TY  - CHAP
AB  - Abstract Traditionally, loan officers use different credit scoring models to complement judgmental methods to classify consumer loan applications. This study explores the use of decision trees, AdaBoost, and support vector machines (SVMs) to identify potential bad loans. Our results show that AdaBoost does provide an improvement over simple decision trees as well as SVM models in predicting good credit clients and bad credit clients. To cross-validate our results, we use k-fold classification methodology.
VL  - 20
SN  - 978-1-83867-001-6, 978-1-83867-000-9/0276-8976
DO  - 10.1108/S0276-897620200000020004
UR  - https://doi.org/10.1108/S0276-897620200000020004
AU  - Malhotra D. K.
AU  - Malhotra Kunal
AU  - Malhotra Rashmi
ED  - Kenneth D. Lawrence
ED  - Dinesh R. Pai
PY  - 2020
Y1  - 2020/01/01
TI  - Evaluating Consumer Loans Using Machine Learning Techniques
T2  - Applications of Management Science
T3  - Applications of Management Science
PB  - Emerald Publishing Limited
SP  - 59
EP  - 69
Y2  - 2024/04/24
ER  -