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| Autor principal: | |
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| Formato: | Recurso digital |
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Zenodo
2025
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| Assuntos: | |
| Acesso em linha: | https://doi.org/10.5281/zenodo.17606884 |
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Sumário:
- <p>Chatbots are intelligent systems designed by Artificial Intelligence (AI) and are upgraded with Natural Language<br>Processing (NLP) algorithms. In an impressive way, it engages users and interacts with them, answering their questions.<br>Conversation facilitators are mostly used by companies, government departments, and non-profit organisations. Money-related<br>industries such as banks, credit card companies, financial institutions, e-commerce stores, and startups are typical places where we<br>find these chatbots implemented. This research paper depicts the implementation and assessment of a Banking Conversational<br>Chatbot powered by Deep Learning (DL) techniques. The bank chatbot dataset, consisting of real user communication, was<br>preprocessed by cleaning, tokenisation, normalisation, and data balancing using SMOTE to ensure the training data was of the<br>highest quality. The authors proposed a Gated Recurrent Unit (GRU) network to capture the sequential dependencies and contextual<br>patterns of the user query, providing a more efficient and compact solution than the traditional LSTM model. In the conducted<br>comparative experiments with different models, namely SVM, XGBoost, and Naive Bayes, the accuracy recorded was 68%, 79%,<br>and 91%, respectively, while the argued GRU model results showed superiority over the other models with its accuracy of 97%,<br>precision of 97.9%, recall of 96%, and an F1-score of 97%. These figures demonstrate the GRU model's strength and effectiveness<br>in identifying user intent; thus, it can be a significant boost to the performance and reliability of conversational banking applications.</p>