Automation of Customer Support System (Chatbot) to Solve Web Based Financial and Payment Application Service


  • Roqib Akintunde Akinyemi Department of Computer Science, Lead City University, Ibadan, Nigeria
  • Wumi Ajayi Department of Computer Science, Babcock University, Ikenne, Nigeria
  • Ayuba Atuman Department of Computer Science, Lead City University, Ibadan, Nigeria



Chatbot, Customer Support, Google Script, Online Service, Testing


One of the most important features of any online service is the quality of its customer care. However, with the development of NLP tools, businesses are considering automated chatbot solutions to keep up with the increasing demand for their products and services. In view of this, the chatbot was developed using AIML java interpreter library Program AB which helps match input and output predefined in the AIML file. AIML (Artificial Intelligence Markup Language) was used to preprocess and train the bot using ready-made AIML file for FAQ questions. Also, vaadin was used to build a web UI to interact with the trained AIML bot. Finally, a google script was written to translate from any language to English for the bot to understand and send the response in the preferred language of the user. Findings showed that the response time of the bot is dependent of the network, as the design gave a score of 70%, 80%, 90% and 90% for load testing, stability, reliability testing and usability testing, respectively. Also, the bot is compatible with different operating systems, both for forward compatibility and backward compatibility having a score of 95%. The bot was able to answer customer questions, enquiries and complaints and the response time of the bot depends on the strength of the network since it is web based. Hence, the system provided a simple, cheaper, and durable customer financial and payment application service. Since chatbots cannot answer all questions, businesses that decide to implement them should ensure that they have enough protections in place against attacks and that routine requests are standardised to ensure optimal performance.


F. Peters, "Master thesis: Design and implementation of a chatbot in the context of customer support," 2018.

Y. Yun, D. Ma, and M. Yang, "Human-computer interaction-based decision support system with applications in Data Mining," Future Generation Computer Systems, Vol. 114, pp. 285-89, 2021. DOI: 10.1016/j.future.2020.07.048.

A. A. A. Weißensteiner, "Chatbots as an approach for a faster enquiry handling process in the service industry," Signature, Vol. 12, No. 04, 2018. DOI: 10.3030/811429.

S. Schanke, G. Burtch, and G. Ray, "Estimating the impact of ‘humanizing’ customer service chatbots," Information Systems Research, Vol. 32, No. 3, pp. 736-751, 2021. DOI: 10.1287/isre.2021.1015.

A. Truong et al., "Towards Automated Machine Learning: Evaluation and comparison of AUTOML approaches and Tools," IEEE 31st International Conference on Tools with Artificial Intelligence (ICTAI), 2019. DOI: 10.1109/ictai.2019.00209.

J. Hill, W. R. Ford, and I. G. Farreras, "Real conversations with artificial intelligence: A comparison between human-human online conversations and human-chatbot conversations," Computers in human behavior, Vol. 49, pp. 245-250, 2015.

W. Maroengsit, T. Piyakulpinyo, K. Phonyiam, S. Pongnumkul, P. Chaovalit, and T. Theeramunkong, "A survey on evaluation methods for chatbots," in Proceedings of the 2019 7th International conference on information and education technology, pp. 111-119, Mar. 2019.

M. H. Huang and R. T. Rust, "Engaged to a robot? the role of AI in Service," Journal of Service Research, Vol. 24, No. 1, pp. 30-41, 2020. DOI:10.1177/1094670520902266.

A. Følstad and M. Skjuve, "Chatbots for customer service: user experience and motivation," in Proceedings of the 1st international conference on conversational user interfaces, pp. 1-9, Aug. 2019.

T. Schachner, R. Keller, and F. V. Wangenheim, "Artificial Intelligence-based conversational agents for chronic conditions: Systematic Literature Review," Journal of Medical Internet Research, Vol. 22, No. 9, 2020. DOI: 10.2196/20701.

C. Rutschi and J. Dibbern, "Towards an understanding of scaling the software robot implementation," Progress in IS, pp. 453-466, 2020. DOI: 10.1007/978-3-030-45819-5_18.

R. Richer, N. Zhao, B. M. Eskofier, and J. A. Paradiso, "Exploring smart agents for the interaction with multimodal mediated environments," Multimodal Technologies and Interaction, Vol. 4, No. 2, pp. 27, 2020. DOI: 10.3390/mti4020027.

R. Law, R. Leung, A. Lo, D. Leung, and L. H. Fong, "Distribution channel in hospitality and tourism," International Journal of Contemporary Hospitality Management, Vol. 27, No. 3, pp. 431-452, 2015. DOI: 10.1108/ijchm-11-2013-0498.

A. Przegalinska, L. Ciechanowski, A. Stroz, P. Gloor, and G. Mazurek, "In bot we trust: A new methodology of Chatbot Performance Measures," Business Horizons, Vol. 62, No. 6, pp. 785-797, 2019. DOI: 10.1016/j.bushor.2019.08.005.

T. Nemoto and T. Fujimoto, "A classification and analysis focusing on attempts to give a computer a personality: A technological history of Chatbots as Simple Artificial Intelligence," in Innovations in Applied Informatics and Media Engineering, pp. 59-70, 2023. DOI: 10.1007/978-3-031-30769-0_6.

S. Singh and H. K. Thakur, "Survey of various AI chatbots based on technology used," 2020 8th International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions) (ICRITO), 2020. DOI: 10.1109/icrito48877.2020.9197943.

S. Fernandes et al., "Survey on various Conversational Systems," 2020 International Conference on Emerging Trends in Information Technology and Engineering (ic-ETITE), 2020. DOI: 10.1109/ic-etite47903.2020.126.

S. J. Shaikh, "Artificially Intelligent, Interactive, and Assistive Machines: A Definitional Framework for Intelligent Assistants," International Journal of Human-Computer Interaction, Vol. 39, No. 4, pp. 776-789, 2023.

P. Kumari and D. Toshniwal, "Long short term memory-convolutional neural network based deep hybrid approach for solar irradiance forecasting," Applied Energy, Vol. 295, pp. 117061, 2021. DOI: 10.1016/j.apenergy.2021.117061.

Ho Le, and Jung Lee, "Application of long short-term memory (LSTM) neural network for flood forecasting," Water, Vol. 11, No. 7, pp. 1387, 2019. DOI: 10.3390/w11071387.

W. Wang, "Running programs," Absolute Beginners Guide to Computing, pp. 29-42, 2016. DOI: 10.1007/978-1-4842-2289-8_3.

N. A. Al-Madi, K. A. Maria, M. A. Al-Madi, M. A. Alia, and E. A. Maria, "An intelligent Arabic chatbot system proposed framework," 2021 International Conference on Information Technology (ICIT), 2021. DOI: 10.1109/icit52682.2021.9491699.

X. Huang and A. CIS, Chatbot: Design, Architecutre, And Applications, 2021.

Y. Zhang et al., "Life after speech recognition: Fuzzing semantic misinterpretation for voice assistant applications," Proceedings 2019 Network and Distributed System Security Symposium, 2019. DOI: 10.14722/ndss.2019.23525.

M. McTear, Z. Callejas, and D. Griol, "Conversational interfaces: Devices, wearables, virtual agents, and Robots," The Conversational Interface, pp. 283-308, 2016. DOI: 10.1007/978-3-319-32967-3_13.

E. Adamopoulou and L. Moussiades, "An overview of chatbot technology," IFIP Advances in Information and Communication Technology, pp. 373-383, 2020. DOI: 10.1007/978-3-030-49186-4_31.

S. Natale, "Introduction," Deceitful Media, pp. 1-15, 2021. DOI: 10.1093/oso/9780190080365.003.0001.

N. Tavichaiyuth and E. Rattagan, "Developing chatbots in higher education: A case study of academic program chatbot in Thailand," 2021.

Y. M. Mohialden, M. T. Younis, and N. M. Hussien, "A novel approach to Arabic Chabot, utilizing Google Colab and the internet of things: A case study at a computer center," Webology, Vol. 18, No. 2, pp. 946-954, 2021. DOI: 10.14704/web/v18i2/web18365.

F. P. D. Sousa, The impact of social virtual presence agents and content-based product recommendation system on on-line customer purchase intention (Master’s thesis)., 2020.

P. Anki, A. Bustamam, H. S. Al-Ash, and D. Sarwinda, "Intelligent chatbot adapted from question and answer system using RNN-LSTM model," Journal of Physics: Conference Series, Vol. 1844, No. 1, pp. 012001, 2021. DOI:10.1088/1742-6596/1844/1/012001.

H. K. Ahmed and J. Ali Hussein, "Design and implementation of a chatbot for Kurdish language speakers using Chatfuel platform," Kurdistan Journal of Applied Research, pp. 117-135, 2021. DOI: 10.24017/science.2020.2.10.

L. Zhou, J. Gao, D. Li, and H. Y. Shum, "The design and implementation of Xiaoice, an empathetic social chatbot," Computational Linguistics, Vol. 46, No. 1, pp. 53-93, 2020. DOI: 10.1162/coli_a_00368.

M. Nuruzzaman and O. K. Hussain, "IntelliBot: A dialogue-based chatbot for the insurance industry," Knowledge-Based Systems, Vol. 196, pp. 105810, 2020. DOI: 10.1016/j.knosys.2020.105810.

R. K. Behera, P. K. Bala, and A. Ray, "Cognitive chatbot for personalised contextual customer service: Behind the scene and beyond the hype," Information Systems Frontiers, 2021. DOI: 10.1007/s10796-021-10168-y.

F. Gutierrez, "Spring Boot," Spring Cloud Data Flow, pp. 9-31, 2020. DOI: 10.1007/978-1-4842-1239-4_2.

R. A. Badru, A. A. Waheed, O. A. Akinmoluwa, and O. R. Obayemi, "Generation of surveillance networked nodes for oil pipelines’ theft," International Journal of Recent Engineering Science, Vol. 8, No. 5, pp. 21-26, 2021. DOI: 10.14445/23497157/ijres-v8i5p104.

D. Al-Ghadhban and N. Al-Twairesh, "Nabiha: An Arabic dialect chatbot," International Journal of Advanced Computer Science and Applications, Vol. 11, No. 3, 2020. DOI: 10.14569/ijacsa.2020.0110357.




How to Cite

Akinyemi, R. A., Ajayi, W., & Atuman, A. (2023). Automation of Customer Support System (Chatbot) to Solve Web Based Financial and Payment Application Service. Asian Journal of Computer Science and Technology, 12(2), 1–17.