Artificial Intelligence in Financial Services Marketing
Call for papers for: International Journal of Bank Marketing
Artificial intelligence (AI) has permeated our lives; it is no longer fictional as many of us unknowingly interact with it every day (Dwivedi, et al., 2019). AI is considered a system that can replicate human's cognitive functions such as learning, speech and problem solving (Russell & Norvig, 2016). Xu et al (2020) further described AI in a financial services context as a technology-enabled system for evaluating real-time service scenarios using data collected from digital and/or physical sources in order to provide personalised recommendations, alternatives, and solutions to customers’ enquiries or problems, even very complex ones (p. 189). These systems have integrated computer programs that allow them to solve problems that may be difficult for humans to comprehend (Gupta and Arora, 2017).
AI has expanded rapidly into many sectors, disrupting service provision, manufacturing, and product design. There are greater levels of adoption within Manufacturing, Supply Chain, Robotics, Healthcare, Digital Imaging, Education, and Government (Nishant, Kennedy, and Corbett, 2020) and their role is becoming much more profound and impacting what we buy, news we consume and how we are cared for (Rai, 2020). While there are benefits of this adoption, the concept of AI is not yet fully understood by society, especially regarding its ethical and economic implications and its broader impact on human life and culture (Duan, et al., 2019). While these implications are relevant, there will be winners and losers and that decision-makers need to be strategic in their outlook for the future (Dwivedi et al. 2019). This has raised concerns to theoretically explore the impact of AI in specific areas, for example, AI and Health Science (De Cos, et al., 2020), AI-supported education (Barnes, et al., 2017) Ethics in AI (Dignum, 2018) and AI as a transformative force within marketing (Bakpayev et al., 2020).
AI technologies have been incorporated into marketing where big data has been used in developing personalised profiles of customers (Payne, Peltier, and Barger, 2021), predicting consumer demand and developing advertisement (Mogaji, et al., 2020). Many financial service providers are, therefore adopting AI to enhance their business operations (Arli et al., 2020). AI application in financial services includes chatbots and virtual assistants, underwriting and lending decision, relationship manager augmentation, fraud detection, personalised banking, process automation, credit score and analytics (Riikkinen et al., 2018). The context of financial services is distinctive as solutions that use big data analytics, AI and blockchain technologies are introduced at an unprecedented rate posing new theoretical and managerial challenges (Giudici, et al., 2019). These challenges involve the financial services providers, policymakers and consumers.
Recently, when looking at the intersect of AI and financial services, scholars and practitioners alike have identified particular challenge. First, the adaptiveness of the Bank. With vast data, it is becoming imperative for financial services providers better to understand their customer needs, attitudes, and preferences and use this information to develop relevant financial services and enhance service delivery. To understand this structure, business and technology architecture is required to support data engineering and data governance in order to support multiple AI components with different ecosystem conventions (Dwivedi, et al., 2019). As part of the banks' ability to adapt, human’s role in working effectively with AI is important (Mogaji, et al., 2020).
Second, Policy and Regulations in a highly regulated market. Financial services is a highly regulated sector, and technology's impact on the customers and services provider is essential. This has implications of information acquisition and information analysis (Dwivedi, et al., 2019). While the financial services providers may be keen on collecting data to run and teach their AI system, there are concerns around AI policies, governance and the control over data.
Next, is the consumers' ethical consideration. There are concerns around ethical data collection, discrimination and inherent biases in the algorithm against certain gender and race (Mogaji, et al., 2020). It becomes essential for financial services providers to be accountable and explore to what extent is their algorithm safe based on different data and ecosystem conventions.
Given these challenges, in this special issue, we invite the submission of original manuscripts that advance our knowledge of how AI can be leveraged to enhance financial services development, dissemination and delivery. To understand how the vast amount of data collected can inform and develop new financial services, how these services are being marketed and effectively delivered.
The objectives of this special issue include:
- Discover philosophical, theoretical, or methodological insight into where Artificial Intelligence and Financial Marketing intersect
- Create insight into how customers are engaging and interacting with intelligent systems
- Give stakeholders with an overview and applications of AI and its related technologies in financial services development, provision, and marketing.
- To understand the ethical implications when developing algorithms which may affect financial services development, provision, and marketing.
To achieve these objectives, we seek high-quality contributions from multiple perspectives, including marketing, information systems, industry practitioners, including FinTech developers, and sociology. Methodologically, we embrace various methods, including surveys, applied research, field experiments, quantitative research, secondary data analytics, market research studies, and qualitative research (among others). All approaches (empirical, analytical, or conceptual) that create novel financial services marketing insights by AI are welcome.
The list of possible topics for this special issue includes, but is not limited to:
- AI-enabled digital marketing to financial services
- Optimisation of customer journey through AI
- AI and Robotics in financial services automation
- AI for fraud detection
- AI for financial decisions
- AI and ethical, moral, and societal challenges in financial services provision
- Implications of algorithmic bias in Financial Services.
- Consumer engagement with AI (Human-Robot Interaction)
- AI-enabled chatbot to drive marketing automation
- Financial Services Employees engagement with and attitude towards AI.
- Marketing practitioners' skills and knowledge requirement for AI adoption in marketing financial services.
- Data and Web Mining for financial services development, provision, and marketing.
- Design of AI/robot solutions to improve customer experience and customer relationship management.
Please review the journal author guidelines prior to submission at www.emeraldinsight.com/ijbm.htm Submit your paper at https://mc.manuscriptcentral.com/ijbm and please be sure to select this special issue from the drop down menu provided during submission. The deadline for submissions is 30 September 2021,
All queries about the special issue should be sent to the Guest Editors:
University of Greenwich
Solent University, Southampton, UK
University of KwaZulu Natal, Durban, South Africa
Patrick Van Esch
Auckland University of Technology, Auckland, New Zealand
Rodrigo Perez Vega
Kent Business School
Arli, D., van Esch, P., Bakpayev, M., & Laurence, A. (2020). Do consumers really trust cryptocurrencies?. Marketing Intelligence & Planning.
Bakpayev, M., Baek, T. H., van Esch, P., & Yoon, S. (2020). Programmatic creative: AI can think but it cannot feel. Australasian Marketing Journal (AMJ).
Barnes, T. et al., 2017. Preface for the special issue on AI-supported education in computer science. International Journal of Artificial Intelligence in Education, 27(1), pp. 1-4.
De Cos, F., Woźniak, M., Méndez, J. & Flecha, J., 2020. Special issue SOCO 2017: AI and ML applied to Health Sciences (MLHS). Neural Computing and Applications, Volume 32, p. 1217–1218.
Dignum, V., 2018. Ethics in artificial intelligence: introduction to the special issue. Ethics and Information Technology, Volume 20, pp. 1-3.
Duan, Y., Edwards, J. & Dwivedi, Y., 2019. Artificial intelligence for decision making in the era of big data – evolution, challenges and research agenda. International Journal of Information Management, Volume 48, pp. 63-71.
Dwivedi, Y. K. et al., 2019. Artificial Intelligence (AI): Multidisciplinary perspectives on emerging challenges, opportunities, and agenda for research, practice and policy. International Journal of Information Management.
Giudici, P. et al., 2019. AI and financial technology. Frontiers in Artificial Intelligence, Volume 2, p. 25.
Gupta, A., & Arora, N. (2017). Consumer adoption of m-banking: a behavioral reasoning theory perspective. International Journal of Bank Marketing.
Mogaji, E., Olaleye, S. & Ukpabi, D., 2020. Using AI to Personalise Emotionally Appealing Advertisement. In: R. N. e. al., ed. Digital and Social Media Marketing. Advances in Theory and Practice of Emerging Markets. Cham: Springer, pp. 137-150.
Mogaji, E., Soetan, T. & Kieu, T., 2020. The implications of artificial intelligence on the digital marketing of financial services to vulnerable customers. Australasian Marketing Journal.
Nishant, R., Kennedy, M., & Corbett, J. (2020). Artificial intelligence for sustainability: Challenges, opportunities, and a research agenda. International Journal of Information Management, 53, 102104.
Payne, E. H. M., Peltier, J., & Barger, V. A. (2021). Enhancing the value co-creation process: artificial intelligence and mobile banking service platforms. Journal of Research in Interactive Marketing.
Rai, A., 2020. Explainable AI: From black box to glass box. Journal of the Academy of Marketing Science, 48(1), pp. 137-141.
Riikkinen, M., Saarijärvi, H., Sarlin, P., & Lähteenmäki, I. (2018). Using artificial intelligence to create value in insurance. International Journal of Bank Marketing.
Russell, S. & Norvig, P., 2016. Artificial intelligence: a modern approach. Malaysia: Pearson Education Limited.
Xu, Y., Shieh, C. H., van Esch, P., & Ling, I. L. (2020). AI customer service: Task complexity, problem-solving ability, and usage intention. Australasian Marketing Journal (AMJ), 28(4), 189-199.