Banks handle thousands of customer support interactions daily, ranging from simple queries to emotionally charged, high-risk financial issues. Traditional support systems struggle to balance speed, empathy, and operational efficiency, while poorly designed automation can damage customer trust. 

The purpose of this internship is to explore how AI, when thoughtfully designed, can enhance customer support by handling repetitive requests, identifying emotional urgency through sentiment analysis, and ensuring timely human intervention for sensitive cases. Learners are challenged to design AI systems that improve efficiency without compromising trust, compliance, or human judgment.

By the end of this virtual internship, learners will produce a complete AI-assisted banking support solution blueprint. Deliverables include a customer support journey analysis, conversational chatbot flow designs, a sentiment classification and escalation framework using banking support data, and a capstone system architecture that integrates chatbots, sentiment intelligence, and human agent workflows. 

Learners also submit a business-focused impact summary outlining how the proposed system improves response times, customer satisfaction, and operational effectiveness within a regulated banking environment.

This virtual internship is designed for learners who have a foundational understanding of data and technology concepts and are eager to apply them in real-world business contexts.

Participants should be comfortable with basic data handling (such as working with spreadsheets or simple datasets), have a conceptual understanding of how AI or machine learning systems work, and possess strong analytical and logical thinking skills. 

Familiarity with customer service processes, business workflows, or digital products is helpful but not mandatory. Most importantly, learners should be willing to think critically, communicate their reasoning clearly, and approach AI design with a customer-centric and responsible mindset.