Financial Distress and Bankruptcy Prediction

Últimos artículos

• A Decision-Support Audit Protocol for Supervision Drift in Proxy-Labeled Credit-Risk Prediction

  Mehrdad Shoeibi, Muhammad Shabanpour, Waldemar Karwowski, Niloofar Yousefi · 16 de septiembre de 2026

  Credit-risk models are trained on proxy labels and deployed under temporal and segment change, yet no single transfer metric separates base-rate shift, probability-scale shift, and feature-label relationship change. We contribute a design-science artifact: a locked, multi-signal audit protocol for s…

• Design of a Deep Learning Credit Risk Early Warning System Integrating Multi-source Heterogeneous Data

  LiYang Wang (Washington University in St. Louis), Zhen Zhong (Georgetown University), Zhen Tian (University of Glasgow), Keyu Chen (Wuyi University), Keyu Chen (Wuyi University) · 15 de septiembre de 2026

  Advancements in data fusion and real-time analytics technologies have opened new avenues for addressing complex domain challenges. Financial risk early warning systems often suffer from inefficiency due to information silos and monitoring delays. This paper proposes a credit risk early warning syste…

• FINESSE: An Agent-Based Simulator and Benchmark Dataset for Multimodal Financial Event Sequences

  Tyler Farnan, Benjamin Eng, Adam Abate, Xirui Hou, Rizal Fathony, Nam H. Nguyen, Senthil Kumar · 14 de septiembre de 2026

  Machine learning research in financial services is limited by the scarcity of representative open-source datasets. Existing resources are often narrowly focused on a single modality or task and fail to reflect the structured, multimodal, and dynamic nature inherent to many problems in financial serv…

• Quantum Feature Engineering for Credit Default Prediction: When and Why IQP Circuits Help Linear Classifiers

  Menachem Finkelstein, Diana Legziel Levy, Zohar Yakhini, Sarel Cohen · 10 de septiembre de 2026

  Credit default prediction is a tabular classification problem in which modest gains in F1 translate directly into reduced financial exposure. We ask whether Instantaneous Quantum Polynomial-time (IQP) circuits can produce features that improve a classifier over both its raw classical baseline and Ke…

• Adversarial Training for Tabular Credit Scoring: A Multi-Attack Robustness Evaluation in P2P Lending

  Gijs A. F. Niewzwaag, Marijn G. S. Veth, Manuele Massei, Marcos R. Machado · 10 de septiembre de 2026

  Machine learning-based credit scoring is increasingly central to Peer-to-Peer (P2P) lending, yet its resilience to adversarial manipulation, where applicants strategically alter self-reported inputs to secure favourable decisions, remains poorly understood. Most adversarial-robustness evidence comes…

• When Does a Classifier Help an LLM? Classifier-Guided Prompting and Hybrid Classifier-LLM Models for Credit-Default Prediction

  Rishi Datta, Lavanya Prahallad · 1 de septiembre de 2026

  Credit-default prediction is an important task in financial decision making. Traditional methods use fitted classifiers such as logistic regression and random forests on tabular features. Large language models (LLMs) have recently been applied to this task through prompting. In this work we study ho…

• $\texttt{findr}$: Transparent and Fair Credit Risk Decisions through Semi-Structured Regressions

  Victor Medina-Olivares, Stefan Lessmann, Jonathan Crook · 26 de agosto de 2026

  Credit risk models increasingly need to combine predictive accuracy with transparent explanations and auditable fairness constraints. Logistic regression remains attractive because its coefficients are easy to interpret, but it can miss nonlinear structure. Flexible models can improve prediction, bu…

• Communicating Credit Risk with Large Language Models: Evaluation of Explanations from Standard and Alternative Data-Based Models

  Sahab Zandi, Noah Kostesku, Christophe Mues, María Óskarsdóttir, Cristián Bravo · 19 de agosto de 2026

  Credit decisioning is a high-stakes task in which model outputs must be accurate and explainable to support compliant decisions. Although modern credit risk models such as eXtreme Gradient Boosting (XGBoost) and Graph Neural Networks (GNNs) improve predictive performance, their explanations are ofte…

• Accurate Ensembles, Fragile Narratives: Multi-Scale Stacking and a Fidelity Audit of LLM-Generated Explanations for Credit Risk

  Gregorius Reynaldi Pratama, Kuo-Kun Tseng · 11 de agosto de 2026

  Credit scoring increasingly relies on models whose decision logic cannot be read off their parameters, in tension with supervisory expectations that adverse decisions be explainable. A common proposal closes that gap with a language model: compute feature attributions, hand them to an LLM, and let i…

• Beyond Aggregate Calibration: Decomposing Income-Conditional Recall Disparities in Automated Credit Default Prediction

  Sai Srikar Boddupalli · 11 de agosto de 2026

  Data-centric curation pipelines frequently rely on model confidence scores to flag and filter noisy or mislabeled training instances. Evaluating this filtering convention on a large-scale consumer lending sample (LendingClub, N = 1,344,936) uncovers an underlying demographic asymmetry: high-income d…

• Against Explainable Artificial Intelligence In Law: Why Justifiable Ai Matters. A Credit Scoring Example

  {\L}ukasz G\'orski · 10 de agosto de 2026

  Artificial intelligence-based solutions offer new efficiency-increasing possibilities in many applications, including credit scoring. Yet, the increasing sophistication of machine-learning models in use raises concerns regarding many of their aspects, explainability notwithstanding. We review the re…

• Counterfactual Analysis via Large Language Models

  Zonghao Yang · 7 de agosto de 2026

  Counterfactual analysis aims to predict potential outcomes under hypothetical scenarios, offering valuable insights for decision-making. This paper investigates the application of large language models (LLMs), specifically the GPT-3.5 model, for counterfactual analysis. We focus on the online lendin…

• The C-index illusion: discrimination without calibration in published survival models

  Rafael da Silva, Danilo Alvares · 23 de julio de 2026

  "Stop Chasing the C-index when Evaluating Survival Analysis Models" (ICML 2026, Spotlight) argued normatively, on synthetic data, that evaluating survival models by discrimination alone, i.e. the concordance index, produces systematically misleading model comparisons, because the metric ignores cali…

• Semantic Pareto-DQN: A Multi-Objective Reinforcement Learning Framework for Financial Anomaly Detection

  Cl\'audio L\'ucio do Val Lopes, Lucca Machado da Silva · 13 de julio de 2026

  Financial anomaly detection suffers from extreme class imbalance, causing traditional single-objective algorithms to exhibit ``fraud collapse'', defaulting to the majority class and failing to balance anomaly interdiction with customer friction. To overcome this without distortive data resampling, w…

• MASCA: LLM based-Multi Agents System for Credit Assessment

  Gautam Jajoo, Atharva Pandey, Pranjal A Chitale, Saksham Agarwal · 8 de julio de 2026

  Recent advancements in financial problem-solving have leveraged LLMs and agent-based systems, with a primary focus on trading and financial modeling. However, credit assessment remains an underexplored challenge, traditionally dependent on rule-based methods and statistical models. In this paper, we…

• Interpretable Machine Learning for Predicting Startup Funding, Patenting, and Exits

  Saeid Mashhadi, Amirhossein Saghezchi, Vesal Ghassemzadeh Kashani · 23 de junio de 2026

  This study develops an interpretable machine learning framework to forecast startup outcomes, including funding, patenting, and exit. A firm-quarter panel for 2010-2023 is constructed from Crunchbase and matched to U.S. Patent and Trademark Office (USPTO) data. Three horizons are evaluated: next fun…

• From Complaint Narratives to Monetary Relief: A Hybrid Machine Learning Framework for CFPB Consumer Complaints

  Zhuoer Wang, Sizhen Zhu, Xiongyu Chen · 23 de junio de 2026

  Consumer financial complaints provide a valuable source of information for identifying service failures, dispute frictions, and operational deficiencies in consumer-facing financial institutions. This paper proposes a hybrid machine learning framework for predicting monetary relief outcomes using Co…

• MortarBench: Evaluating Mortgage Loan Origination Agents

  Matthew Toles, Yunan Lu, Manav Munjal, Bojun Liu, Yuanhao Deng, Stephanie Selig, Derek Rindner, Cheng Li, Zhou Yu · 19 de junio de 2026

  Loan origination is the process by which a lender creates a new loan, from application and underwriting through approval and funding. This process serves a critical role in evaluating the eligibility and level of risk posed by an applicant. Recently, firms have begun using mortgage loan agents to au…

• DeXposure-Claw: An Agentic System for DeFi Risk Supervision

  Aijie Shu, Bowei Chen, Wenbin Wu, Cathy Yi-Hsuan Chen, Fengxiang He · 19 de junio de 2026

  Decentralized finance exposes supervisors to fast-moving, networked credit risks. General-purpose LLM agents fit this setting poorly: they over-read weak evidence and recommend high-stakes interventions, while existing evaluations offer no regulator-aligned way to measure the resulting false alarms.…

• The Illusion of Improvement: Reject Inference Strategies in Credit Scoring

  Bruno Scarone, Ricardo Baeza-Yates · 18 de junio de 2026

  Reject inference methods are widely used to mitigate survival bias in credit scoring, yet their effectiveness remains poorly understood. We systematically evaluate several such methods and uncover a structural failure mode: in a natural retraining cycle, models whose accuracy improves while recall c…

• Privacy-Preserving Credit Risk Prediction with Alternative Data

  Hongzhe Zhang, Jiarong Xu, Jing He, Xiao Fang · 10 de junio de 2026

  Credit risk prediction is a critical problem in the consumer credit industry. Traditionally, financial institutions construct credit risk prediction models using borrowers' demographic, financial, and credit history data, collectively referred to as traditional data. Recent studies have demonstrated…

• TRUST-SCF: Transformer-based Risk Understanding and Scoring for Transactional Supply Chain Finance

  Mohammadamin Davoodabadi, Amirabbas Shakeri · 9 de junio de 2026

  Supply Chain Finance (SCF) and LendTech platforms need credit scoring systems that respond to evolving transaction behavior, repayment delays, and active exposure. We propose TRUST-SCF, a transformer-based framework for transaction-level risk prediction and dynamic credit scoring. Each user history …

• Score Broadcast and Decorrelation: A General Framework for Broadcast-Based Credit Assignment

  Mustafa Uzun, Mete Erdogan, Cengiz Pehlevan, Alper T. Erdogan · 1 de junio de 2026

  We introduce Score Broadcast and Decorrelation (SBD), a principled framework for broadcast-based credit assignment for general families of differentiable losses. Error broadcast is a biologically plausible alternative to backpropagation that sends output information to hidden layers without weight t…

• Evolutionary Rule Extraction from Corporate Default Prediction Models

  Desir\`e Fabbretti, Matteo Pasquino, Elia Pacioni, Caterina Lucarelli, Davide Calvaresi · 29 de mayo de 2026

  Small and medium-sized enterprises (SMEs) represent the majority of firms in most economies and often face financial constraints and higher vulnerability to financial distress. Predicting SME default is therefore crucial for financial institutions, policymakers, and researchers. Recent advances in m…

• The Role of Causal Features in Strategic Classification for Robustness and Alignment

  Antonio Gois, Sophia Gunluk, Nir Rosenfeld, Nidhi Hegde, Simon Lacoste-Julien, Dhanya Sridhar · 27 de mayo de 2026

  In strategic classification, an institution (e.g., a bank) anticipates adaptation from users who change their features to increase utility in a classification task (e.g., loan repayment). Since a key challenge is the distribution shift induced by users, we turn to causal models, which have been show…