Based on a systematic review of influential publications among 402 papers published between 2010 and 2018, this paper identifies gaps in Economics and Finance research regarding two applications of FinTech: crowdfunding and blockchain. Analysing these records shows that (i) current research on FinTech is fragmented with limited theoretical grounding; (ii) crowdfunding and blockchain can be regarded as two innovations that may disrupt traditional financial intermediation but in different ways; (iii) crowdfunding platforms substitute for traditional financial intermediaries and serve as a new intermediary, without eliminating the need for intermediation; (iv) similar to crowdfunding, blockchain also creates new intermediaries; and (v) the trust element inherent in blockchain enables blockchain to eliminate the need for intermediaries in some financial areas but not all.

Although double‐entry accounting has been used for more than 600 years, today’s era of disruptive technological change utilising blockchain and FinTech has led to the emergence of another promising accounting method: triple‐entry accounting. This paper explores triple‐entry accounting, from its conception to the current state of play, using three case studies. We find that: (i) in a blockchain ecosystem, for some accounts, business entities will only need to perform a single entry internally and the opposite entry will be recorded in a public shared ledger; and (ii) triple‐entry accounting is a new and a more efficient way to address fundamental trust and transparency issues that plague current accounting systems. Triple‐entry accounting with blockchain, when properly implemented, can fundamentally improve accounting.

In this paper, we focus on the question to what extent machine learning (ML) tools can be used to support systematic literature reviews. We apply a ML approach for topic detection to analyze emerging topics in the literature-our context is accounting and finance research in the Asia-Pacific region. To evaluate the robustness of the approach, we compare findings from the automated ML approach with the results from a manual analysis of the literature. The automated approach uses a keyword algorithm detection mechanism whereby the manual analysis uses common techniques for qualitative data analysis, that is, triangulation between researchers (expert judgement). From our paper, we conclude that both methods have strengths and weaknesses. The automated analysis works well for large corpora of text and provides a very standardized and non-biased way of analyzing the literature. However, the human researcher is potentially better equipped to evaluate current issues and future trends in the literature. Overall, the best results might be achieved when a variety of tools are used together.