The use of financial technologies (Fintech) has increased recently due to their support to financial institutions in managing their financial operations and achieving competitive advantages. Even though there are several benefits with Fintech development and implementation, selecting the most suitable Fintech project can be complex. This is due to the involvement of numerous decision makers, the conflicting nature of multiple evaluation criteria, and fuzzy data derived from imprecise judgments of qualitative performance ratings. Interval-valued based intuitionistic fuzzy numbers are used to deal with the inherent vagueness and imprecision of the evaluation process. An algorithm based on an interval-valued intuitionistic fuzzy weighted geometric (IIFWG) and the concept of ideal solutions is developed. As a result, the most suitable Fintech project alternative can be selected across all evaluation criteria. To demonstrate the effectiveness of the approach, a Fintech project selection problem is presented.

Big Data can help users attain a competitive advantage, and evidence suggests that by utilising Big Data, organisations can generate insight that can help strengthen their decision-making capabilities. However, a key issue remains that much data is trapped in legacy systems, and is hence not being appropriately retrieved and utilised. This paper builds on the existing literature base to investigate the challenges and issues organisations face in utilising Big Data. Through results of a survey with 97 respondents, this work shows that these issues can be categorised into six areas, including issues of format and structure of the data, as well as identification of the key need for a framework and architecture for organising Big Data.

Financial technologies (FinTech) is beneficial to financial institutions for offering a wide range of services to their customers. However, the rate of FinTech use in Australia is well below the average FinTech adoption rate among some of the developed nations. This is due to the fact that technology adoption can be challenging, and failures can lead to a loss of reputation and customer turnover. This study aims to explore the critical factors influencing the adoption of FinTech in organizations. The proposed study will adopt a mixed-method approach that involves data collection through online survey questionnaires and face-to-face interviews. The data will be analyzed and tested using the structural equation modeling technique. The results are expected to contribute to FinTech literature and offer insights into the key factors enabling FinTech adoption. The results will help Australian financial institutions to develop appropriate policies to enhance FinTech adoption.