On Designing Intelligent, Efficient Chatbot with Artificial Intelligence (AI), Machine Learning (ML) Tools

Authors

  •   Samradnyee Dhananjay Mehendale Assistant Professor, Pune Vidyarthi Griha’s College of Engineering, Technology & Management, Pune Research Scholar, Neville Wadia Institute of Management Studies & Research, Savitribai Phule Pune University
  •   Avinash Bhagwan Hande Associate Professor, P K Technical Campus, Pune, Neville Wadia Institute of Management Studies & Research, Savitribai Phule Pune University, Pune

DOI:

https://doi.org/10.17697/ibmrd/2025/v14i2/174495

Keywords:

Artificial Intelligence (AI) and Machine Learning (ML) tools for Banking, Intelligently Designed Chatbot, Efficient Customer Service

Abstract

The existing Chatbots are of a very elementary type, and their performance is unsatisfactory. To eliminate this lacuna, creating an intelligent, comprehensive, and self-evolving Chatbot for bank customers is necessary. The paper aims to develop a design for an intelligent and efficient chatbot utilizing Artificial Intelligence (AI) and Machine Learning (ML) technologies to create a high-quality online customer service facility for bank customers. In this paper, we focus our attention on bank customers, but the ideas developed could be easily modified and extended for other businesses. The existing Chatbots for bank customers (or for the customers of other businesses) are very elementary type, and the service they provide is very basic and unsatisfactory. We describe how the software program behind such a Chatbot can be enriched and made self-improving so that this Chatbot will substantially contribute to completing routine as well as complex tasks, which bank employees currently do. It will be very beneficial for banks to have such an efficient Chatbot to serve their customers by taking guidelines from the modern AI and ML tools for conducting various routine activities and even guiding the completion of some complex procedures using this Chatbot. Such an online facility for solving the queries of the customers related to banking services will be very welcoming for banks. It will be a great help for the bank customers to easily complete many of their banking-related tasks online. This paper is about inviting software development companies to develop a software comprising an advanced Chatbot containing features described herein for the benefit of bank customers.

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Published

2025-09-30

How to Cite

Mehendale, S. D., & Hande, A. B. (2025). On Designing Intelligent, Efficient Chatbot with Artificial Intelligence (AI), Machine Learning (ML) Tools. IBMRD’s Journal of Management & Research, 14(2), 123–132. https://doi.org/10.17697/ibmrd/2025/v14i2/174495

Issue

Volume 14, Issue 2, September 2025

Section

Articles
Crossref
Scopus
Google Scholar
Europe PMC

References

1. Chen, Lijia, Pingping Chen, and Zhijian Lin. 2020. “Artificial Intelligence in Education: A Review.” IEEE Access 8:75264–78. https://doi.org/10.1109/ACCESS.2020.2988510. DOI: https://doi.org/10.1109/ACCESS.2020.2988510

2. Davenport, Thomas, and Ravi Kalakota. 2019. “The Potential for Artificial Intelligence in Healthcare.” Future Healthcare Journal 6 (2): 94–98. https://doi.org/10.7861/futurehosp.6-2-94. DOI: https://doi.org/10.7861/futurehosp.6-2-94

3. Dikshit, Srishti, Areeba Atiq, Mohammad Shahid, Vinay Dwivedi, and Aarushi Thusu. 2023. “The Use of Artificial Intelligence to Optimize the Routing of Vehicles and Reduce Traffic Congestion in Urban Areas.” EAI Endorsed Transactions on Energy Web 10 (December). https://doi.org/10.4108/ew.4613. DOI: https://doi.org/10.4108/ew.4613

4. Grljević, Olivera, and Zita Bošnjak. 2018. “Sentiment Analysis of Customer Data.” Strategic Management 23 (3): 38–49. https://doi.org/10.5937/StraMan1803038G. DOI: https://doi.org/10.5937/StraMan1803038G

5. Ivanovich Vatin, Nikolai, Vivek John, Richa Nangia, Manish Kumar, and Y. Lakshmi Prasanna. 2024. “Supply Chain Optimization in Industry 5.0: An Experimental Investigation Using Al.” Edited by P.K. Prabhakar. BIO Web of Conferences 86:01093. https://doi.org/10.1051/bioconf/20248601093. DOI: https://doi.org/10.1051/bioconf/20248601093

6. Khurana, Diksha, Aditya Koli, Kiran Khatter, and Sukhdev Singh. 2023. “Natural Language Processing: State of the Art, Current Trends and Challenges.” Multimedia Tools and Applications 82 (3): 3713–44. https://doi.org/10.1007/s11042-022-13428-4. DOI: https://doi.org/10.1007/s11042-022-13428-4

7. Kosanović, Nikola. 2023. “PORTFOLIO MANAGEMENT AND SYSTEMIC RISK IN THE AGE OF ARTIFICIAL INTELLIGENCE.” KNOWLEDGE - International Journal 60 (1): 77–81. https://doi.org/10.35120/kij6001077k. DOI: https://doi.org/10.35120/kij6001077k

8. Krenek, Jiri, Kamil Kuca, Pavel Blazek, Ondrej Krejcar, and Daniel Jun. 2016. “Application of Artificial Neural Networks in Condition Based Predictive Maintenance.” In Recent Developments in Intelligent Information and Database Systems, edited by Dariusz Król, Lech Madeyski, and Ngoc Thanh Nguyen, 642:75–86. Studies in Computational Intelligence. Cham: Springer International Publishing. https://doi.org/10.1007/978-3-319-31277-4_7. DOI: https://doi.org/10.1007/978-3-319-31277-4_7

9. Lomborg, Stine, Anne Kaun, and Sne Scott Hansen. 2023. “Automated Decision-making: Toward a People-centred Approach.” Sociology Compass 17 (8): e13097. https://doi.org/10.1111/soc4.13097. DOI: https://doi.org/10.1111/soc4.13097

10. Nzubechukwu Chukwudum Ohalete, Adebayo Olusegun Aderibigbe, Emmanuel Chigozie Ani, Peter Efosa Ohenhen, and Abiodun Akinoso. 2023. “DATA SCIENCE IN ENERGY CONSUMPTION ANALYSIS: A REVIEW OF AI TECHNIQUES IN IDENTIFYING PATTERNS AND EFFICIENCY OPPORTUNITIES.” Engineering Science & Technology Journal 4 (6): 357–80. https://doi.org/10.51594/estj.v4i6.637. DOI: https://doi.org/10.51594/estj.v4i6.637

11. Phua, Clifton, Vincent Lee, Kate Smith, and Ross Gayler. 2010. “A Comprehensive Survey of Data Mining-Based Fraud Detection Research.” https://doi.org/10.48550/ARXIV.1009.6119.

12. Plathottam, Siby Jose, Arin Rzonca, Rishi Lakhnori, and Chukwunwike O. Iloeje. 2023. “A Review of Artificial Intelligence Applications in Manufacturing Operations.” Journal of Advanced Manufacturing and Processing 5 (3): e10159. https://doi.org/10.1002/amp2.10159. DOI: https://doi.org/10.1002/amp2.10159

13. Praful Bharadiya, Jasmin. 2023. “A Comprehensive Survey of Deep Learning Techniques Natural Language Processing.” European Journal of Technology 7 (1): 58–66. https://doi.org/10.47672/ejt.1473. DOI: https://doi.org/10.47672/ejt.1473

14. Sarker, Iqbal H. 2021. “Machine Learning: Algorithms, Real-World Applications and Research Directions.” SN Computer Science 2 (3): 160. https://doi.org/10.1007/s42979-021-00592-x. DOI: https://doi.org/10.1007/s42979-021-00592-x

15. Sun, Yuechi, Haiyan Liu, and Yu Gao. 2023. “Research on Customer Lifetime Value Based on Machine Learning Algorithms and Customer Relationship Management Analysis Model.” Heliyon 9 (2): e13384. https://doi.org/10.1016/j.heliyon.2023.e13384. DOI: https://doi.org/10.1016/j.heliyon.2023.e13384

16. Ugochukwu Ikechukwu Okoli, Ogugua Chimezie Obi, Adebunmi Okechukwu Adewusi, and Temitayo Oluwaseun Abrahams. 2024. “Machine Learning in Cybersecurity: A Review of Threat Detection and Defense Mechanisms.” World Journal of Advanced Research and Reviews 21 (1): 2286–95. https://doi.org/10.30574/wjarr.2024.21.1.0315. DOI: https://doi.org/10.30574/wjarr.2024.21.1.0315

17. Yang, Cenying, Yihao Feng, and Andrew Whinston. 2022. “Dynamic Pricing and Information Disclosure for Fresh Produce: An Artificial Intelligence Approach.” Production and Operations Management 31 (1): 155–71. https://doi.org/10.1111/poms.13525. DOI: https://doi.org/10.1111/poms.13525

18. Patel, N., Trivedi, S. 2020. “Leveraging Predictive Modeling, Machine Learning Personalization, And NLP Customer Support and Chatbots to Increase Customer Loyalty.” EQM Research Berg 3(3): 1–24

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