Open Daily: 10am - 10pm | Alley door pickup 10am - 7pm 3038 Hennepin Ave Minneapolis, MN
612-822-4611

  • Open Daily: 10am - 10pm
    Alley-side Pickup: 10am - 7pm

    3038 Hennepin Ave Minneapolis, MN
    612-822-4611

Open Daily: 10am - 10pm | Alley-side Pickup: 10am - 7pm
3038 Hennepin Ave Minneapolis, MN
612-822-4611
Recent Methods from Statistics and Machine Learning for Credit Scoring

Recent Methods from Statistics and Machine Learning for Credit Scoring

Kraus, Anne

Paperback

Probability & Statistics

ISBN10: 3954047365
ISBN13: 9783954047369
Publisher: Cuvillier
Published: Jul 8 2014
Pages: 166
Weight: 0.45
Height: 0.35 Width: 5.83 Depth: 8.27
Language: English
Credit scoring models are the basis for financial institutions like retail and consumer credit banks. The purpose of the models is to evaluate the likelihood of credit applicants defaulting in order to decide whether to grant them credit. The area under the receiver operating characteristic (ROC) curve (AUC) is one of the most commonly used measures to evaluate predictive performance in credit scoring. The aim of this thesis is to benchmark different methods for building scoring models in order to maximize the AUC. While this measure is used to evaluate the predictive accuracy of the presented algorithms, the AUC is especially introduced as direct optimization criterion.

Also in

Probability & Statistics

View All