Artificial intelligence in customer-facing financial services: a systematic literature review and agenda for future research

Abstract

PurposeThe objective of this study is to provide a systematic review of the literature on artificial intelligence (AI) in customer-facing financial services, providing an overview of explored contexts and research foci, identifying gaps in the literature and setting a comprehensive agenda for future research.Design/methodology/approachCombining database (i.e. Scopus, Web of Science, EBSCO, ScienceDirect) and manual journal search, the authors identify 90 articles published in Australian Business Deans Council (ABDC) journals for investigation, using the TCCM (Theory, Context, Characteristics and Methodology) framework.FindingsThe results indicate a split between data-driven and theory-driven research, with most studies either adopting an experimental research design focused on testing the accuracy and performance of AI algorithms to assist with credit scoring or investigating AI consumer adoption behaviors in a banking context. The authors call for more research building overarching theories or extending existing theoretical perspectives, such as actor networks. More empirical research is required, especially focusing on consumers' financial behaviors as well as the role of regulation, ethics and policy concerned with AI in financial service contexts, such as insurance or pensions.Research limitations/implicationsThe review focuses on AI in customer-facing financial services. Future work may want to investigate back-office and operations contexts.Originality/valueThe authors are the first to systematically synthesize the literature on the use of AI in customer-facing financial services, offering a valuable agenda for future research.

Feeling the love? How consumer's political ideology shapes responses to AI financial service delivery

“…Previous research on IJBM 40,6 AI in financial services has largely examined technologies such as chatbots (Adam et al, 2021;Eren, 2021), robo-advisors (Belanche et al, 2019;Bhatia et al, 2020) and AI genetic programming (Aloud, 2017) as can be seen in Table 1. These studies provide interesting insights as to the effect of AI on consumers in service encounters, however, as suggested by Hentzen et al (2021), greater understanding is still required to examine the comprehensive effect of AI on consumers and their responses in financial service settings. Next, emotions and trust are suggested as explanatory responses (mediators) which explain how consumers react and evaluate marketing outcomes when provided financial services by AI.…”

“…(2021) conducted a systematic review of previous research on AI in customer-facing financial services and included 90 papers in the systematic review. It was identified that there had been a significant increase in research since 2018 with 30% of articles focusing on banking and 27% focusing on financial advice and investment (Hentzen et al. , 2021).…”

“…For example, Hentzen et al (2021) conducted a systematic review of previous research on AI in customer-facing financial services and included 90 papers in the systematic review. It was identified that there had been a significant increase in research since 2018 with 30% of articles focusing on banking and 27% focusing on financial advice and investment (Hentzen et al, 2021). Previous research on IJBM 40,6 AI in financial services has largely examined technologies such as chatbots (Adam et al, 2021;Eren, 2021), robo-advisors (Belanche et al, 2019;Bhatia et al, 2020) and AI genetic programming (Aloud, 2017) as can be seen in Table 1.…”

“…Research investigating AI use in financial services is growing. For example, Hentzen et al. (2021) conducted a systematic review of previous research on AI in customer-facing financial services and included 90 papers in the systematic review.…”

Guest editorial: Artificial intelligence in financial services marketing

“…This special issue includes nine articles, and it is structured as follows: (1) the intersect of AI and political ideology in financial services marketing (Riedel et al , 2022; Cui, 2022), (2) financial robo-advisors (Bouhia et al , 2022; Northey et al , 2022), (3) developing AI for financial services marketing in emerging countries (Ghazwani et al , 2022; Omoge et al , 2022; Mogaji et al , 2021; Sheth et al , 2022) and (4) new theoretical and empirical approaches (Hentzen et al , 2021). Papers in each theme are subsequently discussed.…”