Smart Banking Chatbot

Abstract

This paper provides the development and implementation of a smart banking chatbot designed to enhance the user experience by providing automated responses to customer inquiries. With the increasing digitalization of banking services, the need for quick and efficient customer support has become crucial. The chatbot, developed using the RASA framework, leverages Natural Language Processing (NLP) to understand user intents and execute predefined actions. It offers services such as checking account balances, making transactions, and addressing customer queries in real-time, reducing the dependency on human customer support. By evaluating different machine learning algorithms, including Random forest, (KNN) k-Nearest neighbors, and Support Vector Machines(SVM), the chatbot ensures high accuracy in predicting user requests. RASA is an open-source (NLP) Natural Language Processing platform used to create virtual assistants and contextual chatbots. RASA has several main components: RASA Open Source and RASA Action Server. Rasa Open Source has Rasa NLU which is used to recognize the intent of communication and Rasa core to determine what to do next and how to continue the conversation. Rasa Action Server allows you to host python scripts to do certain custom tasks, like processing or modifying databases in the backend.The experimental results demonstrate that the chatbot significantly reduces response time and improves service efficiency, making it a valuable addition to modern banking systems

Introduction

The text discusses the development and deployment of a Smart Banking Chatbot designed to enhance customer service in the banking sector by automating tasks like balance inquiries, transactions, and general support through AI-driven interactions. Chatbots are intelligent agents using Natural Language Processing (NLP) and Machine Learning (ML) to understand user intent and provide relevant responses via text or voice. This banking chatbot integrates key components such as Natural Language Understanding (NLU), Dialogue Management, and Natural Language Generation (NLG), supported by ML models including Random Forest, K-Nearest Neighbors (KNN), and Support Vector Machines (SVM).

The paper reviews previous studies on chatbot technologies in banking, noting challenges such as limited capabilities and lack of user familiarity, especially in the Indian banking context. The problem addressed is the need for faster, efficient, and 24/7 customer service beyond traditional human agents, which is costly and often slow.

The system architecture is modular, featuring a user interface with secure login methods, an NLP module based on RASA NLU, a dialogue manager, ML models for intent classification, a secure backend database, and API integration for real-time banking operations. The development follows a Software Development Life Cycle (SDLC) umbrella model, including requirement gathering, system analysis, design, training, integration, testing, deployment, and maintenance.

Evaluation results show that the Random Forest model achieved over 90% accuracy in intent recognition, enabling the chatbot to respond instantly to user queries, improving customer experience by reducing wait times and supporting multiple simultaneous requests. The study highlights the chatbot’s potential to revolutionize banking customer service by providing scalable, reliable, and round-the-clock assistance, thus enhancing operational efficiency and user satisfaction.

Conclusion

Smart banking chatbot development improves customer service automation using AI, NLP, and machine learning significantly, enhancing user experience, response time, and operational effectiveness. The chatbot provides precise answers to questions, making banking services more accessible. Nevertheless, it lacks limitations in addressing complex questions involving more contextual understanding. Future development needs to incorporate deep learning models to further improve contextual understanding and extend banking services. Moreover, future enhancements may involve voice recognition to allow hands-free communication, multilingual capabilities to support different customer bases, and biometric verification to ensure secure transactions. Moreover, integrating investment consulting, fraud monitoring, and financial planning services would provide a value-added banking experience, rendering the chatbot a more integral part of the customer\'s overall financial experience.

References

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Copyright

Copyright © 2025 Sowmya Thota, Sai Keerthana Bongoni, Dr. L.. Chandrasekhar Reddy, Parameswar M. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.