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Meistaravörn við tækni- og verkfræðideild - Ingvar Ágúst Jochumsson

Feature Space Analysis with Unsupervised Learning for Credit Risk Assessment

  • 6.6.2019, 16:00 - 17:00

On Thursday June 6th at 16:00, Ingvar Ágúst Jochumsson will defend his 30 ECTS thesis in MSc Financial Engineering at Reykjavik University. The title of the thesis is Feature Space Analysis with Unsupervised Learning for Credit Risk Assessment, and the lecture will be held in room M208. All welcome.

Student: Ingvar Ágúst Jochumsson
Supervisor: Sverrir Ólafsson, RU
Co-supervisor: Stefán Freyr Guðmundsson
Examiner: Helgi Páll Helgason

Summary – Project Description:
Credit risk analysis is a widely researched topic and forms the foundation that aids in the decision making for numerous businesses around the world. However, with the increasing data availability and complexity it has become more difficult to identify key variables that best serve to distinguish between financially healthy and unhealthy firms. This thesis presents an approach to classify firms into homogeneous groups based on their characteristics by applying unsupervised learning techniques.

Using financial information, we train an autoencoder to perform dimensionality reduction and then proceed to apply and compare different clustering techniques on the reduced feature space. In addition, we compare the performance of our autoencoder with a more traditional approach. Our results show that by performing cluster analysis on a reduced space, constructed by an autoencoder, we can extract valuable relationships from relatively large data sets by partitioning the data objects based on their similarities. However, our results also indicate that unsupervised techniques perform very poorly in assessing defaulting probability of firms.

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