iDigiChat: intelligent digital marketing service chatbot for providing efficient customer services using artificial...

Area of Science:

• Artificial Intelligence
• Natural Language Processing
• Financial Technology

Background:

• Increasing demand for intelligent customer service in finance.
• Limitations of existing chatbots in handling complex financial queries due to insufficient language pattern knowledge.

Purpose of the Study:

• Introduce iDigiChat, an advanced AI framework for financial customer service.
• Improve precision and responsiveness of AI-powered chatbots in the financial sector.

Main Methods:

• Integration of knowledge graph (KG) and artificial neural network (ANN) techniques.
• Utilizing Text-to-Graphql for query structuring and Knowledge Graph Completion for insight extraction.
• Employing a multi-layered ANN framework (KG-ANN) for classification and refinement.

Main Results:

• Achieved 90% R-squared value in simulations.
• Reduced mean absolute error and mean squared error by 25%.
• Enabled faster and more reliable customer interactions.

Conclusions:

• The iDigiChat framework significantly enhances AI chatbot performance in financial customer service.
• The KG-ANN integration provides a more effective approach to understanding and responding to complex customer queries.
• The system demonstrates the potential for more efficient and reliable customer interactions in the financial industry.