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Data Science

Machine Learning for Financial Risk Management

Applying machine learning to uncertainty quantification and financial risk management.

17.5h (5 days)

€1,470

Face-to-face

English

Next edition: July 5-9, 2027

Early Bird deadline: April 15, 2027

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Data Science

Machine Learning for Financial Risk Management

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Who is this Course for?

Data Science in Finance

Machine Learning for Financial Risk Management

Course overview

Machine learning is playing an increasingly important role in financial risk management, but accurate predictions are only part of the picture. Understanding the uncertainty around those predictions is essential for assessing risk and making informed decisions.

This intensive five-day course explores how data science and machine learning methods can be used to quantify uncertainty and support financial risk management. Participants will learn about statistical learning, model evaluation and selection, distributional prediction, quantile models, and conformal prediction, with applications including default probability estimation, value-at-risk, and the development of financial stress indicators.

Combining morning lectures with interactive afternoon sessions, the course offers practical experience applying machine learning techniques to financial risk management problems. It is designed for participants with a solid quantitative background who want to strengthen their ability to develop, evaluate, and apply models in a financial context.

Teaching Faculty

Who is this course for?

This course is designed for:

Master’s or PhD students in Finance or Economics
Finance professionals with a strong quantitative background

Learning outcomes

Participants of this course will:

Be able to build models to forecast outcomes of interest for financial risk managers.
Be familiarized with some of the key methods used by financial data scientists
Have hands-on training on tackling financial risk management problems with machine learning methods

Key topics for Machine Learning for Financial Risk Management course

Here is a list of topics and themes that will be covered during the course.

Key Topics

Statistical learning: regression & regularisation
Model evaluation & selection
Distributional prediction, quantile models
Conformal prediction

List of References

Participants will be provided with lecture slides, example notebooks, and suggestions for further reading during the course

Teaching Methods

The course will be taught through a mix of lectures and hands-on practicals with examples in R.

Why join our Summer School?

All BSE Summer courses are taught to the same high standard as our Master’s programs. Join us to:

1

Network with like-minded peers

2

Study in vibrant Barcelona

3

Learn from world-renowned faculty

Admissions and Requirements

It is the participant’s responsibility to ensure they meet the admissions criteria.

Next edition: July 5-9, 2027

Early Bird deadline: April 15, 2027

Requirements

Summer School applicants normally demonstrate one or more of the following:

A strong background in Economics or a field closely related to the course topic (Statistics, Law, etc.)
Postgraduate degree or current Master’s/PhD studies related to the course topic
Relevant professional experience

Requirements for Machine Learning for Financial Risk Management

Postgraduate degree or current Master’s/PhD studies related to the course topic or relevant professional experience
Students should have a solid background in probability and statistics at the level of a Master’s program in economics, finance, statistics, or a related quantitative field. This includes, for example, linear and logistic regression, hypothesis testing, and quantiles.
Students should be familiar with the fundamentals of programming in R

Schedule

Here is your schedule for this edition of BSE Data Science in Finance Summer School, Machine Learning for Financial Risk Management course.

Time

5

mon

6

tue

7

wed

8

thu

9

fri

09:00 - 11:00

Lecture

14:30 - 16:00

Practical

Credit Transfers (ECTS)

To be eligible for credit transfer, students must complete a final project. Students will deliver a short final project one week after the summer school finishes. It will consist in solving a final problem that will include the practical and empirical issues worked on in class.

Please consult the Summer School Admissions page for more information about this option.

Certificate of Attendance

Participants who attend more than 80% of the course will receive a Certificate of Attendance, free of charge.

Course

Large Language Models in Finance

Machine Learning for Financial Risk Management

Modality

Face-to-face

Total Hours

17.5

ECTS

1

Regular Fee

€1,470

Reduced Fee*

€840

*Reduced Fee applies for PhD or Master’s students, Alumni of BSE Master’s programs, and participants who are unemployed.

Multiple discounts are available.

For more information on discounts for participants funding their own course, please click here.

For more information on discounts for companies and institutions enrolling their employees, please click here.

Full discount conditions are available here.

FAQ

Need more information? Check out our most commonly asked questions or contact our Admissions Team.

Can I see the full Summer School calendar?

You can view the full Summer School calendar here.

Is accommodation included in the course fee?

Unfortunately, accommodation is not included in the course fee. Participants are responsible for finding accommodation.

Are the sessions recorded?

Sessions will NOT be recorded; however, the materials provided by the professor will be available for a month after the course has finished.

How much does each Summer School course cost?

Fees for each course may vary. Please consult each course page for accurate information.

Are there any discounts available?

Yes, BSE offers a variety of discounts on its Summer School courses. See more information about available discounts or request a personalized discount quote by email.

Can I take more than one course?

Yes! you can combine any of the Summer School courses (schedule permitting). See the full course calendar.

Credit Transfers (ECTS)

To be eligible for credit transfers, students must complete a final project.

Students will deliver a short final project one week after the summer school finishes. It will consist in solving a final problem that will include the practical and empirical issues worked on in class.

Consult the Summer School Admissions page for more information about this option.

Cancelation and Refund Policy

Please consult BSE Summer School policies for more information.

Are there any evening activities during the course?

A social dinner will take place during the week for all participants, and it is free to attend.

Contact our Admissions Team

Mix and match your summer courses!

Remember that you can combine this program with courses in any of the other BSE Summer School programs (schedule permitting). Maximise your learning this summer and take advantage of our multiple-course discount.

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