TRENDS IN BUSINESS FRAUDS: CASE OF CROATIA

Business frauds and losses caused by fraud led to corporate scandals and the collapse of some of the world's largest corporations such as Enron and Satyam, andfocused public attention on the problem offrauds and fraudulent financial reporting. The Association of Certified Fraud Examiners has been conducting global fraud research for years in order to familiarize the public with the main characteristics of business frauds and raise the level offraud awareness. A comprehensive survey on fraud in the Republic of Croatia was conductedfor the first time in 2021, and included 124 respondents from 16 different industrial sectors. The research related to frauds that occurred during 2021 and 2020, i.e. it covered the two years affected by the COVID-19 crisis. The research was conducted by the Association of Certified Fraud Examiners Croatia. This paper presents some of the main results of the research and provides a comparative presentation and analysis of trends in the Republic of Croatia with global trends in business frauds. According to the results presented by ACFE Croatia, total financial losses caused by business frauds amount to about HRK 165 million. Furthermore, according to estimates, about 13% of total annual income was lost due to fraud. The characteristics of frauds in the Republic of Croatia do not differ significantly from frauds on the global level, but certain specificities have been observed. The aim of this paper is to point out the basic characteristics and determinants of business frauds in the Republic of Croatia, to compare trends in the Republic of Croatia with the results of research at the global level, and to highlight the importance of anti-fraud controls and raising awareness about fraud at all levels of society. Keywords: business frauds, fraud detection, fraud prevention, anti-fraud controls

Telemedicine Scams

Telemedicine emerged as a lifeline during the COVID-19 pandemic. Although the technology existed long before the pandemic, its use was limited due to strict government regulations that limited reimbursement for telemedicine visits. In response to the pandemic, the Government waived many of its restrictions for the duration of the Public Health Emergency. These changes fueled the growth of telemedicine. The problem, however, is that telemedicine makes it easier to conduct fraud on a large scale because without in-person visits, medical providers can reach many more beneficiaries in a short period of time. Thus, the size and scale of typical health care fraud schemes, such as sending medically unnecessary durable medical equipment, is magnified. This type of fraud has been on the rise since 2016, and, with the relaxed rules for telemedicine reimbursement during the pandemic, there is a serious concern that there will be a sharp increase in telemedicine fraud. This Article examines the fraudulent practices in the telemedicine industry and the conditions that permit them to flourish. This Article critically assesses the changes to telemedicine coverage and their relationship to fraud. It examines the fraudulent practices through the lens of the fraud triangle to determine why telemedicine fraud occurs. After assessing the causes of telemedicine fraud, this Article argues that there is no need for additional criminal statutes to address telemedicine fraud. As the typical telemedicine scam involves the payment of kickbacks and billing for medically unnecessary treatment and services, the existing fraud laws such as the Anti-Kickback statute and the False Claims Act are sufficiently capacious to address the criminality involved in these cases. This Article also argues that in lieu of additional criminal statutes, the Government should focus on additional measures to prevent or detect telemedicine fraud because preventative measures are the best way to safeguard the integrity of federal health care programs.

Artificial Intelligence Improves the Marketing of Brands

The effective usage of supermarkets is visible in customer care, quality control, inventory management, personalization, pricing, and fraud detection as well. During COVID time, when most of the brands were looking for social distancing options, artificial intelligence helped the brands in improving social distancing as well. [...]when it comes to marketing, branding, and communication in such a scenario, the influencers or marketing communicators would need to be and are finding themselves compelled to be from the knowledgeable and highly capable and equally ‘highly qualified and accomplished’ genre. The effective usage of supermarkets is visible in customer care, quality control, inventory management, personalization, pricing, and fraud detection as well. During COVID time, when most of the brands were looking for social distancing options, artificial intelligence helped the brands in improving social distancing as well. [...]when it comes to marketing, branding, and communication in such a scenario, the influencers or marketing communicators would need to be and are finding themselves compelled to be from the knowledgeable and highly capable and equally ‘highly qualified and accomplished’ genre.

It's Fraud! Application of Machine Learning Techniques for Detection of Fraudulent Digital Advertising

Due to the on-going COVID-19 pandemic, industries are heavily reliant on online meeting platforms. The pandemic has forced most MNCs to rely on online platforms such as Zoom or Microsoft Teams to hold daily business meetings and even conferences and other corporate events. Schools and other educational institutions have also been forced to conduct classes and all other events through these platforms. This increased unavoidable dependence on online platforms has resulted in an exponential increase in advertising fraud. Advertising fraud is the application of any method or technology that hampers the proper delivery of advertisements to the proper audience or the proper place, or forcefully inserts advertisements at undesirable times or locations. This could take multiple forms and has become far more widespread with increased use of online platforms. Some common methods used for digital fraud can be the pay-per-click (PPC) model, domain spoofing or in the form of bots, but the main objective is to gain financial advantages from advertising transactions. The primary objective of this study is to identify and understand the factors lying behind the presence of fraudulent activities on any online medium and to analyse the probability of downloading an application after coming across the online advertisement, and watching it. The study also aims to highlight how marketing agencies tend to float fraud advertisement just to gain more revenue from their end.

Financial Fraud Detection Approach Based on Firefly Optimization Algorithm and Support Vector Machine

The usage of credit cards is increasing daily for online transactions to buy and sell goods, and this has also increased the frequency of online credit card fraud. Credit card fraud has become a serious issue for financial institutions over the last decades. Recent research has developed a machine learning (ML)-based credit card fraud transaction system, but due to the high dimensionality of the feature vector and the issue of class imbalance in any credit card dataset, there is a need to adopt optimization techniques. In this paper, a new methodology has been proposed for detecting credit card fraud (financial fraud) that is a hybridization of the firefly bio-inspired optimization algorithm and a support vector machine (called FFSVM), which comprises two sequential levels. In the first level, the firefly algorithm (FFA) and the CfsSubsetEval feature section method have been applied to optimize the subset of features, while in the second level, the support vector machine classifier has been used to build the training model for the detection of credit card fraud cases. Furthermore, a comparative study has been performed between the proposed approach and the existing techniques. The proposed approach has achieved an accuracy of 85.65% and successfully classified 591 transactions, which is far better than the existing techniques. The proposed approach has enhanced classification accuracy, reduced incorrect classification of credit card transactions, and reduced misclassification costs. The evaluation results show that the proposed FFSVM method outperforms other nonoptimization machine learning techniques.

Fraud Boosts Tech

Curbelo discusses that businesses are investing more in anti-fraud technology amid the increase of fraud. In response to heightened fraud risk during the COVID-19 pandemic, 43% of organizations have increased their use of data analytics to fight fraud, according to a new study by the Association of Certified Fraud Examiners (ACFE). The 2022 Anti-Fraud Technology Benchmarking Report also predicts an expansion of anti-fraud technology over the next two years, with the use of machine learning and artificial intelligence in organizations expected to double.