Developing an Advanced Internal Ratings-Based Model by Applying Machine Learning

Sammanfattning: Since the regulatory framework Basel II was implemented in 2007, banks have been allowed to develop internal risk models for quantifying the capital requirement. By using data on retail non-performing loans from Hoist Finance, the thesis assesses the Advanced Internal Ratings-Based approach. In particular, it focuses on how banks active in the non-performing loan industry, can risk-classify their loans despite limited data availability of the debtors. Moreover, the thesis analyses the effect of the maximum-recovery period on the capital requirement. In short, a comparison of five different mathematical models based on prior research in the field, revealed that the loans may be modelled by a two-step tree model with binary logistic regression and zero-inflated beta-regression, resulting in a maximum-recovery period of eight years. Still it is necessary to recognize the difficulty in distinguishing between low- and high-risk customers by primarily assessing rudimentary data about the borrowers. Recommended future amendments to the analysis in further research would be to include macroeconomic variables to better capture the effect of economic downturns.