Objectives:

Upon completion of this course, participants will be able to:

• Analyze the capabilities of **Generative AI (GenAI)** and Large Language Models (LLMs) in automating and scaling customer support functions.
• Design and implement GenAI-powered solutions for providing **localized, multilingual, and context-specific** financial advice and support.
• Develop strategies and fine-tuning techniques to mitigate the risks of **AI hallucination** and misinformation in customer responses.
• Establish a robust internal governance and **compliance framework** for all automated customer communications (e.g., UDAAP, disclosure rules).
• Evaluate the impact of GenAI on call center staff, focusing on training, quality assurance, and the design of **human-in-the-loop (HITL)** processes.
• Design prompt engineering and retrieval-augmented generation (**RAG**) architectures for accessing secure, approved financial information.
• Understand the data privacy and security requirements for using customer data to train and fine-tune localized GenAI models.
• Measure the effectiveness of GenAI deployment using metrics like first-call resolution, customer satisfaction, and cost savings.

• Heads of Customer Service, Call Center Operations, and Digital Channels
• AI/ML Strategists and Data Scientists in Financial Institutions
• Compliance Officers and Legal Counsel focused on Market Conduct
• Product Managers and User Experience (UX) Designers for Digital Tools
• IT/Technology Architects and Chief Innovation Officers
• Training and Quality Assurance Managers for Customer-Facing Roles
• FinTech and Payments System Operators

• Case Studies analyzing successful and failed GenAI customer support deployments in finance.
• Group Activities on designing a prompt engineering strategy for a complex, localized policy query.
• Discussions on the ethical risk of AI hallucination and the legal responsibility for misinformation.
• Individual Exercises on creating a human-in-the-loop escalation protocol for an automated chatbot.
• Workshop on developing a compliance review checklist for AI-generated customer responses.
• Live demonstration of GenAI capabilities for multilingual support.

Unit 1: GenAI in the Customer Support Landscape

• Overview of Generative AI: LLMs, fine-tuning, and their capability in natural language generation.
• Applications in finance: Automated responses, personalized communication, and dynamic knowledge bases.
• The business case: Cost reduction, 24/7 availability, and enhanced customer satisfaction.
• The challenge of maintaining **human touch** and empathy in automated support.

• Strategies for providing **multilingual support** (e.g., translation, direct fine-tuning of local models).
• Ensuring cultural relevance and local idiom/regulatory context in communication.
• The use of GenAI to scale support in hard-to-reach or underserved language communities.
• Measuring the quality and accuracy of localized AI-generated content.

Unit 2: Managing Risk and Compliance

• Understanding and mitigating the risk of **AI hallucination** (generating false information).
• Implementation of **Retrieval-Augmented Generation (RAG)** for grounding responses in secure, verified data.
• Designing a rigorous **content validation and fact-checking** process for all GenAI outputs.
• The role of the compliance team in vetting and approving the knowledge source.

• Applying **UDAAP** (Unfair, Deceptive, or Abusive Acts) principles to automated communication.
• Defining the financial institution's legal **liability** for incorrect or misleading AI-generated advice.
• Mandating clear disclosure that the customer is interacting with an AI (Transparency Principle).
• Training the AI to avoid biased or discriminatory language and responses.

Unit 3: Implementation and Operational Design

• Designing the workflow for **Human-in-the-Loop (HITL)** oversight and escalation.
• Training human agents to supervise AI, intervene when necessary, and provide corrective feedback.
• Strategies for seamless hand-off from the AI to a human agent.
• Defining the boundary: When must a human take over a conversation (e.g., high-risk, complex complaints)?

• Securing customer interaction data used for model fine-tuning and feedback loops.
• Compliance with data privacy rules (e.g., **PII** protection) in GenAI training and deployment.
• Designing robust monitoring systems to detect and prevent data leakage via the GenAI interface.
• The use of anonymization and synthetic data for model development.

Unit 4: Measurement and Future Strategy

• Key Performance Indicators (KPIs): **First-Contact Resolution (FCR)**, Customer Satisfaction (CSAT), and efficiency gains.
• Strategy for continuous model improvement and updating with new regulations/products.
• The future role of GenAI in personalization, proactive customer engagement, and sales.
• Regulatory foresight: Anticipating future rules on AI responsibility and liability.