Aug 2026· International Journal for Research in Applied Science and Engineering Technology· 0 citations
TL;DR
This study examines the role of AI-powered chatbots in enhancing customer service within banking institutions by reviewing existing literature and proposing a conceptual framework for chatbot adoption, and highlights challenges associated with privacy, trust, cybersecurity, and ethical considerations.
Abstract
Artificial Intelligence (AI)-powered chatbots have become an integral component of digital transformation in the
banking sector. These intelligent conversational agents utilize technologies such as Natural Language Processing (NLP),
Machine Learning (ML), and Generative AI to provide efficient, personalized, and round-the-clock customer support. The
increasing demand for instant banking services has encouraged financial institutions to deploy chatbots for handling customer
inquiries, transaction assistance, financial guidance, and complaint resolution. This study examines the role of AI-powered
chatbots in enhancing customer service within banking institutions by reviewing existing literature and proposing a conceptual
framework for chatbot adoption. The study identifies key benefits such as improved operational efficiency, reduced service costs,
enhanced customer satisfaction, and greater accessibility. Simultaneously, it highlights challenges associated with privacy, trust,
cybersecurity, and ethical considerations. The findings suggest that AI chatbots significantly contribute to customer engagement
and service innovation while emphasizing the necessity of integrating human support for complex banking interactions.
The method, ECCOLA, is presented, which aims at making the high-level AI ethics principles more practical, making it possible for developers to more easily implement them in practice.
Ville Vakkuri, Kai-Kristian Kemell, P. Abrahamsson· EUROMICRO Conference on Soft...· 64 citations· ⚡6
The goal is to not only refine the accuracy of the LLM-based tool but also to underscore its potential in streamlining the software development lifecycle through proactive code improvement and education.
Z. Rasheed, Malik Abdul Sami, Muhammad Waseem et al.· arXiv.org· 62 citations· ⚡3
A comprehensive overview of how enhanced sampling methods are reshaping the field, with a particular focus on the data-driven construction of collective variables, is provided.
Kai Zhu, Enrico Trizio, Jintu Zhang et al.· Chemical Reviews· 58 citations
The use of large language models to automatically improve the user story quality in Austrian Post Group IT agile teams is explored, with a reference model for an Autonomous LLM-based Agent System developed and implemented at the company.
Zheying Zhang, M. Rayhan, Tomas Herda et al.· International Conference on...· 48 citations· ⚡4
This paper introduces a novel multi-AI-agent system designed to fully automate SLRs, and demonstrates how it substantially reduces the time and effort traditionally required for SLRs while maintaining comprehensiveness and precision.
Abdul Malik Sami, Z. Rasheed, Kai-Kristian Kemell et al.· arXiv.org· 44 citations· ⚡2
The proposed LLM-based multi-agent system automates qualitative data analysis process, creating opportunities for researchers and practitioners, and future improvements focus on enhancing multilingual performance and integrating continuous expert feedback.
Z. Rasheed, Muhammad Waseem, Aakash Ahmad et al.· arXiv.org· 41 citations
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