Credit Risk Model for loans to SMEs in Sweden : Calculating Probability of Default for SMEs in Sweden based on historical data, to estimate a financial institution’s risk exposure

Abstract: As a consequence from the last financial crisis that began 2007 in USA, regulatory frameworks are continuously improved in order to limit the banks’ risk exposure. Two of the amendments are Basel III and IFRS 9. Basel III regulates the capital a bank is required to hold while IFRS 9 is an accounting standard for how banks and insurance companies should classify their assets and estimate their future credit losses. Mutually for both Basel III and IFRS 9 is the estimation of future credit losses which include probability of default in the calculations.The objective of this thesis was therefore to develop scoring model that can estimate the probability of default in lending capital to enterprises based on information from financial statements. The aim is that the developed model also can be used in the daily operations to reduce fixed costs by optimizing the processes and increase the profit on each loan issued. The model should estimate probability of default within 500 days from the last known information and be customized for small and medium size enterprises.The model is based on logistic regression and is therefore returning values between 0 and 1. Parameters that the model consists of can either be calculated or retrieved directly from financial statements. The authors have during the development of the model divided the data, consisting of information from enterprises, based on branches. The grouping of data has been performed to create as homogenous sets of data as possible in order to increase the degree of explanation for each model. The final solution will thus consist of several models, one for each set of data. The validation of the models is performed, on a new set of enterprises where it is observed how well the models can discriminate enterprises defined as defaults from non-defaults.The master thesis did result in a number of models that are calibrated on default, non-defaults and models developed on data divided on branches. By using the calibrated models, it is possible to discriminate defaulting from non-defaulting enterprises which has been the objective of this thesis. During the project the importance of dividing data into homogenous groups has been shown in order to better create models that more accurately can identify defaults from non-defaults.