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DTSTART:20250101T000000
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DTSTART;TZID=Asia/Krasnoyarsk:20260819T160000
DTEND;TZID=Asia/Krasnoyarsk:20260819T170000
DTSTAMP:20260814T071221Z
CREATED:20260814T071221Z
LAST-MODIFIED:20260814T071221Z
UID:23319-1787155200-1787158800@vi.vnp.edu.vn
SUMMARY:Thesis Public Defense | VNP29 - Võ Ngọc Tường Vy
DESCRIPTION:Alternative behavioral data as screening signals: incremental predictive power and economic implications in credit scoring among thin-file borrowers in Vietnam \nStudent: Võ Ngọc Tường Vy\, VNP-29 \nSupervisor: Dr. Trương Đăng Thụy \nAbstract: \nThis thesis examines whether alternative behavioural-information proxies provide incremental predictive power beyond traditional credit-information proxies in screening small-value\, short-term consumer loans for thin-file and new-to-bank applicants in Vietnam. The anonymised and feature-masked data-set was provided by KCI Credit Information Joint Stock Company and contains 10\,023 unique applicant-originated-loan records. Predictors were measured at or before the scoring point\, while the dependent variable records whether the corresponding one-month loan subsequently reached 30 days past due (DPD30+) after being observed through maturity and for at least 30 additional days. The primary analysis uses unweighted Baseline and Extended Logistic Regression models. RF52 uses the same final 52 predictors as the Extended Logistic Regression and provides a clean algorithm-only robustness comparison. A separate supplementary RF92\nmodel uses all 92 screened source predictors to assess the broader KCI information environment; it is not used to identify the incremental contribution of the behavioural block. \nAfter diagnostic removal of one duplicated traditional predictor and twelve deterministically dependent behavioural predictors\, the Baseline model retained 18 predictors and the Extended model retained 52 predictors. On the ordered out-of-time test sample\, the Extended Logistic Regression increased AUC from 0.6515 to 0.6954 and KS from 0.2218 to 0.2999; the paired DeLong test confirmed that the AUC gain was statistically significant (z = 4.934\, p < 0.001). Within the thin-file proxy segment\, AUC increased from 0.5699 to 0.6449 and KS from 0.1300 to 0.2516. The Extended model also produced a lower Brier score and lower observed bad rates among approved loans at the evaluated predicted-risk cut-offs. Weighted Logistic Regression produced similar ranking results but was retained only as a supplementary sensitivity analysis. The findings indicate that governed behavioural information can complement traditional credit information\,\nparticularly where formal credit histories are sparse; they do not imply that alternative data should replace conventional credit assessment or that the estimated probabilities constitute\nregulatory PD measures. \nKeywords: alternative behavioural information; credit scoring; DPD30+; thin-file borrowers; new-to-bank; logistic regression; Vietnam; model governance
URL:https://vi.vnp.edu.vn/event/thesis-public-defense-vnp29-vo-ngoc-tuong-vy/
LOCATION:H.305\, 1A Hoang Dieu Street\, Phu Nhuan Ward\, Ho Chi Minh\, Viet Nam
CATEGORIES:THESIS PUBLIC DEFENSE
ATTACH;FMTTYPE=image/jpeg:https://vi.vnp.edu.vn/wp-content/uploads/2026/08/credit-scoring-la-gi-6.jpg
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