Artificial Intelligence in Finance provides a platform to discuss the significant impact that financial data science innovations, such as big data analytics, artificial intelligence and blockchains have on financial processes and services, leading to data driven, technologically enabled financial innovations (fintechs, in short). Financial technologies are leading to new financial products and services that improve user experience and customer engagement, increase performance and reduce costs. In addition, fintechs can facilitate technology compliance and financial supervision, by means of through a common risk management framework. Leveraging on the digitalisation of financial products, financial technologies also allow more transparent and accessible services, within the context of a digital society. The section welcomes foundational and applied papers from a wide range of topics underpinning financial data science methods and financial technologies and explores emerging cross-disciplinary themes. It particularly encourages collaboration between universities and research centers, fintechs and financial companies, regulators and supervisors, building a common innovation ecosystem. Specifically, we welcome papers on:
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Artificial Intelligence in Finance welcomes submissions of the following article types: Code, Core Concept, Correction, Hypothesis and Theory, Methods, Mini Review, New Discovery, Opinion, Original Research, Perspective, Review, Specialty Grand Challenge and Technology Report.
All manuscripts must be submitted directly to the section Artificial Intelligence in Finance, where they are peer-reviewed by the Associate and Review Editors of the specialty section.
Articles published in the section Artificial Intelligence in Finance will benefit from the Frontiers impact and tiering system after online publication. Authors of published original research with the highest impact, as judged democratically by the readers, will be invited by the Chief Editor to write a Frontiers Focused Review - a tier-climbing article. This is referred to as "democratic tiering". The author selection is based on article impact analytics of original research published in all Frontiers specialty journals and sections. Focused Reviews are centered on the original discovery, place it into a broader context, and aim to address the wider community across all of Artificial Intelligence.
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