Explaining neural networks used for modeling credit risk
Date
2021-03
Authors
Journal Title
Journal ISSN
Volume Title
Publisher
Stellenbosch : Stellenbosch University
Abstract
ENGLISH ABSTRACT: Calculating risk before providing loans is a common problem that credit companies
face. The most common solution is credit employees manually assessing the risk of
a client by reviewing their credit portfolios. This can be a slow process and is prone
to human error. Recently credit companies have been adopting machine learning
techniques in order to automate this process, however this has been limited to
linear techniques due to interpretability being a strict requirement. Neural networks
could provide significant improvements to the way credit risk is modeled, however
these are still seen as black boxes. In this work we compare various techniques
which claim to provide interpretability into these black boxes. We also use these
techniques to provide explanations on a neural network trained on credit data that
has been provided to us.
Description
Thesis(MSc.)--Stellenbosch University, 2021.
Keywords
UCTD, Machine Learning, Neural networks (Computer science), Credit control -- Automation