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Technology advancement has changed how banks operate. Modernizing technology has, on the one hand, made it simpler for banks to do their daily business, but it has also increased cyberattacks. The purpose of the study is to to determine the factors that have the most effects on online fraud detection and to evaluate the advantages of AI and human psychology research in preventing online transaction fraud. Artificial intelligence has been used to create new techniques for both detecting and preventing cybercrimes. Fraud has also been facilitated in some organizations via employee participation.

The main objective of the research approach is to guide the researcher at every stage to realize the main objectives of the study. This quantitative study used a survey-based methodology. Because it allows for both unbiased analysis of the relationship between components and prediction, a quantitative approach was adopted. The study of the body of literature, the design of research questions and the development of instruments and procedures for data collection, analysis and modeling are all part of the research process. The study evaluated the data using Matlab and a structured model analysis method. For reliability analysis and descriptive statistics, IBM SPSS Statistics was used. Reliability and validity were assessed using the measurement model, and the postulated relationship was investigated using the structural model.

There is a risk in scaling at a fast pace, 3D secure is used payer authentication has a maximum mean of 3.830 with SD of 0.7587 and 0.7638, and (CE2).

This study focused on investigating the benefits of artificial intelligence and human personality study in online transaction fraud and to determine the factors that affect something most strongly on online fraud detection. Artificial intelligence and human personality in the Indian banking industry have been emphasized by the current research. The study revealed the benefits of artificial intelligence and human personality like awareness, subjective norms, faster and more efficient detection and cost-effectiveness significantly impact (accept) online fraud detection in the Indian banking industry. Also, security measures and better prediction do not significantly impact (reject) online fraud detection in the Indian banking industry.

Driven by the Sustainable Development Goals (goals 7, 8, 12 and 13), this study investigates the moderating role of financial development in the link between energy poverty and a sustainable environment in African nations.

Panel cointegration analysis, fully modified least squares, Driscoll and Kraay least squares and method of moments quantile regression were used as estimation techniques to examine the link between financial development, energy poverty and sustainable environment for 28 African nations. Energy poverty is measured using two proxies-access to clean energy and access to electricity, while the environment is gauged using ecological footprint.

The regression outcomes show that access to clean energy and electricity negatively impacts the ecological footprint across all the quantiles; hence, energy poverty increases environmental degradation. Financial development positively influences environmental degradation in the region at the upper quantiles. Similarly, the interactive term of energy poverty and financial development has a significant positive impact on ecological footprint; thus, the financial sector adds to energy poverty and environmental degradation. The results of other variables hint that per capita income and institutions worsen environmental quality while urbanisation strengthens the environment.

This study offers fresh insights into the moderating effect of financial development in the link between energy poverty and sustainable environment in African countries.