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The impact of Goods and Service Tax (GST) on the motor and pump exports in the Coimbatore region is measured in this study using various parameters and scales. The data collected from exporters were used to identify the pros and cons of GST, stating their opinions on variables considered by the researchers through extensive literature on GST and exports.

The data were collected from 220 motors and pump exporters through a field survey from the month of January to September 2021. The impacts have been measured using principal component analysis (PCA) and confirmatory factor analysis (CFA). The factor analyses and CFA will derive the positive and negative impact of GST determined through critical empirical evidence in this study. Also, the problems faced by the exporters allied to GST, which the authors could not include in the questionnaire due to certain reasons, are concisely apportioned and discussed.

The study depicts the major advantages of GST, such as harmonized system, long-run performance, reduction in logistics cost, check-post operation, bonds and ease of doing business. Also, it highlights the disadvantages of GST, such as biases in the indirect tax system, the reimbursement of duty drawback being late or pending and document filing was still a tedious job under the GST regime.

The unavailability of considerable literature on the impact of GST on Indian exports signifies the novelty of this research. So far, this is the first empirical attempt to measure the impact of GST on exports which is a unique and original attempt to highlight the problem that lies under the GST regime and the necessary reforms the tax structure needs in the context of Indian exports.

This study aims to investigate the feasibility of employing a multi-objectives integer-programming model for effective allocation of resources for cyberfraud mitigation. The formulated objectives are the minimisation of the total allocation cost of the anti-fraud capacities and the maximisation of the forensic accounting capacities in all cyberfraud incident prone spots.

From the literature survey conducted and primary qualitative data gathered from the 17 licenced banks in South Africa on fraud investigators, the suggested fraud investigators are the organisation’s finance department, the internal audit committee, the external risk manager, accountants and forensic accountants. These five human resource capacities were considered for the formulation of the multi-objectives integer programming (MOIP) model. The MOIP model is employed for the optimisation of the employed capacities for cyberfraud mitigation to ensure the effective allocation and utilisation of human resources. Thus, the MOIP model is validated by a genetic algorithm (GA) solver to obtain the Pareto-optimum solution without the violation of the identified constraints.

The formulated objective functions are optimised simultaneously. The Pareto front for the two objectives of the MOIP model comprises the set of optimal solutions, which are not dominated by any other feasible solution. These are the feasible choices, which indicate the suitability of the MOIP to achieve the set objectives.

The results obtained indicate the feasibility of simultaneously achieving the minimisation of the total allocation cost of the anti-fraud capacities, or the maximisation of the forensic accounting capacities in all cyberfraud incident prone spots – or the trade-off between them, if they cannot be reached simultaneously. This study recommends the use of an iterative MOIP framework for decision-makers which may aid decision-making with respect to the allocation and utilisation of human resources.

The originality of this work lies in the development of multi-objectives integer-programming model for effective allocation of resources for cyberfraud mitigation.