Are you working in a bank and want to stay ahead by leveraging AI tools?
If you’re not using AI to streamline your processes, enhance decision-making, and boost productivity, you’re missing out on a major competitive advantage. This course is designed to help you master generative AI techniques tailored specifically for the financial industry.
Don’t fall behind—sign up now and lead the future of banking with AI!
About the Course
This eight-week course is designed for non-technical participants interested in exploring how AI and Generative AI transform the fintech industry. Through hands-on, project-based learning, participants will gain practical skills in applying AI tools to real-world financial problems. The course covers key topics such as AI in payments, lending, risk management, and personalized financial services. The program is primarily held online with a few in-person sessions at Reboot Coding Institute.
Learning Outcomes
Understand how AI and Generative AI are applied in fintech.
Gain hands-on experience with AI tools for payment systems, lending, and financial risk management.
Develop AI-driven solutions for fintech use cases like wealth management, compliance, and insurance.
Collaborate on project-based fintech applications using AI and Generative AI.
Build confidence in leveraging AI for non-technical financial roles and business innovation.
Non-technical Professionals in Fintech, Finance, and Banking.
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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Course Modules
Module 1: Introduction to AI and Fintech
Module 2: Machine Learning in Finance
Module 3: Generative AI in Fintech
Module 4: Data in AI and Fintech
Module 5: AI-Powered Fraud Detection and Risk Management
Module 6: AI in Payments, Lending, and Credit Scoring
Module 7: Regulatory and Ethical Considerations in AI for Fintech
Module 8: Future Trends and Capstone Project
Trainer
Dr. Tanya Roosta
Fellow and Lecturer at UC Berkeley | Senior Research Science Manager at Amazon AI
Dr. Tanya received her Ph.D. in Electrical Engineering and Computer Science, from UC Berkeley. Master of Financial Engineering, from Haas Business School, UC Berkeley, and a Master in Statistics, from UC Berkeley. She also received a Master’s in Electrical Engineering and Computer Science, from UC Berkeley. Dr. Tanya is focused on building machine learning systems in finance and high-tech. She provides technical leadership for teams of scientists and engineers. Her core expertise is general ML, Natural Language Processing, and Statistics.
