arXiv Machine Learning By Ujjwala Vadrevu

SHARC: SHAP-Based Interpretability in Machine Learning Risk Models for Regulatory Capital under ICAAP and CCAR

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arXiv:2607. 05484v1 Announce Type: cross Abstract: The adoption of non-parametric machine learning models for regulatory capital estimation introduces a fundamental governance challenge: the inability to explain model outputs in a manner auditable by supervisory bodies.

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arXiv Machine Learning
Jun 9

Forward-Looking Stress Testing Under Macro Scenarios: Stable SVaR Estimation Using a Hybrid GPR-HS Framework with SACS

arXiv:2606. 07575v1 Announce Type: cross Abstract: Regulatory stress testing frameworks, including the Comprehensive Capital Analysis and Review (CCAR) and the Internal Capital Adequacy Assessment Process (ICAAP), require robust Stressed Value-at-Risk (SVaR) estimation under forward-looking macroeconomic scenarios.

By Ujjwala Vadrevu
arXiv AI
Aug 24

GroupSegment-SHAP: Shapley Value Explanations with Group-Segment Players for Multivariate Time Series

GroupSegment-SHAP (GS‑SHAP) introduces explanatory units called group‑segment players that capture cross‑variable dependence and distribution shifts over time in multivariate time‑series models. By attributing Shapley values to these units, GS‑SHAP preserves joint structural signals that traditional time‑series SHAP variants fragment. Experiments on human activity recognition, power‑system forecasting, medical signal analysis, and financial time series show that GS‑SHAP improves deletion‑based faithfulness by about 1.7× and reduces runtime by roughly 40% compared to existing baselines, while a financial case study demonstrates its ability to reveal interpretable multivariate‑temporal interactions during high‑volatility periods.

By Jinwoong Kim, Sangjin Park
arXiv Machine Learning
Jun 16

Machine Learning and the Random Walk Puzzle: Forecasting the CAD/USD Exchange Rate with Expanding Window Evaluation and SHAP Interpretability

arXiv:2606. 15058v1 Announce Type: new Abstract: This study examines whether machine learning (ML) models can outperform the naive random walk benchmark in forecasting the monthly USD/CAD exchange rate.

By Louis Agyekum, Edmund Fosu Agyemang, Obu-Amoah Ampomah, Kofi Acheampong, Emmanuel Boadi, Priscilla Yaa Amakye, Fafa Shalom Tchorly, Enock Adu Bonsu, Eric Nyarko
arXiv AI
Jun 3

Auditable Climate Risk Intelligence from Fragmented ESG Data: Deterministic Orchestration and Imbalance-Aware Learning for Scope 1-3 Validation

arXiv:2606. 02604v1 Announce Type: cross Abstract: ESG and climate risk data remain fragmented across heterogeneous Scope 1, Scope 2, and Scope 3 reporting environments, while conventional validation pipelines lack provenance aware auditability, hidden drift detection, and reproducibility oriented governance.

By Karan Sehgal, Khawar Naveed Bhatti