FinTech - Shaping the Financial World mit course finance technology fintech

Artificial Intelligence, Machine Learning, and Deep Learning

  • Artificial intelligence
    • Computers mimicking human behaviour
    • Concept created ~1950s
  • Machine learning
    • Computer figures out how to achieve some tasks by analyzing large amounts of data
    • First written about in the 1980s and 1990s
    • Computers adapt and changed based on analysis of data
  • Deep learning
  • 95% of the cost of data analytics comes from cleaning up the data
  • In finance, what type of data do we want to feed into our AI systems?
  • Some high frequency trading companies don't feel the need to introduce AI into their systems
    • "Our algorimthic trading doesn't need the overhead"
  • Reasons for AI in finance
    • Interfacing with customers
      • Efficiency
        • Help more people with less cost
      • Inclusion
      • Better targeting
        • Reach more people that are likely to use the services
    • Fraud detection and prevention
    • Credit and insurance
      • AI is used to score someone's credit based on more sophisticated rules than traditional credit scoring
    • Regulatory
      • Anti-money laundering
      • Anti-manipulation
    • Robotic process automation
      • Automate the boring stuff
    • Automated trading

Required Readings

Study Questions

  1. What are artificial intelligence, machine learning, and deep learning? How do these enhanced tools of pattern recognition and decision making relate to financial services?
  2. What is natural language processing? How has it already enhanced user interfaces (UI) and user experiences (UX) in finance? How might chatbots, conversational interfaces and voice assistants transform UI & UX in the future?
  3. What sectors within the financial services sector has seen the most adoption of AI & machine learning? How can it be used to enhance compliance systems, customer interfaces, risk management, underwriting and investment strategies?