Document Type

Theses, Masters


This item is available under a Creative Commons License for non-commercial use only



Publication Details

A dissertation submitted in partial fulfillment of the requirements of Technological University Dublin for the degree of M.Sc. in Computer Science (Data Analytics)


The mortgage arrears crisis in Ireland was and is among the most severe experienced on record and although there has been a decreasing trend in the number of mortgages in default in the past four years, it still continues to cause distress to borrowers and vulnerabilities to lenders. There are indications that one of the main factors associated with mortgage default is loan affordability, of which the level of disposable income is a driver. Additionally, guidelines set out by the European Central Bank instructed financial institutions to adopt measures to further reduce and prevent loans defaulting, including the implementation and identification of Early Warning Indicators (EWIs). Financial institutions currently adopt credit risk models in order to calculate the risk associated with customers. Therefore, this research observed a cohort of mortgage customers in Lender A over a 30-month period and utilised transactional features, explaining the use of disposable income, to expand on existing credit risk models and aid in the identification of EWIs for the mortgage portfolio. Over the course of the study three feature selection techniques were adopted, namely correlation-based analysis, random forest feature importance and decision tree feature importance. A number of transactional categories were identified including insurance spend, gambling spend, savings and the value of ATM withdrawals. Furthermore, it was found that the inclusion of transactional features in existing credit risk models statistically improved performance.