ME319: Machine Learning and Stochastic Simulation: Applications for Finance, Risk Management and Insurance

Are you looking to develop the skills to solve real-world challenges in finance, risk management, and insurance?

These fields often deal with unpredictable phenomena—like investment decisions, insurance claim patterns, or pricing derivatives—which require robust stochastic models and advanced machine learning techniques.

To tackle these challenges effectively, it’s essential to use robust statistical techniques and calibration methodologies to ensure models are reliable. Additionally, state-of-the-art machine learning models and algorithms must be developed and trained, as they have become indispensable across industries—from powering search engines to driving business decisions in risk assessment.

This course equips you with the tools to apply modern statistical and machine learning methods to these complex problems. You will start by exploring Monte Carlo methods, simulating stochastic processes, and applying Generative Adversarial Networks (GANs) in risk management. You will then connect Generalized Linear Models to deep neural networks, discovering their practical applications in the insurance industry. The course also addresses the challenges of calibrating models to ensure their accuracy and reliability.

Combining rigorous theory with hands-on coding exercises in Python, you will gain experience implementing real-world case studies while strengthening your core data science skills. By the end, you will be well-prepared to apply what you’ve learned in fields like finance, risk management, insurance, or data science.

Prerequisites: At least one semester of Calculus, and at least one semester of Probability and Statistics. Some minimal experience with computer programming is also required.

Level: 300 level. Read more information on levels in our FAQs

Fees: Please see Fees and payments

Lectures: 36 hours

Classes: 18 hours

Assessment: Mid-session exam (50%) and a final exam (50%)

Typical credit: 3-4 credits (US) 7.5 ECTS points (EU)

Please note: Assessment is optional but may be required for credit by your home institution. Your home institution will be able to advise how you can meet their credit requirements. For more information on exams and credit, read Teaching and assessment

This course is designed for students from various disciplines that use data, statistical and machine learning models to inform decision-making. The course is largely self-contained and reviews the necessary mathematical and statistics concepts used during the course. You should also consider taking this course if you are targeting a career in data science, insurance or finance. It is equally suited if you are a professional already working in industry or research organisations, looking to develop your understanding of this rapidly developing field.

• Understand key principles and theories in the generation of random variables, stochastic processes, Generative Adversarial Network models, Generalized Linear Models, and the calibration of machine learning models
• Critically evaluate the assumptions behind the models and methods covered in the course
• Apply an analytical approach to the complete modelling process, from selecting appropriate models and methods for a given research question to calibrating and implementing them using Python with synthetic or real-world data
• Accurately interpret, communicate, and discuss findings and ideas with both experts and a lay audience

The design of this course is guided by LSE faculty, as well as industry experts, who will share their experience and in-depth knowledge with you throughout the course.