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The fraudulent claims for Unemployment Insurance (UI) have also risen massively in the United States especially during the onset of COVID-19 pandemic with billions of dollars that were lost. These approaches applied formerly in fraud detection and prevention have been challenged by new and advanced fraud systems. For this reason, AI, Big Data and Predictive Analytics are now crucial for improving fraud mitigation in UI programs. The aim of this research is to understand how far AI, Big Data and Predictive Analytics have been utilized, for how effective they are and the barriers they pose in tackling unemployment insurance fraud in the United States. An online quantitative self-administered questionnaire was administered where 200 participants from the unemployment insurance agencies of the U.S, covering fraud investigators, IT personnel, data analysts and policymakers, were included in the study. Data were analyzed to establish the correlation between technology adoption to application of anti-fraud measures using descriptive statistics, chi-square test and regression analysis. The survey shows that about 42.5% of agencies actively use AI and 34% of them use Big Data very often. However, difficulties like high costs, integration troubles and limited availability of specialized staff are still issues that make adoption a problem. The results of using AI and Big Data were moderate and optimistic but rather elevated, as seen through regression analysis; on the other hand, predictive analytics demonstrated large potential when applied successfully. The study’s results indicate that AI, Big Data and Predictive Analytics present enormous potential for mitigating UI fraud, although broad integration will not be possible without overcoming essential infrastructural and resource limitations.

Cryptocurrencies are attracting considerable attention around the world because of the various advantages that they offer. On the other hand, they also carry some inherent risks. Although monetary authorities broadly agree that cryptocurrencies do not engender an immediate threat to national and global financial systems, the future is full of unknowns. In this regard, drawing a framework based on the current drivers of demand for cryptocurrencies would help visualize the prospects for these assets and create a roadmap to avoid or manage any disruptive risks. This discussion paper aims to contribute to the literature by examining the key factors that will determine the future performance of cryptocurrencies. The main conclusion derived from the discussion is that national regulations will potentially affect the direction of cryptocurrencies, as well as the need for any special efforts in the domain of monetary policy

This study examines the ethical beliefs of Turkish consumers using a modified version of the Muncy-Vitell Consumer Ethics Scale (CES). Turkey’s ethical climate faces challenges such as copyright violations, environmental pollution, deceptive advertising, unsafe products, bribery, and ethical misconduct in business and politics. Focusing on frequent purchasers, the research assesses consumer ethics in shopping behaviors. While prior studies suggest some Turkish consumers engage in unethical practices, this study reveals a heightened sensitivity toward unethical behavior among respondents. Given Turkey’s status as a Muslim-majority country, the influence of Islamic ethical principles on consumption patterns warrants deeper investigation. The findings contribute to understanding how cultural and religious values shape consumer ethics in emerging markets, offering insights for policymakers and businesses aiming to promote ethical consumption.

Controlling corporate governance decision-making RISK is one of the prime safeguarding directives in the PCAOB’s requirement that the financials of Market Trading Firms [MTFs] are reasonably free from material error and thus are reliable indications of the results of operations. In this Corporate Governance Risk [CGR] context, it is important to understand the Interacting- & Integrated-Systemic Components of CGR and how they have evolved over time. We examine these aspects from Circa 1985 to date. We argue that the Driving Control System is: The Firm’s decision-making system of Internal Control over Financial Reporting [ICoFR] that impacts the Nature of CGR—to wit, Increasing CGRisk results as management experiences Increasing difficulties in maintaining adequate ICoFR. We examine the historical development of these systemic components and how they have spawned an Economic Sector of Firms that Rate & Score MTFs on the nature of their CGR profile, one currently predominate aspect of which is: The Nature of the MTF’s Caretaker Persona. With this as context, we present a duel-effect inferential design that examines the reliability of the Bloomberg™ Market Navigation Platform’s Institutional Shareholder Services™ [ISS]-Profiler as an information-link to be used in selecting MTFs to create CGR-sensitive Investment Portfolios. Our inferential results suggest for the MTFs in the ISS-Groups [{1: Lowest CGR} & {5: Mid-Level CGR} & {10: Highest CGR]}, it is likely that: (i) The decisions made by all involved in Management’s System of ICoFR, in its current COSO-version, are reflective of these MTF’s Caretaker Personas, (ii) The effects of these Caretaker Personas are analytically perceptible as reflected in the MTF’s Financials, and (iii) are aligned with the expected Nature of the ISS-Groups.

This study examines the ethical beliefs of Turkish consumers using a modified version of the Muncy-Vitell Consumer Ethics Scale (CES). Turkey’s ethical climate faces challenges such as copyright violations, environmental pollution, deceptive advertising, unsafe products, bribery, and ethical misconduct in business and politics. Focusing on frequent purchasers, the research assesses consumer ethics in shopping behaviors. While prior studies suggest some Turkish consumers engage in unethical practices, this study reveals a heightened sensitivity toward unethical behavior among respondents. Given Turkey’s status as a Muslim-majority country, the influence of Islamic ethical principles on consumption patterns warrants deeper investigation. The findings contribute to understanding how cultural and religious values shape consumer ethics in emerging markets, offering insights for policymakers and businesses aiming to promote ethical consumption.

Controlling corporate governance decision-making RISK is one of the prime safeguarding directives in the PCAOB’s requirement that the financials of Market Trading Firms [MTFs] are reasonably free from material error and thus are reliable indications of the results of operations. In this Corporate Governance Risk [CGR] context, it is important to understand the Interacting- & Integrated-Systemic Components of CGR and how they have evolved over time. We examine these aspects from Circa 1985 to date. We argue that the Driving Control System is: The Firm’s decision-making system of Internal Control over Financial Reporting [ICoFR] that impacts the Nature of CGR—to wit, Increasing CGRisk results as management experiences Increasing difficulties in maintaining adequate ICoFR. We examine the historical development of these systemic components and how they have spawned an Economic Sector of Firms that Rate & Score MTFs on the nature of their CGR profile, one currently predominate aspect of which is: The Nature of the MTF’s Caretaker Persona. With this as context, we present a duel-effect inferential design that examines the reliability of the Bloomberg™ Market Navigation Platform’s Institutional Shareholder Services™ [ISS]-Profiler as an information-link to be used in selecting MTFs to create CGR-sensitive Investment Portfolios. Our inferential results suggest for the MTFs in the ISS-Groups [{1: Lowest CGR} & {5: Mid-Level CGR} & {10: Highest CGR]}, it is likely that: (i) The decisions made by all involved in Management’s System of ICoFR, in its current COSO-version, are reflective of these MTF’s Caretaker Personas, (ii) The effects of these Caretaker Personas are analytically perceptible as reflected in the MTF’s Financials, and (iii) are aligned with the expected Nature of the ISS-Groups.

The fraudulent claims for Unemployment Insurance (UI) have also risen massively in the United States especially during the onset of COVID-19 pandemic with billions of dollars that were lost. These approaches applied formerly in fraud detection and prevention have been challenged by new and advanced fraud systems. For this reason, AI, Big Data and Predictive Analytics are now crucial for improving fraud mitigation in UI programs. The aim of this research is to understand how far AI, Big Data and Predictive Analytics have been utilized, for how effective they are and the barriers they pose in tackling unemployment insurance fraud in the United States. An online quantitative self-administered questionnaire was administered where 200 participants from the unemployment insurance agencies of the U.S, covering fraud investigators, IT personnel, data analysts and policymakers, were included in the study. Data were analyzed to establish the correlation between technology adoption to application of anti-fraud measures using descriptive statistics, chi-square test and regression analysis. The survey shows that about 42.5% of agencies actively use AI and 34% of them use Big Data very often. However, difficulties like high costs, integration troubles and limited availability of specialized staff are still issues that make adoption a problem. The results of using AI and Big Data were moderate and optimistic but rather elevated, as seen through regression analysis; on the other hand, predictive analytics demonstrated large potential when applied successfully. The study’s results indicate that AI, Big Data and Predictive Analytics present enormous potential for mitigating UI fraud, although broad integration will not be possible without overcoming essential infrastructural and resource limitations.

Cryptocurrencies are attracting considerable attention around the world because of the various advantages that they offer. On the other hand, they also carry some inherent risks. Although monetary authorities broadly agree that cryptocurrencies do not engender an immediate threat to national and global financial systems, the future is full of unknowns. In this regard, drawing a framework based on the current drivers of demand for cryptocurrencies would help visualize the prospects for these assets and create a roadmap to avoid or manage any disruptive risks. This discussion paper aims to contribute to the literature by examining the key factors that will determine the future performance of cryptocurrencies. The main conclusion derived from the discussion is that national regulations will potentially affect the direction of cryptocurrencies, as well as the need for any special efforts in the domain of monetary policy