Systematic literature reviews in software engineering as well as other disciplines, serve as the foundation for sound scientific research. The aim for these literature reviews is to aggregate all existing knowledge on a research problem and produce informed guidelines for practitioners. This enables practitioners to apply appropriate software engineering solutions in a specific contexts. However, one major problem exists regarding systematic literature reviews, the overall execution duration may take up as much as 24 months.
The first objective of this study is to provide a solid base for the AI for FinTech Research collaboration by performing a systematic literature review. This literature review is used to identify different machine learning techniques in the context of the FinTech domain. However, during this study, we found that a significant amount of time was spent on repetitive work which potentially could have been automated. Therefore, the second objective of this work is to reduce the overall workload for performing systematic literature reviews. First, a literature review is performed regarding automation solutions for different steps of systematic literature reviews. The identified solutions were used to create a tool to automate steps in both the retrieval and screening phase of systematic literature reviews.
First, this work presented the state of the art regarding machine learning applications in the FinTech domain. Afterwards, a complete overview of possible automation solutions for every step of performing literature reviews was detailed. Using this overview, a tool was created which showed that the overall workload of the retrieval and screening phase of systematic literature reviews can be significantly reduced.
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