Machine Learning-Powered Chatbot

Quantilus used a deep learning model to develop & deploy a chatbot-based solution to understand and automatically reply to customer queries. The model was trained on a large dataset of customer queries and responses to understand the context of the queries and provide accurate responses. As the chatbot interacts with more customers, the model learns and improves its responses over time. The chatbot can also handle complex queries by understanding the intent behind them and providing relevant responses. 

Some of the functions which the chatbot was programmed to handle included: 

• Onboarding: The chatbot can guide new customers through the onboarding process by asking questions, providing necessary information and collecting the responses. 
• Cash Flow Queries: Customers can ask queries related to their cash flow, such as account balance, transactions, and payments. 
• Stock Portfolio Queries: Customers can ask queries related to their stock portfolio, such as stock prices, performance, and investments. 
• Notifications: The chatbot can send notifications to customers regarding account updates, market trends, and investment opportunities. 

• News Feeds: The chatbot can provide relevant news and information related to the investment industry and the customer’s stock portfolio. 
• Reporting: The chatbot can generate reports related to the customer’s account and portfolio. 

For developing the chatbot, Quantilus leveraged on Rasa for the Chatbot Development Framework, Python, PyTorch for the Deep Learning Framework, NLTK for the Natural Language Processing Library, Django for the Backend Framework, PostgreSQL and AWS for the Cloud Platform. 

Benefits of the chatbot solution include: 

• 24/7 availability: The chatbot is available to customers 24/7, providing immediate responses to their queries. 

• Cost-effective solution: The chatbot can handle a high volume of queries at a low cost compared to hiring more customer service agents. 
• Improved customer experience: The chatbot can provide quick and accurate responses to customer queries, improving customer satisfaction and experience. 
• Integration: The chatbot was integrated with the investment firm’s app, customer software, Facebook page, and website, providing a seamless customer experience.