Duration 5 days – 35 hrs
Overview
This course provides a comprehensive understanding of Artificial Intelligence (AI) with a focus on Generative AI, Machine Learning (ML), and Robotic Process Automation (RPA) in the financial services and technology industries. Participants will explore AI fundamentals, real-world applications, ethical considerations, and strategic implementation within financial institutions and technology-driven enterprises. Through hands-on labs and case studies, learners will gain insights into how AI can be leveraged for product development, automation, and decision-making in financial services.
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
