GAI-CL1: Enhancing the Advisory Role in Financial Sales with Generative AI

After completing this course, students will be able to:

• Gain a foundational understanding of GPT models and their role in customer segmentation.
• Recognize the importance of data privacy and compliance, and apply these principles in the context of AI model training.
• Set up GPT bots with appropriate parameters and integrate banking product information as a data source.
• Develop the ability to design effective prompts to guide the GPT model's outputs, ensuring relevance in customer engagement and marketing scenarios.
• Apply GPT models to analyze and segment customers based on key data sources.
• Implement GPT models to optimize various stages of the AIDA digital marketing funnel.
• Create tailored, data-driven sales scripts that align with specific customer profiles and stages of the marketing funnel.
• Design and implement workflows that ensure the GPT model can handle unexpected inputs or scenarios effectively.
• Utilize GPT to simulate real-world customer conversations, managing objections and rejections.
• Critically assess the quality and quantity of data used in GPT model training to ensure accurate and reliable outputs.

Financial Sales Advisor, Relationship Managers, Financial Product Managers, and Marketing Executives in the Banking and Financial Industry.

• Show an understanding of data privacy regulations (e.g., PDPA, GDPR) by outlining the steps they would take to ensure customer data is protected during GPT model training.
• Set up GPT bots with accurate parameters (e.g., token limits, scope) and successfully integrate customer data, ensuring the bots are properly fine-tuned for the banking domain.
• Provide examples of well-constructed prompts for various customer engagement scenarios, demonstrating an understanding of prompt engineering principles.
• Present a breakdown of customer segments based on provided data, explaining how the GPT model identified different profiles.
• Illustrate how GPT-generated content can be used at each stage of the marketing funnel (Awareness, Interest, Desire, Action), offering examples of specific strategies.
• Create and present customized sales scripts for different customer segments, using data-driven insights from GPT models to support their approach.
• Develop and explain a failsafe mechanism in their GPT model, outlining how it would respond to out-of-scope scenarios or unexpected customer inputs.
• Participate in role-playing exercises, where they use the GPT model to handle simulated customer objections and rejections, demonstrating adaptability and strategic thinking.
• Critically review a set of training data, identifying potential quality issues and explaining how these could affect the GPT model’s accuracy and output reliability.

DAY 1: Understanding AI, Data Privacy, and Setting Up GPT Models

Module 1: Introduction to Generative AI and GPT Models

• Introduction to OpenAI and ChatGPT.
• Overview of AI in the financial sector.
• Understanding Large Language Models (LLMs) and how GPT works.
• Understanding the capabilities and limitations of ChatGPT.
• Case studies of AI implementations in banking

Module 2: Data Privacy, Compliance, and Preparing Data for AI

• Overview of key banking data privacy regulations: PDPA, GDPR, and financial data laws.
• Best practices for ensuring compliance and safeguarding sensitive customer data during model training.
• Preparing and structuring data for uploading into the GPT model.
• Setting up model parameters: Token limits, response variability, and knowledge scope.
• Understanding domain-specific fine-tuning for financial products and selecting training data sources.

Module 3: Prompt Engineering for GPT Models

• Introduction to the concept of prompt engineering.
• An overview of what prompts are and how they guide the output of ChatGPT. Explanation of key principles in creating effective prompts, including clarity, specificity, and tone.
• Understanding the structure of prompts and how they influence GPT model responses.
• Exploration of a few techniques for prompting, including the use of system instructions (guiding the AI's behavior with specific instructions), and role-playing methods (pretending the AI is a specific character or professional).

Module 4: Customer Segmentation and GPT in the Digital Marketing Funnel

• Introduction to the AIDA (Awareness, Interest, Desire, Action) model in digital marketing.
• Using GPT models to segment customers based on uploaded data.
• Applying GPT across each stage of the marketing funnel:
• Awareness: Crafting content to introduce financial products.
• Interest: Generating personalized insights to engage customers.
• Desire: Customizing offers based on customer profiles and purchase history.
• Action: Creating scripts and automated responses to close sales.
• Best practices for designing complex prompts to handle objections and specific customer inquiries.

 

Day 2: Simulating Customer Engagement and Ensuring Model Accuracy

Module 5: Sales Script Generation for Conversion

• Developing personalized sales scripts for various customer segments using GPT-generated insights.
• Crafting customized scripts for online, phone, and in-person customer interactions.
• Aligning scripts with the "Desire" and "Action" stages of the digital marketing funnel for better conversion rates.
• Best practices for fine-tuning prompt strategies for different stages of the digital marketing funnel (Awareness, Interest, Desire, Action).

Module 6: Failsafe Mechanisms for GPT Models

• Failsafe Workflow: Teaching GPT models to recognize when they are operating outside their knowledge base and how to respond to extreme or out-of-scope scenarios.
• Importance of designing workflows that handle unpredictable customer inputs and ensure compliance.
• Building robust failover responses for out-of-bounds customer queries.

Module 7: Simulating Customer Interactions and Handling Objections

• Simulating real-world customer conversations using GPT.
• Training the model to handle customer objections and navigate sales negotiations.
• Creating scripts to respond to typical customer concerns, such as price objections or product comparisons.

Module 8: Evaluating Training Data Quality and Ensuring Model Accuracy

• Importance of both quantity and quality of data in GPT training.
• Methods for critically evaluating the training data sources for better accuracy and reliability of GPT outputs.
• Reviewing how training data impacts the accuracy and reliability of customer segmentation, interactions, and sales conversions.