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The research on analysis on the new type and countermeasures of credit card fraud in mainland China mainly aims to take a comprehensive approach to fight against fraud and place a strong emphasis on fraud prevention conducted in the best interests of financial institutions, card holders, merchants and law enforcement authorities to keep fraud from happening in the first place and to give more information on anti‐credit card fraud best practices.

In this paper, the authors analyzed the relevant definitions in Chinese statutes and ordinances, the new types of credit card fraud which have occurred in China in recent years and what would be the effective countermeasures to fight against and prevent them. It was completed based on many references and extensive survey in police organs in China.

With the credit card more and more widely used in mainland China as a most prevalent means of payment and settlement, credit card crime has been rapidly increasing to accompany this, with more fraudulent and disguised features which add to the complexity and difficulties of the work on its combating and prevention.

Hopefully, it explores for the best to detect and combat credit card fraud crimes and can contribute to the healthy growth of bankcard industries, meanwhile calling for the involvement of the concerted efforts of the banks, merchants, the public and the law enforcement personnel.

Every year, millions of consumers around the world become victims of credit card fraud. These individuals have to appeal to their credit card companies to reverse unauthorized charges. This study aims to profile the American consumers’ experience when complaints to their credit card companies about unauthorized charges fail to produce a resolution. Using a large database of consumer complaint filings with the Consumer Financial Protection Bureau (CFPB), the characteristics of these consumer complaints are identified, and the drivers of consumer financial hardship resulting from credit card fraud are determined.

A random sample of consumer complaints about their credit card companies’ perceived mishandling of cases, filed with the CFPB, is used to conduct content analysis. The resulting content analysis categories are used in a predictive model to determine the drivers of consumer hardship.

In nearly one-quarter of all complaint filings, the credit card company had blamed the complainant as the party responsible for the fraudulent charges or refused to open a fraud investigation altogether. Nearly 60% of complaint reports contain expressions of emotional distress and many mention financial hardship. Nearly half of all complainants consider the fraud department operations of their credit card company as lacking in service quality, many reporting inability to reach the department or to receive a returned call. Even after CFPB intermediation, only 15% of complainants receive some form of financial relief from their credit card company. The majority of the complainants report a lack of willingness by the credit card company to reverse unauathorized charges, leaving the complainant financially responsible for them.

This study focused on data collected from consumers. Future research can expand the scope of inquiry by surveying the staff and executives in the fraud investigation departments of credit card companies to determine the norms of fraud investigation used within the industry.

This study sheds light on the financial hardship and emotional pains that consumers victimized by credit card fraud experience in dealing with their credit card companies.

To the best of the authors’ knowledge, this is the first study to empirically examine American consumers’ complaints about the fraud investigation operations of their credit card companies. Using data captured through the complaint filing system of a federal bureau (CFPB), the findings have implications for policymakers, regulators and credit card companies.

To make readers aware of the pervasiveness of credit card fraud and how it affects credit card companies, merchants and consumers.

A range of recent publications in journals and information from internet web sites provide corroboration and details of how fraudsters are using credit cards to steal billions of dollars each year. Numerous schemes and techniques are described in addition to recommendations as to how to help control this growing type of fraud.

Credit card fraud is a healthy and growing means of stealing billions of dollars from credit card companies, merchants and consumers. This paper offers current information to help understand the techniques used by fraudsters and how to avoid falling prey to them.

This fraud relies on technology currently available and the easy ability to obtain machinery to steal individual identities and account information, and to produce fraudulent credit cards. Information cited is current but could change radically as technological breakthroughs occur. The changing nature of technology also affects the recommendations made to control this fraud.

A very useful source of current information on credit card fraud for bank, credit card companies, merchants, and consumers.

This paper provides specific current information and recommendations regarding a fraud topic that is of interest to a wide audience.

The purpose of this paper, which is based on author's PhD study, is to assess the efficacy of Indonesia's credit card fraud prevention from a strategic point of view, using a model of payments fraud prevention practice developed by the author based on similar practices in the USA, the UK and Australia.

Primary and secondary data, particularly from the payments system of the USA, the UK, Australia and Indonesia were used. Such data were collected by means of literature reviews and in‐depth interviews with payments system professionals.

The author establishes that credit card fraud prevention practice in Indonesia is still at a lower level of robustness than those in the USA, the UK and Australia. Deficiencies in the credit card fraud prevention practice in Indonesia are indicated, inter alia, by a lack of reliable fraud data collection, management and distribution mechanisms as well as a lack of effective and efficient identity management practice. Deficiencies and weaknesses in the system should be identified and action taken to make it more consistent with credit card fraud prevention practices of other countries.

The paper sees credit card fraud prevention practice in Indonesia as a function of many factors which influence one another, based on which the analysis is built.

The purpose of this paper is to discuss credit card fraud in Trinidad and Tobago.

The paper describes credit card typologies in Trinidad and Tobago and the existing law governing such fraud. It outlines the success and inadequacies of the enforcement machinery and issues involving credit card fraud detection and prevention.

The law regarding credit cards is in a very confused and unsatisfactory state in Trinidad and Tobago. Education in counteracting the criminal activities of credit card fraudsters is vital. Informing the public of the various fraudulent typologies relative to credit cards and at the same time, advising members how to protect themselves are the most effective methods to address the fraud problem. The Bankers Association of Trinidad and Tobago should also play a critical role in addressing credit card fraud. The association should formulate credit card policies along similar principles as those formulated for cheque fraud to benefit all banks and merchants.

Credit card fraud has only been a recent phenomenon in Trinidad and Tobago. This paper is valuable in offering suggestions as to the way forward in the prevention of such fraud.

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 paper aims to examine the “effectiveness” of the Cyprus police in investigating credit card fraud.

In total, 19 semi‐structured interviews with key criminal justice personnel were undertaken to assess the current capacity of police in investigating credit card fraud.

The paper discovers that a far more co‐ordinated approach is needed to tackle credit card fraud in Cyprus, with a lack of specialised knowledge of fraud a major concern.

All interview schedules were sent before the interviews took place and three of the respondents provided written responses only.

This is the first study of its kind that involved primary research interviewing 19 key personnel involved in policing credit card fraud in Cyprus.

The purpose of this paper, which is based on author's PhD study, is to analyze the trends in credit card fraud prevention in the USA, the UK, Australia and Indonesia, particularly over the period 2003‐2007, with special focus on the fraud prevention practices in the payments systems.

This study uses primary and secondary data particularly from the payments systems of the USA, the UK, Australia and Indonesia to conduct historical and benchmarking analyses to highlight the trends in credit card fraud prevention in the four countries.

The study establishes that a common approach in preventing credit card fraud is reducing offenders' opportunities to commit their offences, which often require significant amount of resources and thus sound strategy needs to be properly formulated and executed. Referring primarily to the practices in the USA, the UK, Australia and Indonesia, resources are mainly allocated to six key areas of fraud prevention: understanding of the real problems, fraud prevention policy, fraud awareness, technology‐based protection, identity management and legal deterrence. These are supported in principle by four main groups in a payments system: user, institution, network and government and industry.

The paper provides insights into the nature of credit card fraud, as well as a framework for designing a sound credit card fraud prevention strategy in a country's payments system.

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 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.