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Purpose: The study aims to explore the wider acceptance of blockchain technology and growing faith in this technology among all business domains to mitigate the chances of fraud in various sectors.

Design/Methodology/Approach: The authors focus on studies conducted during 2015–2022 using keywords such as blockchain, fraud detection and financial domain for Systematic Literature Review (SLR). The SLR approach entails two databases, namely, Scopus and IEEE Xplore, to seek relevant articles covering the effectiveness of blockchain technology in controlling financial fraud.

Findings: The findings of the research explored different types of business domains using blockchains in detecting fraud. They examined their effectiveness in other sectors such as insurance, banks, online transactions, real estate, credit card usage, etc.

Practical Implications: The results of this research highlight (1) the real-life applications of blockchain technology to secure the gateway for online transactions; (2) people from diverse backgrounds with different business objectives can strongly rely on blockchains to prevent fraud.

Originality/Value: The SLR conducted in this study assists in the identification of future avenues with practical implications, making researchers aware of the work so far carried out for checking the effectiveness of blockchain; however, it does not ignore the possibility of zero to less effectiveness in some businesses which is yet to be explored.

Purpose: This chapter aims to examine machine learning (ML) models for predicting credit card fraud (CCF).

Need for the study: With the advance of technology, the world is increasingly relying on credit cards rather than cash in daily life. This creates a slew of new opportunities for fraudulent individuals to abuse these cards. As of December 2020, global card losses reached $28.65billion, up 2.9% from $27.85 billion in 2018, according to the Nilson 2019 research. To safeguard the safety of credit card users, the credit card issuer should include a service that protects customers from potential risks. CCF has become a severe threat as internet buying has grown. To this goal, various studies in the field of automatic and real-time fraud detection are required. Due to their advantageous properties, the most recent ones employ a variety of ML algorithms and techniques to construct a well-fitting model to detect fraudulent transactions. When it comes to recognising credit card risk is huge and high-dimensional data, feature selection (FS) is critical for improving classification accuracy and fraud detection.

Methodology/design/approach: The objectives of this chapter are to construct a new model for credit card fraud detection (CCFD) based on principal component analysis (PCA) for FS and using supervised ML techniques such as K-nearest neighbour (KNN), ridge classifier, gradient boosting, quadratic discriminant analysis, AdaBoost, and random forest for classification of fraudulent and legitimate transactions. When compared to earlier experiments, the suggested approach demonstrates a high capacity for detecting fraudulent transactions. To be more precise, our model’s resilience is constructed by integrating the power of PCA for determining the most useful predictive features. The experimental analysis was performed on German credit card and Taiwan credit card data sets.

Findings: The experimental findings revealed that the KNN achieved an accuracy of 96.29%, recall of 100%, and precision of 96.29%, which is the best performing model on the German data set. While the ridge classifier was the best performing model on Taiwan Credit data with an accuracy of 81.75%, recall of 34.89, and precision of 66.61%.

Practical implications: The poor performance of the models on the Taiwan data revealed that it is an imbalanced credit card data set. The comparison of our proposed models with state-of-the-art credit card ML models showed that our results were competitive.

This chapter focuses on a case study of Kareem Exchange Company and its use of big data analysis to detect and prevent fraud and suspicious financial transactions. The chapter describes the various phases of the big data analysis cycle, including discovery, data preparation, model planning, model building, operationalization, and communicating results, and how the Kareem Exchange Company team implemented each phase. This chapter emphasizes the importance of identifying the business problem, understanding the resources and stakeholders involved, and developing an initial hypothesis to guide the analysis. The case study results demonstrate the potential of big data analysis to improve fraud detection capabilities in financial institutions, leading to informed decision making and action.

This chapter introduces a risk control framework on credit card fraud instead of providing a solely binary classifier model. The anomaly detection approach is adopted to identify fraud events as the outliers of the reconstruction error of a trained autoencoder (AE). The trained AE shows fitness and robustness on the normal transactions and heterogeneous behavior on fraud activities. The cost of false-positive normal transactions is controlled, and the loss of false-negative frauds can be evaluated by the thresholds from the percentiles of reconstruction error of trained AE on normal transactions. To align the risk assessment of the economic and financial situation, the risk manager can adjust the threshold to meet the risk control requirements. Using the 95th percentile as the threshold, the rate of wrongly detecting normal transactions is controlled at 5% and the true positive rate is 86%. For the 99th percentile threshold, the well-controlled false positive rate is around 1% and 83% for the truly detecting fraud activities. The performance of a false positive rate and the true positive rate is competitive with other supervised learning algorithms.

Introduction: The insurance sector is playing a crucial role in the sustainable growth of the Indian economy. But in India, this sector loses crores of rupees every year due to the increasing fraud cases. With the increase in insurance customers, insurance companies need to efficiently equip themselves with a robust system to handle claims fraud. Detection of insurance fraud is a pretty challenging problem. Nowadays, machine learning (ML) and artificial intelligence (AI) are the strategic choices of many leading organisations that want to proceed in a new digital arena.

Purpose: This chapter’s main objective is to highlight the fundamental market forces driving the adoption of AI and ML and showcase the traditional and modern methods to predict insurance claims fraud intelligently.

Methodology: Various research papers have been reviewed, and ML methods have been discussed, which are all being used to predict insurance fraud claims. This chapter also highlights various driving forces influencing the adoption of ML.

Findings: This study highlights the introduction of blockchain technology in fraud detection and in combatting insurance fraud. Literature indicates that the quantity and quality of data significantly impact predictive accuracy. ML models are beneficial to identify the majority of fraudulent cases with reasonable precision. Insurance companies should explore the benefits of experienced resource persons from the same domain and develop unique business ideas/rules.

Researchers are continually trying to find reliable fraud indicators (e.g., Beasley, 1996) and some are working on building fraud prediction models (e.g., Spathis, 2002) to aid auditors in fraud detection. With this same goal of predicting fraud in mind, the purpose of this study is to explore the potential of qualitative fraud risk indicators. Content analysis is used in analyzing the Management's Discussion and Analysis (MDA) section of the annual report to identify potential indicators of deception to increase the likelihood of fraud detection in a timelier manner than current quantitative models.

By examining asynchronous communication contained in annual reports for companies required by the SEC to restate their financial statements, patterns of key linguistic characteristics were identified and compared to those used by companies not required to restate. Findings evidence significant differences on several dimensions. Using language cues for detection of deception has the advantage over quantitative methods of providing a more timely method of determining deception. Quantitative models often cannot detect deception until the effects are validated by financial impairment.

Implications of the findings suggest that qualitative methods of deception detection may provide an earlier, and thus more useful, method of the detection of fraud. The results of this study should provide stakeholders with a set of indicators to aid in identifying misstated information. This approach is also one that can be generalized to other written documents used to predict fraudulent communication.

In the early days of the COVID-19 pandemic, an area could only report its first positive cases if the infection had spread into the area and if the infection was subsequently detected. A standard probit model does not correctly account for these two distinct latent processes but assumes there is a single underlying process for an observed outcome. A similar issue confounds research on other binary outcomes such as corporate wrongdoing, acquisitions, hiring, and new venture establishments. The bivariate probit model enables empirical analysis of two distinct latent binary processes that jointly produce a single observed binary outcome. One common challenge of applying the bivariate probit model is that it may not converge, especially with smaller sample sizes. We use Monte Carlo simulations to give guidance on the sample characteristics needed to accurately estimate a bivariate probit model. We then demonstrate the use of the bivariate probit to model infection and detection as two distinct processes behind county-level COVID-19 reports in the United States. Finally, we discuss several organizational outcomes that strategy scholars might analyze using the bivariate probit model in future research.

The need for independent audit goes back to the agency theory, the theory of delegation of power and the issue of trust. Stakeholders delegate power to management to manage the business on their behalf, yet they face the risk of information asymmetry and management motivations to commit fraud. The main aim of having an independent auditor was therefore to reduce the risk of information asymmetry and fraudulent behaviour by management. Auditors are required by the International Auditing Standards to detect material fraud and error, and they are expected to have a duty of care for stakeholders. However, recently independent auditors, whether conducting private or public audit, have been scrutinised for failing to detect material fraud. There have been a lot of discussions in the literature about the role of private auditors in detecting fraud, but very little discussions about the role of public auditors in detecting fraud. This chapter will outline the difference between private audit and public audit; explain the legal liability of public auditors in relation to fraud detection; the role of public auditors in detecting fraud; and will critically review the root causes for auditors’ failure to detect fraud.

Accounting-based financial scandals caused by fraudulent financial reports negatively affect the financial markets and cause loss of confidence in investors. Financial reporting quality needs to be improved in order to build and maintain trust in financial markets. To increase the quality of financial reports, fraudulent financial reporting risks should be defined. At this point, regulators, practitioners, and researchers are in constant search.

There are improved approaches to the detection of financial reporting frauds in the literature. Many studies have been conducted on the “Fraud Triangle Theory” and the “Fraud Diamond Theory” approaches. The Fraud Triangle Theory argues that while fraudulent action is taking place in defining the elements of press, rationalization, and opportunity, the Fraud Diamond Theory approach argues that in order to achieve these three elements, the capability to carry out a fraud in individuals must be improved.

In this study, it is aimed to investigate the effect of Fraud Diamond elements on fraudulent financial reports. For the scope of the research, data of 26 companies from Manufacturing Industry enterprises operating in BORSA ISTANBUL between 2013 and 2017 were used. Financial reports of the companies are divided into two groups: (1) Fraudulent Financial Reports and (2) Non-Fraud Financial Reports. The hypotheses developed within the scope of the research were tested using the Logistic Regression analysis in IBM SPSS Statistic 20 program.

As a result of the study, it has been determined that there is a negative correlation between borrowing level, asset profitability, independent audit firm, auditor exchanges and institutionalization level, and fraudulent financial reports. It was understood that the change in assets and the size of the audit committee did not have any effect on the fraudulent financial reports.