IoT and AI for Real-Time Customer Behavior Analysis in Digital Banking

Title

IoT and AI for Real-Time Customer Behavior Analysis in Digital Banking

Creator

Madhumita G.; Das T.; Das S.; Khatri E.; Ravisankar P.; Hemachandu P.

Description

Digital transformation has revolutionized the banking industry, ushering in an era of enhanced customer experiences and operational efficiency. The convergence of Internet of Things (IoT) and Artificial Intelligence (AI) technologies has further propelled this evolution by providing real-time insights into customer behavior. This research explores the integration of IoT and AI for real-time customer behavior analysis in the context of digital banking. The proliferation of connected devices, ranging from smart phones to wearables, has generated an unprecedented volume of data. IoT facilitates the collection of diverse data points, such as transaction history, location information, and device interactions, creating a comprehensive digital footprint for each customer. Simultaneously, AI algorithms leverage this wealth of data to analyze, predict, and respond to customer behavior dynamically. In the realm of digital banking, understanding and adapting to customer behavior in real-time is crucial for providing personalized services, preventing fraud, and optimizing operational processes. This research delves into the mechanisms by which IoT sensors and devices, coupled with AI algorithms, enable banks to gain deeper insights into customer behavior patterns. Key components of the proposed system include data acquisition through IoT devices, secure data transmission protocols, and AI-driven analytics engines. In conclusion, this research advocates for the symbiotic relationship between IoT and AI in digital banking to enable real-time customer behavior analysis. 2024 IEEE.

Source

5th International Conference on Recent Trends in Computer Science and Technology, ICRTCST 2024 - Proceedings, pp. 198-203.

Date

2024-01-01

Publisher

Institute of Electrical and Electronics Engineers Inc.

Subject

AI (Artificial Intelligence); Context-aware computing; Customer behavior analysis; Customer experience; Data analytics; Digital banking; IoT (Internet of Things); Machine learning; Personalization; Predictive modeling; Real-time; Smart devices

Coverage

Madhumita G., Vels Institute of Science, Technology and Advanced Studies, Department of MBA, Tamil Nadu, Chennai, India; Das T., Christ University, Department of School of Business Management, UP, Ghaziabad, India; Das S., Christ University, Department of School of Commerce, Finance & Accountancy, UP, Ghaziabad, India; Khatri E., Nitte Meenakshi Institute of Technology, Department of Management Studies, Karnataka, Bengaluru, India; Ravisankar P., Saveetha College of Liberal Arts and Sciences, Department of Commerce, Thandalam, Chennai, India; Hemachandu P., Sasi Institute of Technology and Engineering, EEE Department, Andhrapradesh, Tadepallidusem, 534101, India

Rights

Restricted Access

Relation

ISBN: 979-835035137-8

Format

Online

Language

English

Type

Conference paper

Identifier

https://doi.org/10.1109/ICRTCST61793.2024.10578458

https://www.scopus.com/inward/record.uri?eid=2-s2.0-85199390309&doi=10.1109%2fICRTCST61793.2024.10578458&partnerID=40&md5=0b6d38ae852821a8e01c3836b83fe3c4