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This study aims to investigate the moderating role of natural language processing natural language processing (NLP) on the relationship between AI-empowered AIS (data gathering, data analysis, risk assessment, detection, prevention and Investigation) and auditing and fraud detection.

Quantitative methodology was adapted through a questionnaire. In total, 221 individuals represented the population of the study, and SPSS was used to screen primary data. The study indicated the acceptance of the hypothesis that “Artificial Intelligence in AIS has a statistically significant influence on auditing and fraud detection,” showing a strong correlation between auditing and fraud detection. The study concluded that NLP moderates the relationship between AI in AIS and auditing and fraud detection.

The study’s implications lie in its contribution to the development of theoretical models that explore the complementary attributes of AI and NLP in detecting financial fraud.

A cross-sectional design is a limitation.

NLP is a useful tool for developing more efficient methods for detecting fraudulent activities and audit risks.

The study’s originality stems from its focus on the use of AI-empowered AIS, a relatively new technology that has the potential to significantly impact auditing and fraud detection processes within the accounting field.

Technology advancement has changed how banks operate. Modernizing technology has, on the one hand, made it simpler for banks to do their daily business, but it has also increased cyberattacks. The purpose of the study is to to determine the factors that have the most effects on online fraud detection and to evaluate the advantages of AI and human psychology research in preventing online transaction fraud. Artificial intelligence has been used to create new techniques for both detecting and preventing cybercrimes. Fraud has also been facilitated in some organizations via employee participation.

The main objective of the research approach is to guide the researcher at every stage to realize the main objectives of the study. This quantitative study used a survey-based methodology. Because it allows for both unbiased analysis of the relationship between components and prediction, a quantitative approach was adopted. The study of the body of literature, the design of research questions and the development of instruments and procedures for data collection, analysis and modeling are all part of the research process. The study evaluated the data using Matlab and a structured model analysis method. For reliability analysis and descriptive statistics, IBM SPSS Statistics was used. Reliability and validity were assessed using the measurement model, and the postulated relationship was investigated using the structural model.

There is a risk in scaling at a fast pace, 3D secure is used payer authentication has a maximum mean of 3.830 with SD of 0.7587 and 0.7638, and (CE2).

This study focused on investigating the benefits of artificial intelligence and human personality study in online transaction fraud and to determine the factors that affect something most strongly on online fraud detection. Artificial intelligence and human personality in the Indian banking industry have been emphasized by the current research. The study revealed the benefits of artificial intelligence and human personality like awareness, subjective norms, faster and more efficient detection and cost-effectiveness significantly impact (accept) online fraud detection in the Indian banking industry. Also, security measures and better prediction do not significantly impact (reject) online fraud detection in the Indian banking industry.

This study aims to assess the effectiveness of forensic accounting techniques to prevent and detect fraudulent activities in firms in Pakistan.

A descriptive research approach has been adopted in this study. Primary data has been collected through structured questionnaires distributed to professionals from investigating firms, professional bodies and field researchers. The independent variables that were analyzed included fraud investigation, litigation support and dispute resolution, whereas the dependent variables were fraud detection and prevention. The Statistical Package for Social Sciences has been used for data analysis to derive objective results.

This research reveals that forensic accounting techniques such as fraud investigation, litigation support and dispute resolution have a positive impact on fraud detection and prevention in Pakistani firms.

Firms should train staff on forensic accounting techniques, implement fraud risk management and anti-corruption policies, conduct regular financial statement audits and develop a whistleblower protection program to encourage employees to report fraudulent activities. The government should develop regulations and guidelines to promote the use of forensic accounting in firms.

This study is covering the gap in literature on financial fraud and forensic accounting practices concerning emerging economies such as Pakistan. This study can serve as a valuable resource for firms and policymakers to strengthen their fraud prevention efforts and build a more robust culture of financial integrity.

Stockbrokers’ frauds in India frequently occur, causing investors significant financial loss. This study aims to unfold the various dubious practices adopted by stock brokers in the recent past to defraud investors and the necessary corrective regulations passed by the market regulator to prevent and detect fraud.

The authors conduct exploratory research using a collective model of literature review, case studies and regulatory changes.

The authors find tightening the system’s loopholes and strengthening the regulatory system using technology helps in the early detection and prevention of fraud. Media activism and investors’ awareness play a role in reducing incidences of fraud.

This study unfolds the practices followed by stock brokers to defraud investors, indicative of regulatory gaps and enforcement lapses. Regulators are evolving a robust system to curb these practices and make them on par with international standards. But, it has a long way to go.

Robust fraud detection and prevention mechanism is desirable to restore investors’ confidence in the stock market. Regulators should focus on investors’ protection and education and whistleblowers’ protection. Compared to the market regulators worldwide, the Securities and Exchange Board of India has less power to identify, detect and punish fraudulent brokers and needs to be empowered.

Besides the regulatory changes, strict enforcement and investor campaigns are required to increase public awareness and restore trust in the stock market to combat the recurrence of fraud.

This paper can be helpful to regulators, investors and financial intermediaries like stock brokers and aid in strengthening the reliability of capital markets and restoring investors’ confidence.

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.

Financial fraud is a severe corporate fraud committed for achieving various objectives, like attaining financial targets, lowering debt and providing good signals to the market. Such financial fraud deceives stakeholders and results in substantial financial losses. This study aims to detect financial fraud using the modified Beneish M-Score, the most appropriate forensic tool for fraud detection. Furthermore, the current study also examines the influential role of the fraud triangle’s elements (pressure, opportunity and rationalization) on financial fraud in nonfinancial firms during 2018–2021, offering insight for understanding and mitigating fraudulent activities in the corporate world.

Financial fraud is treated as a dependent variable measured through a modified Beneish M-score, while the fraud triangle elements (pressure, opportunity and rationalization) are measured through six proxies, which are financial stability, leverage, financial target, nature of the industry, the effectiveness of supervision and auditor changes.

The study's finding proclaimed that fraud triangle elements result in financial fraud. Findings unveil that all elements (pressure, opportunity and rationalization) of the fraud triangle significantly influence financial fraud. The study confirms that these elements must be considered to protect investors and provide a safe environment for investment.

Rare literature found addressing the detection of financial fraud and its nexus with the fraud triangle specifically in Pakistan where deficient governance is notably prevalent. This study attempts to fill such a gap and contribute to knowledge.

This study aims to compare machine learning models, datasets and splitting training-testing using data mining methods to detect financial statement fraud.

This study uses a quantitative approach from secondary data on the financial reports of companies listed on the Indonesia Stock Exchange in the last ten years, from 2010 to 2019. Research variables use financial and non-financial variables. Indicators of financial statement fraud are determined based on notes or sanctions from regulators and financial statement restatements with special supervision.

The findings show that the Extremely Randomized Trees (ERT) model performs better than other machine learning models. The best original-sampling dataset compared to other dataset treatments. Training testing splitting 80:10 is the best compared to other training-testing splitting treatments. So the ERT model with an original-sampling dataset and 80:10 training-testing splitting are the most appropriate for detecting future financial statement fraud.

This study can be used by regulators, investors, stakeholders and financial crime experts to add insight into better methods of detecting financial statement fraud.

This study proposes a machine learning model that has not been discussed in previous studies and performs comparisons to obtain the best financial statement fraud detection results. Practitioners and academics can use findings for further research development.

The purpose of this study was to determine the determinants of financial statement fraud: the perspective of pentagon fraud theory.

This study used quantitative methods with an explanatory research design by applying secondary data on Islamic banking companies listed on the Indonesia Stock Exchange (IDX).

External pressure affects financial statement fraud, ineffective monitoring affects financial statement fraud, external auditor quality affects financial statement fraud, change in auditor affects financial statement fraud, frequent number of CEO’s picture affects financial statement fraud, external pressure affects firm size, ineffective monitoring affects firm size, external auditor quality affects firm size, change in auditor affects firm size, frequent number of CEO’s picture affects firm size, firm size affects financial statement fraud, firm size mediates the relationship between external pressure on financial statement fraud, firm size mediates the relationship between ineffective monitoring on financial statement fraud, firm size mediates the relationship between external auditor quality and financial statement fraud, firm size mediates the relationship between change in auditor and financial statement fraud, firm size mediates the relationship between frequent number of CEO’s picture and financial statement fraud.

The limitations of this research were found during the research process and can be used as input for further research and related parties in conducting the research to obtain better research results. The limitations of this study are as follows: this study only focused on Islamic banking, so it cannot be generalized to other sectors. Besides, this study only tested five independent variables, one dependent variable and one mediating variable.

For external auditors, financial statement fraud by management might be caused by many factors and is a social as well as an economic problem that must be addressed immediately. Therefore, in carrying out the duties and roles as an external auditor, they must have an attitude of independence (not taking sides) in the mental attitude that must be maintained by the auditor related to the assignment. Auditors must have sufficient technical expertise and training as auditors. In carrying out the audit, the auditor should use their professional skills in responding carefully and thoroughly. Moreover, in carrying out audit work, the auditor must have a plan, must know adequate internal control and obtain sufficiently competent audit evidence.

To the best of the authors’ knowledge, very few studies in Indonesia have applied the Beneish model. There is only one study that implemented the Beneish model, and the study examined only a few companies listed on the IDX. The findings of the present study have important implications not only for banks but also for users of financial statement accounts in Indonesia, especially for investors, auditors, regulators, taxation and other state authorities.

This study aims to examine the effects of religiosity, ethical leadership and local wisdom on the relationship between fraud risk management and asset misappropriation in Indonesia.

Data were collected using a set of questionnaire surveys administered to the head office, local government internal auditors (inspectorate) and local government employees in Indonesia. Sample selection used purposive techniques and obtained 151 respondents who became research data. The dependent variable was asset misappropriation. The independent variable was fraud risk management. The moderating variables for this study were religiosity, leader ethics and local wisdom. The analysis technique applied the structural equation model-partial least square (SEM-PLS).

Fraud risk management has a significant negative effect on asset misappropriation. In addition, this study finds evidence that religiosity, ethical leadership and local wisdom increase fraud risk management against asset misappropriation.

This study proposes an integrative model that enables local governments to understand fraud risk management. By integrating religiosity, ethical leadership and local wisdom, managers can design strategies to prevent asset misappropriation.

This research has the advantage of proposing an integrative model for mitigating asset misappropriation. Research on asset misappropriation is limited. Therefore, this study provides insights into fraud risk management, particularly in Indonesia’s local governments. In addition, this study adds ethical aspects such as religiosity, leadership and local wisdom to complement the weaknesses of fraud risk management and reduce the potential for asset misappropriation.

This study aims at investigating the effect of corporate governance attributes on the Fraud disclosure of Takaful insurance companies in Saudi Arabia from 2014 to 2022.

This study uses a self-constructed disclosure index to quantify the level of fraud information using content analysis. The count regression (Poisson and negative binomial) models in panel data modeling are used to check the interdependence relationship between the Fraud disclosure and the corporate governance structure of 26 Takaful insurers.

The findings confirm the negative effect of ownership structure and the board size on the Fraud disclosure. However, the high proportion of independent board members, the audit board committee and the size of the risk board committee positively affect the extent of Fraud disclosure. Finally, this study provide evidence that large size of Shariah board is associated with a lower level of voluntary Fraud disclosure.

Both economics-based theories and social exchange theory provide a better basis upon which to understand mechanisms by which board of directors in Takaful insurance provides external stakeholders with valuable information about corporate fraud.

It seems important to equip audit and Shariah board committee with the tools to give them an operational content that focus systematically on the “tone at the top” in investigating fraud, including disclosure and discipline.

Corporate governance is rapidly changing in Saudi Arabia and it is unclear whether adopting a corporate governance practices in financial institutions is appropriate for Islamic insurance companies.