Generative AI and Machine Learning is an advanced, industry-focused training program designed to help professionals understand and apply AI technologies to financial services and technology-driven business environments. The course combines Generative AI, machine learning, predictive analytics, automation, and practical AI applications to help participants identify opportunities for innovation while addressing the unique challenges of finance and technology.
Duration 5 days – 35 hrs
Overview
This 5-day intensive training provides a practical and industry-focused exploration of Generative AI, Machine Learning, and Robotic Process Automation (RPA) for financial services and technology organizations. Participants will learn how these technologies are transforming financial operations, customer experiences, risk management, product development, and technology-driven decision-making.
The course moves from AI and machine learning fundamentals to practical implementation, giving participants a clear understanding of how AI solutions are developed, evaluated, and applied to real-world financial and technology challenges. Through hands-on labs, demonstrations, and industry-based case studies, learners will explore applications such as fraud detection, risk assessment, customer service automation, predictive analytics, intelligent document processing, and personalized financial services.
Participants will also gain practical exposure to Generative AI and Large Language Models (LLMs) and learn how they can support financial research, reporting, customer communication, knowledge management, and business process automation. The training demonstrates how Generative AI can work alongside traditional machine learning and RPA to create more intelligent and efficient workflows.
A strong emphasis is placed on AI governance, data privacy, security, explainability, bias, regulatory considerations, and responsible AI adoption. Participants will examine the challenges of implementing AI in highly regulated financial environments and learn how organizations can balance innovation with appropriate risk management and human oversight.
By the end of the program, participants will be able to identify high-value AI opportunities, evaluate suitable AI and automation technologies, understand machine learning and Generative AI applications, and design an AI-driven solution for a financial or technology use case. The capstone project allows learners to bring these concepts together by developing a practical AI solution aligned with business objectives.
Learning Objectives
- Understand the fundamentals of AI, Machine Learning, and RPA.
- Explore AI applications in the financial services and technology sectors.
- Implement AI-driven solutions to enhance product development and automation.
- Analyze AI models and their effectiveness in fraud detection, risk management, and customer service automation.
- Learn ethical considerations, governance, and compliance related to AI in finance and technology.
- Gain hands-on experience with AI tools, including Generative AI and ML models.
Audience
- Product Development Managers
- Project Managers (Tech Department)
- AI & Data Science Enthusiasts in Financial Services
- Business Analysts in Technology & Finance
- IT and Automation Professionals in FinTech
- Risk & Compliance Officers Exploring AI Governance
Prerequisites
- Basic proficiency in English writing and business communication
- Experience in drafting business or technical documents
- Familiarity with Microsoft Word, Google Docs, or similar writing tools
- Interest in using AI for document automation and refinement
Course Content
Day 1: Introduction to AI in Financial Services & Technology
- Overview of Artificial Intelligence
- AI vs. Machine Learning vs. RPA: Understanding the Differences
- AI Trends in the Financial Services & Tech Industry
- Use Cases: AI in Fraud Detection, Risk Analysis, and Automation
- Hands-on Lab: Exploring AI-driven Financial Applications
Day 2: Machine Learning Fundamentals
- Types of Machine Learning: Supervised, Unsupervised, Reinforcement Learning
- ML Model Lifecycle: Data Collection, Training, Testing, and Deployment
- Feature Engineering and Data Preparation for AI
- Hands-on Lab: Building a Simple Machine Learning Model
Day 3: Generative AI and Advanced AI Models
- What is Generative AI? Applications in Finance and Technology
- Introduction to Large Language Models (LLMs) and NLP
- Generative AI Use Cases: Personalized Finance Assistants, AI Chatbots, and Report Generation
- Hands-on Lab: Working with OpenAI GPT, BERT, and Financial AI Models
Day 4: Robotic Process Automation (RPA) in Financial Services
- RPA vs. AI: Understanding the Relationship
- Automating Repetitive Tasks in Financial Workflows
- AI-Powered Chatbots and Virtual Assistants for Customer Service
- Hands-on Lab: Implementing RPA with AI-based Decision Making
Day 5: AI Governance, Compliance, and Future Trends
- Ethical AI and Bias Considerations in Finance
- Regulatory Frameworks and AI Compliance in Financial Services
- Future of AI in Financial Technology and Emerging Trends
- Capstone Project: Designing an AI-Driven Financial Solution
