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The best algorithm that was implemented on this Brazilian dataset was artificial immune system (AIS) algorithm. But the time and cost of this algorithm are high. Using asexual reproduction optimization (ARO) algorithm, the authors achieved better results in less time. So the authors achieved less cost in a shorter time. Their framework addressed the problems such as high costs and training time in credit card fraud detection. This simple and effective approach has achieved better results than the best techniques implemented on our dataset so far. The purpose of this paper is to detect credit card fraud using ARO.

In this paper, the authors used ARO algorithm to classify the bank transactions into fraud and legitimate. ARO is taken from asexual reproduction. Asexual reproduction refers to a kind of production in which one parent produces offspring identical to herself. In ARO algorithm, an individual is shown by a vector of variables. Each variable is considered as a chromosome. A binary string represents a chromosome consisted of genes. It is supposed that every generated answer exists in the environment, and because of limited resources, only the best solution can remain alive. The algorithm starts with a random individual in the answer scope. This parent reproduces the offspring named bud. Either the parent or the offspring can survive. In this competition, the one which outperforms in fitness function remains alive. If the offspring has suitable performance, it will be the next parent, and the current parent becomes obsolete. Otherwise, the offspring perishes, and the present parent survives. The algorithm recurs until the stop condition occurs.

Results showed that ARO had increased the AUC (i.e. area under a receiver operating characteristic (ROC) curve), sensitivity, precision, specificity and accuracy by 13%, 25%, 56%, 3% and 3%, in comparison with AIS, respectively. The authors achieved a high precision value indicating that if ARO detects a record as a fraud, with a high probability, it is a fraud one. Supporting a real-time fraud detection system is another vital issue. ARO outperforms AIS not only in the mentioned criteria, but also decreases the training time by 75% in comparison with the AIS, which is a significant figure.

In this paper, the authors implemented the ARO in credit card fraud detection. The authors compared the results with those of the AIS, which was one of the best methods ever implemented on the benchmark dataset. The chief focus of the fraud detection studies is finding the algorithms that can detect legal transactions from the fraudulent ones with high detection accuracy in the shortest time and at a low cost. That ARO meets all these demands.

This paper aims to present a bibliometric analysis of publications from the Scopus database on fraud detection studies.

The current research used Microsoft Excel to conduct the frequency analysis, VOSviewer for data visualisation and Harzing’s Publish or Perish for citation metrics and analysis.

In alignment with these research results, the publications on fraud detection studies have been consistently increasing since 2005. India was rated first as the most active country in fraud detection research. Tongji University from China was the most active institution that published significant publications related to fraud detection research. A total of 160 scholars from 89 various countries and 160 different institutions published several fraud detections studies with multi-authors’ participation in different languages.

To the best of the authors knowledge, this study is the first study to review fraud detections publications in the Scopus science database.

The purpose of this study is to investigate the effectiveness of fraud detection instruments in not-for-profit (NFP) organizations. Not-for-profit organizations rely on trust and volunteer support. They are often small in size and do not have relevant expertise to prevent fraud. Such organizations are more vulnerable to fraud and, consequently, require effective fraud detection instruments. The existing literature on fraud detection is primarily descriptive and does not measure instrument performance. The authors address this research gap and provide a detailed overview of the impact of nine common fraud detection instruments.

Data were obtained from an NFP fraud survey conducted in Australia and New Zealand. A set of contingency tables is produced to explore the relationship between the existence of a specific fraud detection instrument and actual detection of fraud. We also investigate the relationship between organization size and fraud detection strategy.

The findings provide valuable insights into understanding fraud detection mechanisms. Although most fraud detection measures may not lead to more fraud detection, three highly effective instruments emerge, namely, fraud control policies, whistle-blower policies and fraud risk registers. The results also reveal that commonly used fraud detection instruments are not necessarily the most effective. This is true in a significant number of small organizations that appear to be focusing on ineffective fraud detection instruments.

Implementation of more effective fraud detection measures will reduce the damage caused to an organization and is highly relevant for practitioners.

The results show that differences in the effectiveness of fraud detection instruments in the NFP sector exist. This knowledge is directly applicable by related organizations to reduce fraud damage.

This paper aims to provide information of parameters and techniques used in the automated fraud detection system during online transaction. With the increase in the use of online transactions, the concerns regarding data security have also increased. To tackle the frauds, lot of research has been done and plethora of papers are available on the related topics. The purpose of this paper is to provide the clear pathway for researchers to move in the direction of development of automated fraud detection system to prevent the fraud during online transaction.

This literature review analyses and compares the different types of techniques for detecting fraud during online transaction. An in-depth study of the most prominent journals has been done and the core methodology of the papers has been presented. This article also shed some light on different types of parameters used in fraud detection techniques during online transaction.

There are vast varieties of various fraud detection techniques, and every technique has completed task in its own way. After studying approximately 41 research papers, 14 books and four reports, in total 30 parameters have been identified and a detailed study of the parameters has been presented. The parameters are also listed with their details that how these parameters are used in the security system for detecting online transaction fraud.

This paper provides empirical insight about the parameters and their prominence in the development of automated fraud detection security system of online transaction. This paper encourages the researchers to development of improved fraud detection system.

This paper will pave the way for researchers to do a focused research on the fraud detection methodologies. The analysis will help in zeroing down the most prevalent topic of research in this field. The researchers will be able to understand the internal details of parameters and techniques used in the fraud detection systems. This literature also helps the research to think in a variety of ways that how these parameters will be used in the development of fraud detection system.

This paper is one of the most comprehensive reviews in its field. It tries and attempts to fill a void created because of lack of compilation of the laid fraud detection parameters.

Currency usage either in the physical or electronic marketplace through chip-based or magnetic strip-based plastic card becoming the vulnerable point for the handlers. Proper education and awareness can only thrive when concrete fraud detection techniques are being suggested together with potential mitigation possibilities. The purpose of this research study is tendering in the same direction with a suitable plan of action in developing the authentication strength metric to give weightage marks for authentication techniques.

In this research study, a qualitative in-depth exploration approach is being adapted for a better description, interpretation, conceptualization for attaining exhaustive insights into specific notions. A concrete method of observation is being adopted to study various time boxed reports on plastic card fraud and its possible impacts. Content and narrative analysis are being followed to interpret more qualitative and less quantitative story about existing fraud detection techniques. Moreover, an authentication strength metric is being developed on the basis of time, cost and human interactions.

The archived data narrated in various published research articles represent the local and global environment and the need for plastic card money. It gives the breathing sense and capabilities in the marketplace. The authentication strength metric gives a supporting hand for more solidification of the authentication technique with respect to the time, cost and human ease.

The research study is well controlled and sufficient interpretive. The empirical representation of authentication technique and fraud detection technique identification and suggestive mitigation gives this research study an implication view for the imbibing research youths. An application and metric based pathway of this research study provides a smoother way to tackle futuristic issues and challenges.

This research study represents comprehensive knowledge about the causes of the notion of plastic card fraud. The authentication strength metric represents the novelty of a research study which produced on the basis of rigorous documentary and classified research analysis. The creativity of the research study is rendering the profound and thoughtful reflection of the novel dimension in the same domain.

This study aims to combine two fraud-related streams of the literature on guanxi and overconfidence into an integrated framework, which is the fraud triangle, to interpret the mechanism of fraud commission and detection.

A bivariate probit model with Partial Observability (POBi Probit) is applied. Moreover, the POBi Probit model is adjusted to the Chinese context. The China-specific POBi Probit model is constructed using data of Chinese A-share listed companies from 2008 to 2014, with a total of 15,109 firm-year observations.

Overconfidence induces fraud commission and worsens fraud detection; overconfidence mediates the relationship between fraud and guanxi; the “white side” of guanxi comes from alumni networks, while the “dark side” is derived from relatives-based networks; overconfidence induces fraud commission in accounting and disclosure and benefits the detection of disclosure frauds. Guanxi suppresses fraud commission in management and disclosure, however, it worsens fraud detection given fraud in management and disclosure; overconfidence induces fraud commission in both state-owned enterprises (SOE) and non-SOEs, and benefits fraud detection in SOEs. Guanxi suppresses fraud commission and worsens fraud detection in SOEs and city-owned firms.

There are two drawbacks of the partial observable bivariate probit (POBi-Probit) method that must be mentioned here. On one hand, the ex ante variable selection is one of the most difficult parts of applying the POBi-Probit model and different variables are included in different studies. On the other hand, the POBi-Probit model might not converge if too many variables are included. Thus, many widely accepted factors can be included in the model. Thus, this study initially sets the POBi-Probit model based mainly on Khanna et al. (2015) and then adjusts the model for the Chinese context (e. g. considering government ownership) according to Yiu et al. (2018) and Zhang (2018) and the local study of Meng et al. (2019). Considering the observability of fraud, on one hand, the observability of fraud commission is a widely accepted limitation, especially when accounting opacity comes across with regulatory efficiency (Yiu et al. (2018). On the other hand, the observability of relationships is another obstacle to this study. Future studies can go further by revealing the presently unobservable relationships using Big Data technology.

This paper theoretically and practically contributes to the literature on both corporate fraud and corporate governance. Theoretically, by introducing integrated principal-agent resource-reliance theory (IPRT) and upper echelon theory (UET), this paper broadens the framework of fraud triangle theory (FTT) and testifies the availability of the broaden FTT in the transitional and emerging-market context of China. Practically, this paper provides evidence that guanxi and overconfidence are two of the factors affecting corporate fraud. Thus, this paper provides a governance approach opposing corporate fraud in China, which may help the other emerging economies in transition.

Fraud transaction detection has become a significant factor in the communication technologies and electronic commerce systems, as it affects the usage of electronic payment. Even though, various fraud detection methods are developed, enhancing the performance of electronic payment by detecting the fraudsters results in a great challenge in the bank transaction.

This paper aims to design the fraud detection mechanism using the proposed Harris water optimization-based deep recurrent neural network (HWO-based deep RNN). The proposed fraud detection strategy includes three different phases, namely, pre-processing, feature selection and fraud detection. Initially, the input transactional data is subjected to the pre-processing phase, where the data is pre-processed using the Box-Cox transformation to remove the redundant and noise values from data. The pre-processed data is passed to the feature selection phase, where the essential and the suitable features are selected using the wrapper model. The selected feature makes the classifier to perform better detection performance. Finally, the selected features are fed to the detection phase, where the deep recurrent neural network classifier is used to achieve the fraud detection process such that the training process of the classifier is done by the proposed Harris water optimization algorithm, which is the integration of water wave optimization and Harris hawks optimization.

Moreover, the proposed HWO-based deep RNN obtained better performance in terms of the metrics, such as accuracy, sensitivity and specificity with the values of 0.9192, 0.7642 and 0.9943.

An effective fraud detection method named HWO-based deep RNN is designed to detect the frauds in the bank transaction. The optimal features selected using the wrapper model enable the classifier to find fraudulent activities more efficiently. However, the accurate detection result is evaluated through the optimization model based on the fitness measure such that the function with the minimal error value is declared as the best solution, as it yields better detection results.

The purpose of this paper is to survey the automobile insurance fraud detection literature in the past 31 years (1990–2021) and present a research agenda that addresses the challenges and opportunities artificial intelligence and machine learning bring to car insurance fraud detection.

Content analysis methodology is used to analyze 46 peer-reviewed academic papers from 31 journals plus eight conference proceedings to identify their research themes and detect trends and changes in the automobile insurance fraud detection literature according to content characteristics.

This study found that automobile insurance fraud detection is going through a transformation, where traditional statistics-based detection methods are replaced by data mining- and artificial intelligence-based approaches. In this study, it was also noticed that cost-sensitive and hybrid approaches are the up-and-coming avenues for further research.

This paper’s findings not only highlight the rise and benefits of data mining- and artificial intelligence-based automobile insurance fraud detection but also highlight the deficiencies observable in this field such as the lack of cost-sensitive approaches or the absence of reliable data sets.

This paper offers greater insight into how artificial intelligence and data mining challenges traditional automobile insurance fraud detection models and addresses the need to develop new cost-sensitive fraud detection methods that identify new real-world data sets.

This study investigates the perception of professionals in the field of accounting, and those associated with forensic auditing, about the knowledge and skills, experience and technique that a forensic auditor should possess to provide high-quality services in fraud detection. The study also shows the impact of forensic auditing tools on fraud detection.

With the use of a self-administered questionnaire, the study adopts a survey design in which 298 respondents participated. Data were subjected to descriptive statistics (ranking, mean and standard deviation), inferential statistics (binary logistic regression and ordinary least square regression).

The findings indicate that adequate knowledge of economic damage calculation and financial statement valuation is essential for forensic auditors' service. The results also reveal that forensic auditor skills and techniques is a significant predictor for fraud detection in the Nigerian public sector.

The paper draws attention of the federal government parastatals to the need to improve their internal control system to reduce the fraudulent practices in their parastatal. The study also draws the attention of the Nigeria University Commission and the Institute of Chartered Accountants of Nigeria on the needs for revision of the accounting curricular for the training of accounting graduates and professional accountants in Nigeria.

The paper is of importance to other developing nation as it provides empirical evidence on the needs to do periodic forensic audits of government corporations.

With the persistent increase in the number of fraudulent cases, current views of those associated with forensic auditing (judiciaries, parastatals, forensic auditors and academics) on mechanisms for timely detection of fraud are needed.

This paper aims to determine how forensic accounting contributes to fraud detection and prevention and answer the following research questions: What are the standard techniques for fraud detection and prevention; and What are the significant challenges that hinder the application of forensic accounting in fraud prevention and detection?

The authors use the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) method to carry out a systematic literature review (SLR) to identify and assess the existing literature on forensic accounting.

There exists a positive correlation between forensic accounting and fraud detection and prevention. Moreover, in both the empirical and non-empirical findings, the authors note that fraud is complex, and in carrying out fraud investigations, one must be aware of its complexity.

Although drug counterfeiting is a sector where forensic accountants have paid less attention, it is a rapidly expanding fraud area. This paper finds that to detect fraud at an early stage, one must increase consumer understanding of basic forensic accounting techniques by implementing accurate supply chain monitoring systems and inventory management controls and conducting adequate and effective regulatory, honest and legitimate customs inspections.

The major factors that restrict forensic accounting are a lack of awareness and education. Hence, it is essential to incorporate forensic accounting in undergraduate and post-graduate courses.

From the existing literature, it has been observed that very few studies have been conducted in this field using the PRISMA and SLR techniques. Also, the authors carried out a holistic study that focuses on three different areas – fraud detection, fraud prevention and the challenges in forensic accounting.