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The change of production methods, the industrial revolutions, technological developments, and digital transformation have affected almost all functions in the enterprises. Accounting and auditing areas are also quite affected by this transformation. Another important result of technology and digitalization is the rapid increase in errors, frauds, and irregularities. Enterprises are looking for new solutions and investigations against irregularities and frauds. Audits for errors, frauds, or irregularities are among the interests of forensic accounting. Many methods are used to identify errors and frauds in the forensic accounting. However, it is inevitable that digital technologies should be utilized in forensic accounting applications as a result of the rapid spread of automation and computer programs in enterprises within the framework of digitalized business activities. Hence, enterprises will be able to get more effective results through computer programs and artificial intelligence in terms of fraud audit in forensic accounting. Expert system applications use artificial intelligence to enable computer programs to behave just like people. One of the most widely used, most easily applicable, and most understandable types of expert system is rule-based expert system. The aim of this study is to determine the accounting fraud that may occur in enterprises within the framework of forensic accounting through rule-based expert systems. For this purpose, various applications have been implemented in a large-scale production enterprise through the use of rule-based expert systems for the determination of accounting fraud. Benford’s Law, risk levels, and various other criteria were used in the creation of expert systems. According to the results obtained from the study, it has been seen that by means of rule-based expert system applications, enterprises can better detect existing frauds and prevent further irregularities in the future. The study is important and it is expected that the study will contribute to the literature because it is shown in the study that the rule-based expert systems, applied in many fields under the title of social sciences, can also be applied in the field of forensic accounting and auditing.

This study investigates the relationship among economic growth, energy usage, urbanization, trade openness, carbon emission, ecological footprint, and ecological carbon footprint with the sample of G-7 countries over the period 1991–2017. The Continuously-Updated and Fully-Modified (CUP-FM) estimator method applied to the panel series is utilized. As a result of empirical analysis, an increase in the real per capita GDP increases the ecological footprint by 0.844%. Also, there are positive impacts that are detrimental to the environment among the environmental quality and explanatory variables for each established model. The findings indicate that a relationship exists among the variables in the long run, and at the same time, carbon emission as an indicator of pollution should be noticed as well as the ecological footprint and ecological carbon footprint. It is observed that a positive relationship exists among the variables such as economic growth, energy usage, and environmental pollution indicator. In this context, G-7 countries indicate that, upon taking their developed economic structures and global pollution levels into account, the Environment Kuznets curve (EKC) hypothesis becomes valid, and that the U-shaped behavior is valid for the total ecological footprint. It is been seen that, besides economic growth and energy usage, other explanatory variables contribute to environmental degradation in trade openness. In compliance with these results, in terms of the sustainability of economic development, G-7 countries should pay attention to economic activities that would impair the environmental quality and control them with regulations.