Developing a selection of credit scoring models based on customer data

Detta är en Master-uppsats från KTH/Matematisk statistik; KTH/Matematisk statistik

Författare: Thomas Eriksson; Tomas Petkov; [2019]

Nyckelord: ;

Sammanfattning: Consumer credits are becoming increasingly popular and widespread in Sweden, with many actors trying to establish themselves on the market. In this thesis, we develop a selection of quantitative models for credit scoring, based on logistic regression and decision trees. These models may be used to reduce the number of credits approved to customers who are likely to default, and are mainly intended for e.g. newly started credit institutes who lack a statistically rigorous credit approval process, relying instead on qualitative, subjective judgements.

  HÄR KAN DU HÄMTA UPPSATSEN I FULLTEXT. (följ länken till nästa sida)