DARQ technologies in the financial sector: artificial intelligence applications in personalized banking

Abstract

Purpose

This paper aims to analyze the impact of DARQ technologies (distributed ledger, artificial intelligence, extended reality, quantum computing) in the financial sector, focusing on artificial intelligence (AI) applications in personalized banking, which consists of treating every customer as a segment of one. The research has two main goals. First, providing a complete and organic analysis of the DARQ technologies framework currently missing in the literature. Because this research focuses on the financial sector, more attention is dedicated to DARQ technologies in this industry. Second, studying applications of one of the DARQ technologies, AI, in personalized banking, where it appears to have a great potential impact.

Design/methodology/approach

The research analyses both the supply side, collecting secondary data from documentation, reports and research studies to study the major trends and results obtained by leading banks, and on the demand side, collecting primary data through a dedicated survey and elaborating opinions and preferences of potential customers. Using this information, a detailed go-to-market plan based on the framework elaborated by Bain and Co. in 2012 is developed, considering the hypothesis of a well-known universal bank, operating globally, with an established brand and access to modern AI technologies, which decides to invest in this field as a priority.

Findings

In addition to giving a detailed overview of DARQ technologies from a technical and a business perspective, the results related to the hypothetical case of the study help to understand which would be the most suitable target for the launch phase, which value proposition should be offered and how to deliver it, but also how to evolve the project to attract more customers and strengthen the relationship with the existing ones. Nevertheless, this research could be a starting point for future studies and updates, considering related evolutions, investigating more representative demand samples or analyzing how the combination of more DARQ technologies could be applied to the financial sector.

Research limitations/implications

Some limitations affect this work. First, the topics studied are evolving rapidly and partially dependent on other innovations under development; therefore, they may become obsolete and less significant in the next years. As regard the data collection, the supply-side analysis involves strategical information kept private by companies; therefore, the collected data probably miss some useful details. As concerns primary data, the sample could have been larger and more heterogeneous and biases and misinterpretations could have affected the answers. A compromise has been found between the time and resources available and the qualitative and quantitative characteristics of the sample.

Practical implications

This research could be a tool for financial companies interested in investing in AI for personalized banking, but it also provides useful insights about the whole DARQ framework, which could be interesting for all the financial and nonfinancial firms. Applying AI effectively and efficiently could offer great benefits, both economic and noneconomic, to financial firms but also to their customers, who could benefit from hyper-tailored services at a reasonable and affordable price, whereas in the past, they were reserved only for very important person clients. This win-win situation could lead the way to further investments and consequent innovations in the future.

Social implications

Some issues still exist, mainly about data security and privacy, but also the social risk linked to the labor market due to the AI substitution for some tasks and the related shift in professionals required by employers, which could negatively affect the salary gap among workers with different levels of educations, tightening up existing inequality problems. An effort by public and private subjects will be required to make this transition inside the labor market smoother. Despite this, the research shows that AI applications in personalized m-banking could mutually benefit both the demand and the supply of the market.

Originality/value

Apart from the organic overview offered on DARQ technologies and their related business applications, currently missing in the literature, which could be useful for a better comprehension of the topic and could also give interesting insights to firms, this research presents an original and concrete roadmap to follow for financial companies interested in delivering a personalized mobile banking service leveraging on AI. Every step presented in the output of this work is based on an in-depth analysis of past, and present actions carried out and result obtained by competitive firms on the market and on needs and preferences observed among potential customers.

Citation

Gigante, G. and Zago, A. (2023), "DARQ technologies in the financial sector: artificial intelligence applications in personalized banking", Qualitative Research in Financial Markets, Vol. 15 No. 1, pp. 29-57. https://doi.org/10.1108/QRFM-02-2021-0025