Financial Distress and Bankruptcy Prediction

Neueste Paper

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

  Rishi Datta, Lavanya Prahallad · 1. September 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. August 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. August 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. August 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. August 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. August 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. August 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. Juli 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. Juli 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. Juli 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. Juni 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. Juni 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. Juni 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. Juni 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. Juni 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. Juni 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. Juni 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. Juni 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. Mai 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. Mai 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…

• Market Regime Council for Dynamic Credit Assignment in Multi-Agent LLM Decision Systems

  Yunhua Pei, Zerui Ge, Jin Zheng, John Cartlidge · 26. Mai 2026

  Multi-agent LLM decision systems for portfolio management still lack a principled way to assign credit across specialist agents, remain vulnerable to cold-start dominance under regime shifts, and offer limited transparency into how final allocations are formed. We propose Market Regime Council (MRC)…

• Data Presentation Over Architecture: Resampling Strategies for Credit Risk Prediction with Tabular Foundation Models

  Aditya Tanna, Mitul Solanki, Mohamed Bouadi, Nassim Bouarour, Pratinav Seth, Vinay Kumar Sankarapu · 19. Mai 2026

  Credit default prediction is a tabular learning problem with severe class imbalance, heterogeneous features, and tight latency budgets. Tabular Foundation Models (TFMs) approach this problem through in-context learning, which makes their predictions sensitive to how the context window is built. We b…

• Foundation Models for Credit Risk Prediction: A Game Changer?

  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, Tim Verdonck, Wouter Verbeke · 19. Mai 2026

  Predictive models play a pivotal role in credit risk management, guiding critical decisions through accurate estimation of default probabilities and losses. Extensive research has introduced new modeling techniques, complemented by large-scale benchmarking studies consolidating the state-of-the-art.…

• Comparative Evaluation of Machine Learning Approaches for Minority-Class Financial Distress Prediction Under Class Imbalance Constraints

  Karan Sehgal, Khawar Naveed Bhatti · 15. Mai 2026

  Financial distress prediction remains a significant challenge in enterprise risk analysis due to the highly imbalanced nature of real-world financial datasets, where bankrupt or distressed firms typically constitute only a small minority of observations. This paper presents a comparative evaluatio…

• V4FinBench: Benchmarking Tabular Foundation Models, LLMs, and Standard Methods on Corporate Bankruptcy Prediction

  Marcin Kostrzewa, Sebastian Tomczak, Roman Furman, Anna Poberezhna, Micha{\l} Furga{\l}a, Julia Farganus, Oleksii Furman, Maciej Zi\k{e}ba · 14. Mai 2026

  Corporate bankruptcy prediction is a high-stakes financial task characterized by severe class imbalance and multi-horizon forecasting demands. Public datasets supporting it remain scarce and small: widely used free benchmarks contain between 6,000 and 80,000 company-year observations, while larger r…