Santander Case Study

Can artificial intelligence be used to predict financial risk? UCL Computer Science students explored this project with Santander on the UCL Industry Exchange Network (UCL IXN) programme.

Santander has worked with UCL master's students for over six years through the UCL Industry Exchange Network (UCL IXN). UCL IXN enables students, as an integrated part of their course, to gain invaluable experience working with industry partners.

Santander takes on around 15 students a year from the Computational Finance and Financial Risk Management MSc courses. From June to August, the master's students work full-time on their dissertation projects, supported by a technical mentor within Santander and a supervisor at UCL. Their projects focus on computational and mathematical techniques applied to risk in banking. Recent subjects include sentiment analysis for credit rating, reverse stress testing using Gaussian processes and artificial data solutions for neural net imbalanced data.

UCL IXN brings clear benefits to the students, but the industry partners gain immensely from the programme too. Together, the students and Santander explore projects of mutual interest. The bank wants to investigate specific problems, and they may ask students to confirm an existing idea or tackle something completely new. Santander is impressed by the technical skills and enthusiasm of the UCL students, and the matching process that ensures they are a good fit for the bank. What's more, they are not only a useful resource at the time, but they are also potential employees.

Dr  Peter  Mitic - Head of Operational Risk Methodology UK, Santander UK 

To learn more about partnering with UCL IXN, contact UCL Computer Science's Strategic Alliances Team