Purpose – The internet provides a mechanism by which buyers and sellers meet in order to exchange goods and services online with the utmost convenience. However, there are many risks associated with the internet which, if left unattended, could continue deterring the adoption of e-commerce. These risks ultimately diminish online consumer trust in e-commerce. Web assurance models have been designed in an attempt to encourage online consumer trust through assurance. Unfortunately, many of these models have been inadequate in certain areas and this research aims to improve on them. Design/methodology/approach – It presents a comprehensive empirical survey on trustworthiness issues and e-commerce assurance models and proposes a new compliance-based e-commerce assurance model that integrates adaptive legislation, adaptive e-commerce-related standards and cooperative rating. The intelligent cooperative rating is based on the analytic hierarchy process and page-ranking techniques. Findings – Some findings of this research study influence the thinking that some of the untrustworthy sites are posing as trustworthy sites because they display web seals. The findings can be used as a reference guide to understand e-commerce assurance models, as well as the effectiveness of ensuring the trustworthiness of these models. Practical implications – The research presents deployment analysis on the use of the proposed compliance model through real life scenarios categorized as trustworthy and untrustworthy e-commerce web sites. Originality/value – This research is relevant to information management and computer security in e-commerce as a development of a newly proposed e-commerce assurance model for trustworthiness safety inspections and knowledge generation as a reference guide to understand e-commerce trustworthiness in general and e-commerce assurance models in particular detail.

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Machine learning (ML) is another branch of technology deemed valuable in the financial sector because of its ability to assist organisations in identifying fraudulent transactions and predicting the ability of customers to repay their bank-issued loans. However, like any type of technology, the adoption of ML introduces changes that impact the processes and operations of the financial service sector. Research on the merits of implementing ML is well captured; however, research on such developments' challenges, issues, and impact is scant. To address this gap, a systematic literature review was undertaken to contribute to the research discourse by investigating the issues, challenges and impacts of implementing ML in the financial business sector. The ScienceDirect, EBSCOhost and ProQuest databases were used to search for the relevant scholarly sources published from 2013-2022. The literature was reviewed based on the PRISMA flow diagram and a thematic analysis of the 35 articles that met the inclusion criteria. The outcome of the review revealed that more complex models, such as artificial neural networks, were implemented in all the identified financial services sectors, followed by support vector machines. This review concludes that the larger the quantity and complexity of financial data, the less the data quality, which significantly reduces the prediction performance, efficiency, and accuracy of the model, which can significantly impact the operations, financial aspects, and the overall reputation of the firms. Future research must explore the impact of ML on the operational, adoption and skills shortages in the financial sector.

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