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TZID:Asia/Krasnoyarsk
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DTSTART:20250101T000000
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BEGIN:VEVENT
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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BEGIN:VEVENT
DTSTART;TZID=Asia/Krasnoyarsk:20260930T150000
DTEND;TZID=Asia/Krasnoyarsk:20260930T160000
DTSTAMP:20260923T080235Z
CREATED:20260923T074345Z
LAST-MODIFIED:20260923T080235Z
UID:23427-1790780400-1790784000@vi.vnp.edu.vn
SUMMARY:Thesis Public Defense | VNP29 - Võ Thị Kim Nguyệt
DESCRIPTION:Shapelet Discovery in EU Carbon Prices: A Policy Lens for Vietnam \nStudent: Võ Thị Kim Nguyệt\, VNP-30 \nSupervisor: Dr. Trần Thị Tuấn Anh \nAbstract: \nThis study develops a shapelet-based framework for regime detection in the European Union Emissions Trading System (EU ETS). Analysis of 5\,217 daily EU Allowance (EUA) prices from 2005 to 2025 reveals nine distinct market regimes characterized by unique temporal patterns. The proposed methodology achieves 99.68% classification accuracy with an Adjusted Rand Index of 0.850\, substantially outperforming benchmark methods including Gaussian Mixture Model (GMM)\, K-Means\, DBSCAN\, Hidden Markov Models (HMM)\, ARIMA\, and Pruned Exact Linear Time (PELT). The approach identifies 104 discriminative shapelets across multiple temporal scales\, of which 53 achieve statistical significance through Kolmogorov-Smirnov testing with Bonferroni\ncorrection\, ensuring patterns represent genuine regime characteristics rather than spurious correlations.\n \nAnalysis reveals that discriminative patterns occur on average 18.3 days before external regime boundary labels in the historical dataset\, with regime-specific temporal offsets ranging from 12 to 28 days. Nine regimes are characterized: Phase I Formation (2005-2007)\, Phase II Stability (2008-2011)\, Phase III Launch (2013-2014)\, Backloading Anticipation (2014-2015)\, MSR Announcement (2015-2017)\, MSR Active (2018-2019)\, COVID-19 Shock (2020-2021)\, Post-COVID Recovery (2021-2023)\, and Post-COVID Volatility (2023-2025). A conceptual four-phase policy framework for\nVietnam’s carbon market development (2025-2035) is presented based on these findings.\n \nHowever\, all results are derived from in-sample classification without temporal traintest splits. Shapelets were discovered using the complete 2005-2025 dataset with full knowledge of all regime periods. The reported accuracy and temporal patterns represent retrospective classification capability\, not prospective forecasting performance. Out-ofsample validation through walk-forward testing is required before operational deployment. The Vietnam framework represents a conceptual application contingent on successful validation. \nKeywords: Carbon market\, EU ETS\, Shapelet analysis\, Time-series classification\, Regime detection\, Vietnam policy
URL:https://vi.vnp.edu.vn/event/thesis-public-defense-vnp29-v-th-kim-nguyet/
LOCATION:H.001\, 1A Hoàng Diệu\, Thành phố Hồ Chí Minh\, Viet Nam
CATEGORIES:THESIS PUBLIC DEFENSE
ATTACH;FMTTYPE=image/jpeg:https://vi.vnp.edu.vn/wp-content/uploads/2026/09/carbon-price.jpg
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