Shubhendu Banerjee
Papers
1
Total Citations
2
H-Index
1
About
Dr. Shubhendu Banerjee is a pioneering researcher at the intersection of artificial intelligence, decision science, and financial technology. His work centers on developing advanced multi-criteria decision-making (MCDM) frameworks, particularly through the innovative application of neutrosophic logic—a mathematical approach that handles uncertainty, indeterminacy, and inconsistency in complex evaluations. His most notable contribution is the creation of a Pentagonal Neutrosophic TODIM approach, a novel methodology designed to assess digital banking chatbot performance. This work, published in 2025 and already garnering 2 citations, addresses the critical need for robust evaluation tools in the rapidly evolving fintech landscape, where customer service automation demands continuous, reliable assessment. By integrating fuzzy set theory with behavioral decision-making, Banerjee provides a rigorous framework for comparing AI-driven banking solutions, helping institutions select chatbots that balance efficiency, user satisfaction, and operational reliability. His research bridges theoretical mathematics and practical industry applications, offering actionable insights for developers and financial managers. Banerjee’s contributions are particularly timely as the banking sector shifts toward 24/7 digital service models, making his work essential reading for scholars and practitioners in computational intelligence, operations research, and fintech innovation.
Research Focus
Key Achievements
Top Papers
- 1