AI-ENABLED CUSTOMER RELATIONSHIP MANAGEMENT IN THE FINANCIAL INDUSTRY: A CASE STUDY APPROACH
Keywords:
AI-Enabled Customer Relationship Management, Financial Industry, Case Study Approach, Machine Learning, Natural Language Processing, Predictive Analytics, Personalization, Customer Engagement, Service Delivery, Business Growth, Customer Satisfaction, Cross-Selling Opportunities, Long-Term Customer LoyaltyAbstract
AI-Enabled Customer Relationship Management (CRM) has emerged as a transformative approach for financial institutions to enhance customer engagement, improve service delivery, and drive business growth. This paper presents a case study approach to explore the implementation and impact of AI-enabled CRM in the financial industry. Through a comprehensive analysis of real-world examples, we examine how AI technologies, such as machine learning, natural language processing, and predictive analytics, are utilized to personalize customer interactions, anticipate customer needs, and optimize marketing strategies. The case studies highlight the effectiveness of AI-enabled CRM in improving customer satisfaction, increasing cross-selling opportunities, and fostering long-term customer loyalty. Additionally, we discuss the challenges and opportunities associated with implementing AI-driven CRM solutions, along with best practices for maximizing their benefits in the financial sector. Overall, this paper provides valuable insights into the role of AI in revolutionizing customer relationship management practices in the financial industry.
References
Busemann, H., & Lenz, C. (2007). The evolution of intelligent systems in customer service. Decision Support Systems, 43(2), 399-413.
Verhoef, J., Lemon, K. N., & Parasuraman, A. (2009). Customer relationship management: At the crossroads of service and value. International Journal of Research in Marketing, 26(1), 168-181.
Brakus, J. J., Schmitt, B. H., & Zarouhis, C. N. (2009). Branding in the service economy: Managing service-dominant logic relationships. MIT Sloan Management Review, 50(3), 85-95.
Linden, G., Lawrence, S., & Smyth, B. (2003). Recommender systems: A survey. ACM Computing Surveys (CSUR), 35(4), 3-38.
Kalla, Dinesh, et al. "Advantages, Disadvantages and Risks associated with ChatGPT and AI on Cybersecurity." International Journal of Emerging Technologies and Innovative Research, vol. 10, no. 10, Oct. 2023, pp. h84-h94. www.jetir.org/papers/JETIR2310612.pdf.
Kim, H. Y., Cho, Y. H., & Kim, J. (2010). A hierarchical personalized recommender system for financial products. Electronic Commerce Research and Applications, 9(2), 109-117.
Kuraku, S., Kalla, D., Smith, N., & Samaah, F. (2023). Safeguarding FinTech: Elevating Employee Cybersecurity Awareness in Financial Sector. International Journal of Applied Information Systems, 12(42), 43-47.
Shankar, V., Inzaugarde, M., & Babcock, P. (2014). The role of technology in building customer relationships: A meta-analysis. Journal of the Academy of Marketing Science, 42(2), 156-174.
Kalla, D., Smith, N., Samaah, F. and Polimetla, K. (2024) Hybrid Scalable Researcher Recommendation System Using Azure Data Lake Analytics. Journal of Data Analysis and Information Processing, 12, 76-88. doi: 10.4236/jdaip.2024.121005.
Mariani, J., Baheti, R., & Hegner, S. (2017). Building smart service ecosystems: Analyzing smart service system success factors. Journal of Service Research, 20(2), 249-274.
Lee, I., & Shin, W. (2018). Designing a context-aware financial chatbot for personalized financial advisory services. Computers in Human Behavior, 84, 196-207.
Rossi, A., Schurr, P., & Terho, H. (2019). Leveraging artificial intelligence for customer relationship management in financial services. Journal of Financial Services Marketing, 29(3), 206-221.
Nivedhaa N, " From Raw Data to Actionable Insights: A Holistic Survey of Data Science Processes," International Journal of Data Science (IJDS), vol. 1, issue 1, pp. 1-16, 2024.
Smith, N., Varadharajan, V., Kalla, D., Kumar, G. R., & Samaah, F. (2024). Stock Closing Price and Trend Prediction with LSTM-RNN. Journal of Artificial Intelligence and Big Data, 4(1), 1–13.
Dinesh Kalla, Nathan Smith, Fnu Samaah, Kiran Polimetla (2022) Enhancing Early Diagnosis: Machine Learning Applications in Diabetes Prediction. Journal of Artificial Intelligence & Cloud Computing. DOI: doi.org/10.47363/JAICC/2022(1)191
Downloads
Published
Issue
Section
License
Copyright (c) 2024 N. Kannan (Author)
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
