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Introduction

AI is poised to become a multi-billion contributor to industry revenues (McKinsey & Co, 2024). AI application in financial services marketing includes chatbots and virtual assistants, customer relationship management, fraud detection, personalized banking, and analytics (Riikkinen et al., 2018). The context of financial services is distinctive as solutions that use AI, big data analytics and blockchain technologies (Mogaji et al., 2020; Mogaji et al., 2022). The rapid implementation of AI in banking poses new theoretical and managerial challenges (Bussmann et al., 2020). Despite the overwhelming positive outlook towards adoption of AI in financial and banking services, there is limited understanding of the trickle-down effects of this new technology on the already vulnerable and marginalized population groups, particularly in North American and European context. Researchers and practitioners are raising concerns regarding the potential impact of AI on vulnerable audiences. For example, a recent report identifies the potential impact of generative AI in widening the racial wealth divide in the U.S. by $ 43 billion each year (Brown et al., 2024). The Consumer Financial Protection Bureau (CFPB) has identified that AI biases are leading to “digital redlining” and “robot discrimination” (CFPB, 2023). In the marketing literature (Akter et al., 2021; Akter et al., 2022) identify several issues of algorithmic bias against population groups based on several factors such as gender, race, religion, etc. While AI adoption is becoming integral in fraud detection, the lack of regulatory oversight can amplify biases against marginalized groups. Taken together, there is a need for more research directed towards theoretical development on understanding the impact of AI biases for marginalized population groups or extending existing theoretical perspectives. In addition, AI biases can have far-reaching consequences on other stakeholders as well.
Given the above issues, a deeper understanding of AI biases and its impact on population groups is needed. Identifying solutions towards limiting these biases while improving productivity in the financial services sector would enable organizations to responsibly adapt and implement AI technologies.

List of Topic Areas

• The transformative role of AI in addressing potential biases in the banking and financial services sector
• Ethical AI implementation in financial institutions (both internal and external focused processes)
• The role of AI in financial services to improve customer well-being
• Role of top and middle level management in responsible AI implementation in the financial services
• The impact of AI-based algorithms on the use of nonbanking/subprime financial services such as mortgage lending, payday lending, Buy-Now-Pay-Later (BNPL) services, etc.
• The interaction of financial services firms, policymakers and other stakeholders in the context of AI
  Additional topics dealing with AI-biases within the financial services sector are welcome We welcome empirical research using either qualitative or quantitative approaches.

Submissions Information

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Submitted articles must not have been previously published, nor should they be under consideration for publication anywhere else, while under review for this journal.

Key Deadlines

Opening date for manuscripts submissions:��03/03/2025��
Closing date for manuscripts submission:��31/10/2025

Guest Editors

Pramod Iyer, Kennesaw State University, USA,��pramod.iyer@kennesaw.edu
Nik Nikolov, Kennesaw State University, USA,��nik.nikolov@kennesaw.edu
Ania Izabela Rynarzewska, Georgia College and State University, USA,��ania.rynarzewska@gcsu.edu

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