AI-Powered Remittance Chatbot by Team BERT HDSC Spring '24 Cohort
In today's interconnected world, remittances have become a financial lifeline for millions of families, particularly in developing countries. These cross-border financial transfers, sent by migrant workers to their home countries, play a crucial role in supporting livelihoods and driving economic development. However, the complexity of remittance processes, varying costs, and ever-changing regulations often pose significant challenges for both senders and recipients. Enter the AI-powered Remittance Chatbot—a revolutionary tool designed to simplify and streamline remittance services.
Introducing the Remittance Chatbot
The Remittance Chatbot is an artificial intelligence-driven virtual assistant developed by Team BERT from the HDSC Spring '24 Cohort. This innovative chatbot aims to provide accessible, accurate, and timely information on remittance patterns, costs, and processes, empowering users to make informed decisions about their international money transfers.
Why Remittance Chatbots?
In an era where digital technologies are reshaping the financial landscape, there is a growing need for solutions that can simplify complex processes and improve access to financial services. The Remittance Chatbot leverages natural language processing (NLP) and machine learning techniques to understand and respond to user queries in a conversational manner. By integrating a comprehensive knowledge base of remittance data, regulatory information, and domain-specific expertise, the chatbot addresses the challenges faced by users in navigating the remittance ecosystem.
The Development Journey
The development of the Remittance Chatbot followed a systematic approach, incorporating various techniques and models to achieve the most effective solution. Here’s a brief overview of the process:
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Data Collection and Preprocessing: A diverse set of remittance-related data was collected from reputable financial institutions, government agencies, and international organizations. This data underwent rigorous preprocessing to ensure it was clean, structured, and suitable for training the chatbot model.
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Knowledge Base Construction: A comprehensive knowledge base was constructed using the preprocessed data, encapsulating relevant information required to answer remittance-related queries effectively.
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Model Exploration and Implementation: Various NLP and machine learning approaches were explored, including models from Langchain, OLLAMA, Hugging Face, and OpenAI. After rigorous evaluation, the GPT-3.5-Turbo model was selected for its superior performance.
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Local Deployment and Testing: The chatbot was deployed locally using Streamlit, a popular Python library for creating web applications. Final testing showed promising results in terms of user interaction and query response accuracy.