TY - JOUR
T1 - Predicting loss given default of unsecured consumer loans with time-varying survival scores
AU - Li, Aimin
AU - Li, Zhiyong
AU - Bellotti, Anthony
N1 - Publisher Copyright:
© 2023 Elsevier B.V.
PY - 2023/4
Y1 - 2023/4
N2 - Loss Given Default (LGD) is an essential element in effective banking supervision, as set out in the Basel Accords. In this paper, we focus on improving LGD predictions with the help of time-varying covariates. Based on online unsecured consumer loan data, we first build application scores with a Cox proportional hazard model, and behavioral scores with a multiplicative hazard model. We add these time-varying survival scores to fit the specifications of four separate LGD models - Tobit regression, decision trees, Logit-transformed linear regression and Beta regression. It is shown that better LGD predictions can be achieved when both application and behavioral scores are incorporated. Our framework further facilitates the prediction of expected loss, which can produce loss estimates at any time during the repayment period. Our experiment shows that the loss estimates are accurate, though some inherent errors cannot be avoided.
AB - Loss Given Default (LGD) is an essential element in effective banking supervision, as set out in the Basel Accords. In this paper, we focus on improving LGD predictions with the help of time-varying covariates. Based on online unsecured consumer loan data, we first build application scores with a Cox proportional hazard model, and behavioral scores with a multiplicative hazard model. We add these time-varying survival scores to fit the specifications of four separate LGD models - Tobit regression, decision trees, Logit-transformed linear regression and Beta regression. It is shown that better LGD predictions can be achieved when both application and behavioral scores are incorporated. Our framework further facilitates the prediction of expected loss, which can produce loss estimates at any time during the repayment period. Our experiment shows that the loss estimates are accurate, though some inherent errors cannot be avoided.
KW - Application scoring
KW - Behavioral scoring
KW - Expected loss
KW - Loss Given Default
KW - Survival analysis
UR - http://www.scopus.com/inward/record.url?scp=85147357473&partnerID=8YFLogxK
U2 - 10.1016/j.pacfin.2023.101949
DO - 10.1016/j.pacfin.2023.101949
M3 - Article
AN - SCOPUS:85147357473
SN - 0927-538X
VL - 78
JO - Pacific Basin Finance Journal
JF - Pacific Basin Finance Journal
M1 - 101949
ER -