, litigious and constraining sentiments among risk culture features to be significant in defining the risk culture of banks. Cluster analysis on the PCA factors proposes three distinct risk culture clusters: good, fair and poor. Consistent with regulatory expectations, a good or fair risk culture in banks is characterized by high profitability ratios, bank stability, lower default risk and good governance.

The relationship between culture and risk management can be difficult to study given that it is hard to measure culture from traditional data sources that are messy and diverse. This study offers a better understanding of risk culture using an unsupervised machine learning approach.

This study reviews and discusses the accounting literature that analyzes the role of auditors and enforcers in the context of fraud.

This literature review includes both qualitative and quantitative studies, based on the idea that the findings from different research paradigms can shed light on the complex interactions between different financial reporting controls. The authors use a mixed-methods research synthesis and select 64 accounting journal articles to analyze the main proxies for fraud, the stages of the fraud process under investigation and the roles played by auditors and enforcers.

The study highlights heterogeneity with respect to the terms and concepts used to capture the fraud phenomenon, a fragmentation in terms of the measures used in quantitative studies and a low level of detail in the fraud analysis. The review also shows a limited number of case studies and a lack of focus on the interaction and interplay between enforcers and auditors.

This study outlines directions for future accounting research on fraud.

The analysis underscores the need for the academic community, policymakers and practitioners to work together to prevent the destructive economic and social consequences of fraud in an increasingly complex and interconnected environment.

This study differs from previous literature reviews that focus on a single monitoring mechanism or deal with fraud in a broadly manner by discussing how the accounting literature addresses the roles and the complex interplay between enforcers and auditors in the context of accounting fraud.

The board structure (BS) is pivotal in modern corporate governance (CG). This study aims to investigate BS variables (BSIZE, BIND and chief executive officer [CEO] duality) and their correlation with risk-taking behavior indicators, enriching the understanding of how CG shapes financial institutions’ (FIs) decision-making in Pakistan.

By scrutinizing data from 67 financial entities listed on the Stock Exchange of Pakistan spanning from 2011 to 2022 through panel data regression techniques, the research emphasizes that BS holds a substantial influence over the risk tendencies exhibited by these firms.

Key findings suggest that board size has a positive influence, aligned with previous CG research. Smaller boards perform better and avoid excessive risk-taking, contrasting some negative relationship claims. More independent directors are recommended to curtail risk and financial disruption. Holding both CEO and chair roles reduces risk exposure, resonating with reputational and employment risk theory. It is essential to recognize that BS’s impact on risk-taking is nuanced and context-dependent.

Policymakers, scholars, practitioners and investors working in the market for financial companies might greatly benefit from the empirical findings of this study. Imposing mandates on FIs to uphold adequate capital reserves functions as a safeguard against unforeseen losses, thereby diminishing the probability of unwarranted risk-taking.

Prior studies in this domain predominantly focus on nonfinancial sectors. In addition, existing research often explores the relationship between BS and firm risk-taking solely within the banking sector, overlooking other FIs. This study contributes by using a comprehensive data set encompassing all types of FIs, thus extending the existing literature.

This study aims to objectively synthesize the volume of accounting literature on financial statement fraud (FSF) using a systematic literature review research method (SLRRM). This paper analyzes the vast FSF literature based on inclusion and exclusion criteria. These criteria filter articles that are present in the accounting fraud domain and are published in peer-reviewed quality journals based on Australian Business Deans Council (ABDC) journal ranking. Lastly, a reverse search, analyzing the articles' abstracts, further narrows the search to 88 peer-reviewed articles. After examining these 88 articles, the results imply that the current literature is shifting from traditional statistical approaches towards computational methods, specifically machine learning (ML), for predicting and detecting FSF. This evolution of the literature is influenced by the impact of micro and macro variables on FSF and the inadequacy of audit procedures to detect red flags of fraud. The findings also concluded that A* peer-reviewed journals accepted articles that showed a complete picture of performance measures of computational techniques in their results. Therefore, this paper contributes to the literature by providing insights to researchers about why ML articles on fraud do not make it to top accounting journals and which computational techniques are the best algorithms for predicting and detecting FSF.

This paper chronicles the cluster of narratives surrounding the inadequacy of current accounting and auditing practices in preventing and detecting Financial Statement Fraud. The primary objective of this study is to objectively synthesize the volume of accounting literature on financial statement fraud. More specifically, this study will conduct a systematic literature review (SLR) to examine the evolution of financial statement fraud research and the emergence of new computational techniques to detect fraud in the accounting and finance literature.

The storyline of this study illustrates how the literature has evolved from conventional fraud detection mechanisms to computational techniques such as artificial intelligence (AI) and machine learning (ML). The findings also concluded that A* peer-reviewed journals accepted articles that showed a complete picture of performance measures of computational techniques in their results. Therefore, this paper contributes to the literature by providing insights to researchers about why ML articles on fraud do not make it to top accounting journals and which computational techniques are the best algorithms for predicting and detecting FSF.

This paper contributes to the literature by providing insights to researchers about why the evolution of accounting fraud literature from traditional statistical methods to machine learning algorithms in fraud detection and prediction.

This study examines the extent to which regulatory policy uncertainty, macroeconomic risk, banking industry innovations, etc. influence variability in financial sector development among emerging economies in sub-Sahara Africa (SSA).

Data for the empirical inquiry were compiled from a sample of 25 economies from the subregion from 2010 to 2020. Empirical estimates examining the relationships noted above were carried out using the two-step system generalized method of moments estimation technique.

Results the empirical estimates suggest that regulatory policy uncertainty and macroeconomic risk adversely influence or constrain financial sector development among the economies examined in the study. Banking industry innovations on the other hand is found to positively influence the development of the financial sector in these economies. Furthermore, moderating empirical analysis suggests that effective governance positively moderates the relationship between banking industry innovations and financial development among economies in the subregion.

This study’s approach to the mechanics of financial development among economies in SSA is designed to offer different perspectives to those found in the existing literature on financial development in three fundamental ways. First, although the verification of the role of banking industry innovations in financial development may not be new, it is important to point out that the approach used in this study is based on an index for innovations with different constituents or principal components in its construction; making the variable significantly different from what has been examined in the literature. In addition, the review of regulatory policy uncertainty and macroeconomic risk (both variables are multifaceted constructs using the principal component analysis procedure) further brings into this study’s analysis, a different approach to examining conditions influencing variability in financial development among developing economies.

This study sought to examine the interaction between rural bank performance, information and communication technology (ICT) investment, ICT diffusion and financial development.

Data were sourced from the Association of Rural Banks (ARB) Apex and World Development Indicators (WDI) for the period 2014–2020. A total of 122 rural banks were used for this study. The study adopted the two-step system generalized method of moments (SGMM) estimation technique in assessing the interactions among variables.

This study found compelling evidence to support the positive effect of ICT investment on banks’ performance (return on asset and net interest margin). Further, ICT diffusion and financial development positively influence banks’ performance. The results show a positive moderating effect exerted by ICT diffusion and financial development on the impact of bank risk (bank stability) and ICT investment on all three performance measures.

The study focuses on the rural banking sector in the Ghanaian economy, compared to related studies that examine the subject matter for commercial banks. The moderating effects of ICT diffusion and financial development are assessed to guide policy on rural banking development in Ghana.

This study aims to explore the relationship between risk governance characteristics (chief risk officer [CRO], chief financial officer [CFO] and senior directors [SENIOR]) and regulatory adjustments (RAs) in Organization for Economic Cooperation and Development public commercial banks.

Using principal component analysis (PCA) and regression models, the research analyzes a representative data set of these banks.

A significant negative correlation between risk governance characteristics and RAs is found. Sensitivity analysis on the regulatory Tier 1 capital ratio and the total capital ratio indicates mixed outcomes, suggesting a complex relationship that warrants further exploration.

The study’s limited sample size calls for further research to confirm findings and explore risk governance’s impact on banks’ capital structures.

Enhanced risk governance could reduce RAs, influencing banking policy.

The study advocates for improved banking regulatory practices, potentially increasing sector stability and public trust.

This study contributes to understanding risk governance’s role in regulatory compliance, offering insights for policymaking in banking.

This study aims to analyse the systemic risk emitted by all publicly listed commercial banks in a key emerging economy, India.

The study makes use of the Tobias and Brunnermeier (2016) estimator to quantify the systemic risk (ΔCoVaR) that banks contribute to the system. The methodology addresses a classification problem based on the probability that a particular bank will emit high systemic risk or moderate systemic risk. The study applies machine learning models such as logistic regression, random forest (RF), neural networks and gradient boosting machine (GBM) and addresses the issue of imbalanced data sets to investigate bank’s balance sheet features and bank’s stock features which may potentially determine the factors of systemic risk emission.

The study reports that across various performance matrices, the authors find that two specifications are preferred: RF and GBM. The study identifies lag of the estimator of systemic risk, stock beta, stock volatility and return on equity as important features to explain emission of systemic risk.

The findings will help banks and regulators with the key features that can be used to formulate the policy decisions.

This study contributes to the existing literature by suggesting classification algorithms that can be used to model the probability of systemic risk emission in a classification problem setting. Further, the study identifies the features responsible for the likelihood of systemic risk.