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A Chatbot to Help Promoting Financial Literacy

Authors Davi Silva Eleuterio , Pedro Filipe Oliveira , Paulo Matos , Jorge Manuel Afonso Alves

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Subject Classification

ACM Subject Classification
  • Applied computing → Document analysis
  • Computing methodologies → Information extraction
  • Computing methodologies → Machine learning
Keywords
  • chatbot
  • financial literacy
  • web scraper
  • LLM
  • RAG

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Abstract

Currently, governments and many other institutions have been making significant efforts to promote financial literacy. However, a considerable portion of the population still lacks basic financial knowledge, highlighting the need for updated strategies to enhance financial education. In today’s digital world - where people often search for quick and convenient solutions - the development of a reliable and intelligent chatbot to answer questions related to financial concepts and decision-making can be very beneficial. This paper proposes the implementation of an automated web scraper to extract content from a trustworthy financial education website with plenty of useful concepts about finances, using this collected data to develop a chatbot which provides accurate and helpful responses to users. The solution is built using technologies such as Streamlit, Langchain, and OpenAI.

Cite As Get BibTex

Davi Silva Eleuterio, Pedro Filipe Oliveira, Paulo Matos, and Jorge Manuel Afonso Alves. A Chatbot to Help Promoting Financial Literacy. In 14th Symposium on Languages, Applications and Technologies (SLATE 2025). Open Access Series in Informatics (OASIcs), Volume 135, pp. 7:1-7:9, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2025) https://doi.org/10.4230/OASIcs.SLATE.2025.7

Author Details

Davi Silva Eleuterio
  • UNIAG, Instituto Politécnico de Bragança, Portugal
Pedro Filipe Oliveira
  • CeDRI, Instituto Politécnico de Bragança, Portugal
Paulo Matos
  • CeDRI, Instituto Politécnico de Bragança, Portugal
Jorge Manuel Afonso Alves
  • UNIAG, Instituto Politécnico de Bragança, Portugal

Funding

This work was supported by national funds through FCT/MCTES (PIDDAC): CeDRI, UIDB/05757/2020 (DOI: https://doi.org/10.54499/UIDB/05757/2020); and SusTEC, LA/P/0007/2020 (DOI: https://doi.org/10.54499/LA/P/0007/2020). This work was supported by national funds through FCT/MCTES (PIDDAC): UID/04752 – Applied Management Research Unit (UNIAG).

References

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