Big data technologies have a strong impact on different industries, starting from the last decade, which continues nowadays, with the tendency to become omnipresent. The financial sector, as most of the other sectors, concentrated their operating activities mostly on structured data investigation. However, with the support of big data technologies, information stored in diverse sources of semi-structured and unstructured data could be harvested. Recent research and practice indicate that such information can be interesting for the decision-making process. Questions about how and to what extent research on data mining in the financial sector has developed and which tools are used for these purposes remains largely unexplored. This study aims to answer three research questions: (i) What is the intellectual core of the field? (ii) Which techniques are used in the financial sector for textual mining, especially in the era of the Internet, big data, and social media? (iii) Which data sources are the most often used for text mining in the financial sector, and for which purposes? In order to answer these questions, a qualitative analysis of literature is carried out using a systematic literature review, citation and co-citation analysis.

In this chapter, a study about how Italian SMEs understand and use FinTech technologies is presented. The study focuses on FinTech-aided banking services, in particular, due to the fact that these are, at present, the most widely used FinTech technologies available in Italy. The study shows how, despite FinTech entering Italy only in recently, the Italian SMEs market is very active and fruitful for digital companies. In the last years, a continuous growth of investment has seen the development of FinTech technologies in multiple areas, such as mobile networks, big data, trust management, mobile embedded systems, cloud computing, image processing, and data analytic techniques.

M-Banking is developed to support the clients in using various banking services, by using their mobile phones, thus allowing them to overcome the barriers in terms of time and location. Clients are increasingly using m-banking, so for some of them, this is the most used way of communication with the bank and doing banking transactions. Therefore, high-quality m-banking services significantly impact trust towards the bank, and it can influence bank reputation. Given the influence of m-banking, as well as the importance of its perceived quality, the paper aims to investigate the elements of m-banking quality, and to analyze the relation between m-banking quality and bank reputation. We investigate several dimensions of m-banking (safety, simplicity, and variety of m-banking services), and their impact on perceived m-banking quality. Besides, we examine the effect of perceived m-banking quality to bank reputation. For the analysis of these relationships, we use structural equation modeling, based on the survey results on a sample of clients of major banks in Croatia. Results of empirical research indicate that safety, simplicity, and a variety of m-banking services have a significant impact on the perceived m-banking quality, which, in turn, has a positive impact on the bank’s reputation.

Ease of access to and low cost of hardware and software for 3D scanning have made 3D technologies increasingly popular in recent research. One of the possible 3D scanning approaches is photogrammetry which relies on using a data set consisting of photographs of the same physical object. In this paper are evaluated different 3D models generated from the same input data set by specialised software packages for photogrammetry. The main attributes of the 3D models are examined in comparative analyses and their differences highlighted. Furthermore, visual qualitative inspections are performed on the models and the results are compared.

Affiliations: 1: University of Maribor/University of Zagreb