The paper presents a deep learning credit risk early warning system that fuses heterogeneous data sources, such as transaction behaviors and social networks, using deep neural networks and attention mechanisms. By extracting multidimensional features, the system establishes an early identification mechanism for corporate and individual credit risks. Testing shows that this approach improves the accuracy and timeliness of risk warnings compared to traditional rule‑based engines.
By LiYang Wang (Washington University in St. Louis), Zhen Zhong (Georgetown University), Zhen Tian (University of Glasgow), Keyu Chen (Wuyi University), Keyu Chen (Wuyi University)
arXiv:2605. 18147v2 Announce Type: replace Abstract: Predictive models play a pivotal role in credit risk management, guiding critical decisions through accurate estimation of default probabilities and losses.
By Bart Baesens, Andreas Goethals, Stefan Lessmann, Simon De Vos, Cristi\'an Bravo, David Martens, Victor Medina-Olivares, Christophe Mues, Maria Oskarsd\'ottir, Seppe vanden Broucke, Tony Van Gestel, Tim Verdonck, Wouter Verbeke
arXiv:2609.25542v1 Announce Type: new
Abstract: Corporate default prediction is a core problem in financial risk management, yet traditional credit models rely heavily on financial statements that ar...
By Junghoon Kim, Hyunsung Kim, Seungyoon Choi, KyoungYong Park, Jihun Lee, YongGu Ji, Chanyoung Park
arXiv:2608.20343v1 Announce Type: new
Abstract: This study develops and evaluates a bankruptcy prediction framework that integrates consensus-based feature selection, hybrid resampling, stacking ense...
By Obu-Amoah Ampomah, Edmund Fosu Agyemang, Kofi Acheampong, Louis Agyekum, Enock Adu Bonsu, Eric Nyarko
arXiv:2608. 12424v1 Announce Type: cross Abstract: This study focuses on developing an AI-supported prototype for multiperspective interest rate forecasting that combines classical econometric models with modern artificial intel-ligence methods.
By Ekkehardt Bauer, Dirk Holl\"ander, Linus Wolff, Christoph Ostermair, Kyrillus Aiad, Joachim Hasebrook
arXiv:2604. 04241v2 Announce Type: replace Abstract: Risk scoring systems are widely used in high-stakes domains to assist decision-making.
By Wenhao Chi, \c{S}. \.Ilker Birbil
arXiv:2608.28944v1 Announce Type: new
Abstract: Credit risk analysis in financial institutions traditionally requires analysts to manually write SQL queries, run statistical computations, and build v...
By Vennise Ho, Kristian Diana, Sandy Mourad, Milena Pilipovic, Vineel Nagisetty, Hossein Hajimirsadeghi
arXiv:2608.24582v1 Announce Type: cross
Abstract: Credit risk models increasingly need to combine predictive accuracy with transparent explanations and auditable fairness constraints. Logistic regres...
By Victor Medina-Olivares, Stefan Lessmann, Jonathan Crook
arXiv:2609.35864v1 Announce Type: new
Abstract: SEC 10-K filings contain substantial financial information that is not consistently captured in structured datasets, creating a missing-data problem af...
By Prisha Nair, Roee Shraga
arXiv:2607. 14174v1 Announce Type: new Abstract: Financial sentiment extraction has largely relied on news text and supervised extraction against return labels alone, leaving 10-K filings -- and volatility, the target risk disclosure is arguably best suited to informing -- comparatively unexplored.
By Sanggyu Sean Choi
Credit risk models increasingly need to combine predictive accuracy with transparent explanations and auditable fairness constraints. Logistic regression remains attractive because its coefficients ar...
arXiv:2607. 19259v1 Announce Type: cross Abstract: Financial statement fraud detection (FSFD) is crucial for market integrity but faces challenges from increasingly sophisticated schemes and under-utilized textual data in financial reports.
By Guy Stephane Waffo Dzuyo (Forvis Mazars, LORIA CNRS Universit\'e de Lorraine), Ga\"el Guibon (LORIA CNRS Universit\'e de Lorraine, LIPN CNRS Universit\'e Sorbonne Paris Nord), Christophe Cerisara (LORIA CNRS Universit\'e de Lorraine), Luis Belmar-Letelier (Forvis Mazars)