About the Course
Learning Outcomes
Who is this Course for?
Modules
Session 1: Overview of AI and its Role in Fintech
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Definition of AI, machine learning, and generative AI
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AI applications in financial services: payments, lending, insurance, wealth management
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The transformative potential of AI in fintech
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Case studies of AI in finance
Session 2: Fundamentals of AI: Key Concepts
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Introduction to data-driven decision-making
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Key AI technologies: machine learning, natural language processing, and computer vision
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Overview of AI lifecycle: data collection, model building, and deployment
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Session 3: Machine Learning Fundamentals for Fintech
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Supervised vs. unsupervised learning
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Application of machine learning models in financial forecasting and risk assessment
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Understanding credit scoring models and fraud detection
Session 4: Hands-On Introduction to Machine Learning
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Basic hands-on exercises in data analysis and model training (simplified using no-code/low-code tools)
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Demo of a machine learning tool in the financial context
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Session 5: Introduction to Generative AI
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What is generative AI and its core components
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Use cases of generative AI in fintech: personalized financial services,= chatbot development, and automated content generation
Session 6: Generative AI and Chatbots in Financial Services
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Exploring AI chatbots for customer service and financial advice
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Case studies of chatbot deployment in fintech companies
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Session 7: The Role of Data in AI and Fintech
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Importance of data quality and data governance in AI applications
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Introduction to data analytics tools in fintech
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Compliance and privacy concerns related to financial data
Session 8: Data-Driven Insights for Financial Decision-Making
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Practical example: building basic financial dashboards using data analytics tools
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Real-world case study: leveraging AI for personalized financial product recommendations
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Session 9: AI for Fraud Detection in Fintech
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Machine learning algorithms for detecting fraudulent transactions
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AI-driven risk-scoring models
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Case study: AI tools used by banks and payment processors
Session 10: Implementing AI for Risk Management
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Identifying risks using predictive modeling
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AI applications for credit risk, operational risk, and liquidity risk management
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Demo: Basic predictive risk model using AI tools
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Session 11: AI in Payments
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How AI optimizes payment processing systems
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Use cases: AI-driven payment gateways and fraud prevention
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Case studies: PayPal, Stripe, and AI-driven payment solutions
Session 12: AI for Lending and Credit Scoring
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AI’s impact on peer-to-peer lending and microfinance
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Credit scoring models powered by AI
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Case studies on AI in digital lending platforms
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Session 13: Regulatory Landscape for AI in Finance
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Overview of financial regulations impacting AI adoption
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Compliance with data protection laws (e.g., GDPR, PSD2)
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Addressing algorithmic bias and fairness in AI models
Session 14: Ethical AI and Responsible Innovation in Fintech
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Importance of transparency, fairness, and accountability in AI models
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Ethical considerations in customer data usage
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Best practices for deploying ethical AI in fintech
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Session 15: Future Trends in AI for Fintech
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Trends shaping the future of AI in finance: decentralized finance (DeFi), blockchain, AI-driven asset management
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How AI and fintech will evolve in the coming years
Session 16: Capstone Project Presentation and Course Wrap-Up
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Team-based or individual capstone projects
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Presentations and feedback ○ Course review and next steps
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