TY  - JOUR
AU  - Ariza Garzón, Miller Janny
AU  - Arroyo Gallardo, Javier
AU  - Caparrini López, Antonio
AU  - Segovia Vargas, María Jesús
PY  - 2020
DO  - 10.1109/access.2020.2984412
SN  - 2169-3536
UR  - https://hdl.handle.net/20.500.14352/92059
T2  - IEEE Access
AB  - Peer-to-peer (P2P) lending demands effective and explainable credit risk models. Typical machine learning algorithms offer high prediction performance, but most of them lack explanatory power. However, this deficiency can be solved with the help of...
LA  - eng
M2  - 64873
PB  - IEEE
KW  - Credit risk
KW  - P2P lending
KW  - Explainability
KW  - Shapley values
KW  - Boosting
KW  - Logistic regression
TI  - Explainability of a Machine Learning Granting Scoring Model in Peer-to-Peer Lending
TY  - journal article
VL  - 8
ER  -