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In recent years, the penetration of digital technologies in the financial industry determined the arising of Fintech, which generated a dynamic and rapid change that business operators and supervisory authorities in the banking industry are struggling to follow it. This is especially due to issues affecting financial intermediaries and customers, and potential risks of stability of the financial system. The aim of this paper is to provide a review of Fintech in the banking industry thus to update the knowledge about technology innovation in the banking sector, identify the major trends in the domain and delineate future research directions.

The study reviews 377 articles indexed on Scopus from 2014 to 2021 that focus on Fintech and the banking industry. The methodology adopted is structured in two steps: the keywords selection and the analysis of the documents extracted. The first step identified “Fintech” and “bank” as keywords to be searched within the title, abstract or keywords of documents indexed on Scopus; whereas the second step combined R and VOSviewer to provide a descriptive analysis of the dataset and the analysis of keywords and occurrences, respectively.

Results achieved in the study allow providing a systemic view of the Fintech in the banking industry, including the emergent phenomenon of digital banking. In particular, it is provided with a general overview and descriptive information on the entire sample of documents analyzed, their authors, the keywords used and the most cited works. Besides, a deepening on the model of digital banking is provided, by delineating the six dimensions of the key effects generated by the digital bank model.

Two main elements of originality characterize this study. The first one is related to the fact that few review studies have been published on Fintech in the banking industry, and the second one concerns the multiple dimensions of the impact of Fintech in the banking sector, which includes customer, company, bank, regulation authority and society.

This study explores challenges facing the applicability of deep learning (DL) in software-defined networks (SDN) based campus networks. The study intensively explains the automation problem that exists in traditional campus networks and how SDN and DL can provide mitigating solutions. It further highlights some challenges which need to be addressed in order to successfully implement SDN and DL in campus networks to make them better than traditional networks.

The study uses a systematic literature review. Studies on DL relevant to campus networks have been presented for different use cases. Their limitations are given out for further research.

Following the analysis of the selected studies, it showed that the availability of specific training datasets for campus networks, SDN and DL interfacing and integration in production networks are key issues that must be addressed to successfully deploy DL in SDN-enabled campus networks.

This study reports on challenges associated with implementation of SDN and DL models in campus networks. It contributes towards further thinking and architecting of proposed SDN-based DL solutions for campus networks. It highlights that single problem-based solutions are harder to implement and unlikely to be adopted in production networks.