Ensemble Machine Learning for Credit Risk Prediction in Vietnam: Evidence from Loan-Level Bank Data
Ngày xuất bản: 2815-6234
Trang: The two types of classification error in credit scoring carry very different costs: a false negative
is far more expensive than a false positive. Using 57,187 loan-level records from a Vietnamese
commercial bank, of which 1.23% are non-performing, we evaluate six machine learning models
and four voting ensembles. Because missing a non-performing case is the costlier error, we
prioritise recall and the F2 score in model selection. We also separate application-time predictors
from post-origination information, evaluating each model against only the data available at its
decision stage. The behavioural model reaches an AUC of 0.992, reflecting concurrent
discrimination driven by accumulated loan tenure rather than early warning. Restricted to
origination information, discrimination falls to 0.82-0.87, yet the model still ranks risk usefully,
with the riskiest fifth of applicants containing about 72% of non-performing cases. Repayment-
related variables provide the most predictive information, and recall-oriented ensembles catch most
non-performing borrowers, with recall between 0.91 and 0.93. The practical message is that model
evaluation should be tied to the information available at each decision point and to the relative cost
of the errors that matter most.
| Issue: Journal of Development and Integration
Thời gian trích dẫn: The two types of classification error in credit scoring carry very different costs: a false negative
is far more expensive than a false positive. Using 57,187 loan-level records from a Vietnamese
commercial bank, of which 1.23% are non-performing, we evaluate six machine learning models
and four voting ensembles. Because missing a non-performing case is the costlier error, we
prioritise recall and the F2 score in model selection. We also separate application-time predictors
from post-origination information, evaluating each model against only the data available at its
decision stage. The behavioural model reaches an AUC of 0.992, reflecting concurrent
discrimination driven by accumulated loan tenure rather than early warning. Restricted to
origination information, discrimination falls to 0.82-0.87, yet the model still ranks risk usefully,
with the riskiest fifth of applicants containing about 72% of non-performing cases. Repayment-
related variables provide the most predictive information, and recall-oriented ensembles catch most
non-performing borrowers, with recall between 0.91 and 0.93. The practical message is that model
evaluation should be tied to the information available at each decision point and to the relative cost
of the errors that matter most.
Tóm tắt:
Ensemble Machine Learning for Credit Risk Prediction in Vietnam: Evidence from Loan-Level Bank Data
