--- title: Risk, Credit & Banking url: https://www.ml-quant.com/topics/risk-credit-banking/ site: ML-Quant (https://www.ml-quant.com) updated: 2026-09-26 license: Summaries CC BY 4.0; links go to the original sources index: https://www.ml-quant.com/llms.txt --- # Risk, Credit & Banking Credit risk, default prediction, banking, systemic risk and risk measures. 363 papers featured; 41 in the last 12 months. Papers featured per quarter: 2023 Q2 17, 2023 Q3 32, 2023 Q4 31, 2024 Q1 22, 2024 Q2 51, 2024 Q3 44, 2024 Q4 34, 2025 Q1 41, 2025 Q2 43, 2025 Q3 7, 2025 Q4 18, 2026 Q1 0, 2026 Q2 3, 2026 Q3 20 ## Most cited - [Attention-based Dynamic Multilayer Graph Neural Networks for Loan Default Prediction](https://www.ml-quant.com/papers/arxiv/2402.00299/): 49 citations. A dynamic multilayer network model has been created for improved credit risk assessment, considering borrower connections and their evolution over time. - [Moderating effects of gender and family responsibilities on the relations between work–family policies and job performance](https://www.ml-quant.com/papers/doi/10-1080-09585192-2018-1505762/): 28 citations. Research on the Spanish banking sector shows that work-family policies indirectly improve job performance through generated well-being, with no significant influence from gender or family responsibilities. - [Machine Learning Based Risk Assessment for Financial Management in Big Data IoT Credit](https://www.ml-quant.com/papers/ssrn/5086671/): 27 citations. The article highlights the importance of machine learning in evaluating financial management in big data and IoT in the credit industry, improving creditworthiness accuracy. - [Unleashing the power of text for credit default prediction: Comparing human-written and generative AI-refined texts](https://www.ml-quant.com/papers/arxiv/2503.18029/): 19 citations. The study shows that using AI language model, ChatGPT, in lending decisions can improve credit default predictions and increase profitability in finance. - [Explainable Automated Machine Learning for Credit Decisions: Enhancing Human Artificial Intelligence Collaboration in Financial Engineering](https://www.ml-quant.com/papers/arxiv/2402.03806/): 19 citations. The use of Explainable Automated Machine Learning (AutoML) in financial engineering can improve the development of machine learning models for credit scoring and increase transparency in AI financial decisions. - [Contagion Effects of the Silicon Valley Bank Run](https://www.ml-quant.com/papers/arxiv/2308.06642/): 19 citations. The study analyzes the impact of Silicon Valley Bank's failure on other banks, highlighting the role of uninsured deposits and bank size, with mid-sized banks being most affected. - [Failing Banks](https://www.ml-quant.com/papers/arxiv/2506.06082/): 18 citations. A study reveals that US bank failures from 1863 to 2024 are mainly due to worsening bank fundamentals like increasing asset losses and reliance on costly noncore funding. - [Neural Networks for Insurance Pricing with Frequency and Severity Data: A Benchmark Study from Data Preprocessing to Technical Tariff](https://www.ml-quant.com/papers/doi/10-1080-10920277-2025-2451860/): 16 citations. The article discusses the application of deep learning in insurance pricing, comparing different models and offering a method to interpret neural network insights through generalized linear models. - [Infinite-mean models in risk management: Discussions and recent advances](https://www.ml-quant.com/papers/arxiv/2408.08678/): 16 citations. The article explores the importance and challenges of using infinite-mean models in economics and finance, particularly when dealing with heavy-tailed datasets. - [Comparative Evaluation of Anomaly Detection Methods for Fraud Detection in Online Credit Card Payments](https://www.ml-quant.com/papers/arxiv/2312.13896/): 16 citations. A study found that LightGBM was the best for fraud detection when comparing anomaly detection and standard supervised learning methods, but it was more susceptible to distribution shifts, questioning the advantage of combining these two methods. - [Law-invariant return and star-shaped risk measures](https://www.ml-quant.com/papers/arxiv/2310.19552/): 16 citations. The paper introduces new characterizations for law-invariant star-shaped functionals, demonstrating their wide use in finance, insurance, and probability scenarios. - [Model Aggregation for Risk Evaluation and Robust Optimization](https://www.ml-quant.com/papers/arxiv/2201.06370/): 16 citations. The model aggregation (MA) approach is a new method for risk evaluation that provides a robust value and distributional model, refining Value-at-Risk and Expected Shortfall characterizations. ## Latest - [Financial Tail Risk Beyond Lipschitz Continuity via Semi-Discrete Optimal Transport](https://www.ml-quant.com/papers/arxiv/2609.27785/) (2026-09-25): Proposes semi-discrete optimal transport to capture heavy tails in financial returns, maintaining stable tail ratio estimates across diverse neural generators when standard Lipschitz methods fail. - [DefaultGNN: A Dual-Perspective GNN Framework for Predicting Corporate Default from Buyer-Seller Transaction Networks](https://www.ml-quant.com/papers/arxiv/2609.25542/) (2026-09-25): A dual-perspective graph neural network framework predicts corporate defaults from buyer-seller transaction networks, improving approval rates by 7-11 percentage points without increasing default risk. - [Risk Measures under Paired-Ambiguity: A Deep Learning Reflected BSDE Framework](https://www.ml-quant.com/papers/arxiv/2609.23768/) (2026-09-25): Develops a deep learning scheme for optimal stopping under simultaneous model and discount-rate ambiguity, with application to American option valuation under uncertainty. - [Forward Guidance and the Dynamics of Bank Credit: The Bank Balance-Sheet Channel of Monetary News](https://www.ml-quant.com/papers/ssrn/7514178/) (2026-09-25): High-frequency analysis reveals contractionary forward guidance immediately cuts bank lending, while expansionary guidance produces weak stimulus, driven by binding capital constraints. - [Monetary policy transmission by securitising banks](https://www.ml-quant.com/papers/ssrn/7515879/) (2026-09-25): Banks engaged in securitization contract lending more sharply after monetary tightening because their investor base demands higher returns and cuts risk exposure when rates rise. - [Hedge Fund Performance and Interest Rate Conditions: Evidence from Regulatory Data](https://www.ml-quant.com/papers/ssrn/7493702/) (2026-09-25): Using SEC filings from 2013-2021, the paper finds hedge fund returns show heterogeneous sensitivity to interest rates, with effects varying by strategy, leverage, and derivative exposure. - [State-dependent global banking systemic risk: An integrated framework of network connectedness, tail risk, and global financial conditions](https://www.ml-quant.com/papers/ssrn/7493706/) (2026-09-25): Combining quantile-connectedness, tail-risk measures, and network analysis, the research shows tail connectedness exceeds median levels and lower-tail effects persist longer, with the VIX alone reliably predicting next-week systemic risk. - [The Low Return Channel of Negative Interest Rates in Bank Lending](https://www.ml-quant.com/papers/ssrn/7489554/) (2026-09-25): Japan's 2016 negative-rate policy reduced lending from low-profitability banks holding reserves, consistent with lower expected returns on bank assets rather than deposit-side stress. - [Signature-Based Structural Models and Applications in Credit Markets](https://www.ml-quant.com/papers/ssrn/7498599/) (2026-09-25): The study develops a time-varying signature asset model for structural credit that improves calibration across CDS maturities and equity option prices, especially for high-yield firms. - [Sell, Hold Out, or Accept: The Creditor's Trilemma in Distressed Debt Exchanges](https://www.ml-quant.com/papers/ssrn/7502204/) (2026-09-25): Analysis of 284 distressed exchanges from 2009-2022 reveals over 50% of firms face subsequent default, with large illiquid creditors trapped in a prisoner's dilemma explaining high acceptance rates. - [The Global Credit Cycle](https://www.ml-quant.com/papers/repec/cpr-ceprdp-21268/) (2026-09-25): A nonlinear factor constructed from credit spreads and equity volatility prices global corporate bond returns, explaining up to 13% of three-month-ahead return variation across markets. - [The credit channel of monetary policy: direct survey evidence from UK firms](https://www.ml-quant.com/papers/repec/boe-boeewp-023260/) (2026-09-25): UK firm survey data validates that external borrowers face larger cost-of-capital increases and cut investment more than internal funders when rates rise, accounting for a quarter of monetary policy's total effect. - [Credit Card Banking](https://www.ml-quant.com/papers/repec/nbr-nberwo-35607/) (2026-09-25): Analysis of 550 million US credit card accounts shows that despite high charge-off rates, card lenders earn 1.5% alpha and 6.8% return on assets through pricing power and non-interest income. - [Bank Runs With and Without Bank Failure](https://www.ml-quant.com/papers/repec/nbr-nberwo-35504/) (2026-09-25): A database of 3,984 historical US bank runs shows runs are more likely in weak banks but often occur in strong banks; failures concentrate in fundamentally weak institutions. - [LASH Risk and Interest Rates](https://www.ml-quant.com/papers/repec/cpr-ceprdp-20158/) (2026-09-25): The study measures liquidity risk from solvency hedging in sterling repo and swaps, finding that pre-crisis LASH risk predicted pension fund bond sales during the 2022 UK market stress. - [Sovereign vs. Corporate Debt and Default: More Similar Than You Think](https://www.ml-quant.com/papers/repec/cpr-ceprdp-20100/) (2026-09-25): Analysis of 20 years of US junk bonds and emerging market sovereign debt reveals surprisingly similar average returns, Sharpe ratios, default frequencies, and haircuts across the two asset classes. - [Collateral policy surprises](https://www.ml-quant.com/papers/repec/zbw-bubdps-343110/) (2026-09-25): Expansionary central bank collateral policy surprises reduce bank default risk and volatility while compressing government bond spreads, transmitting effects distinctly from asset purchases. - [Pension Liquidity Risk](https://www.ml-quant.com/papers/repec/cpr-ceprdp-21095/) (2026-09-25): Dutch pension funds use interest rate swaps more aggressively when underfunded, exposing themselves to margin calls exceeding 6% of assets and forcing procyclical sales of government bonds. - [A theory of bank liquidity requirements](https://www.ml-quant.com/papers/repec/ecb-ecbwps-20263252/) (2026-09-25): The study develops a general equilibrium model of financial intermediation showing that liquidity regulation alone cannot achieve efficient allocations and requires complementary policies like bank size limits. - [Systemic at Home: the Persistence of a Too-Big-to-Fail Premium in Europe](https://www.ml-quant.com/papers/repec/dnb-dnbwpp-868/) (2026-09-25): European banks with assets exceeding half of home GDP enjoy at least 30 percent lower credit spreads, and this implicit subsidy persists and depends on sovereign fiscal strength. - [Lambda R{\'e}nyi entropic value-at-risk](https://www.ml-quant.com/papers/arxiv/2604.10657/) (2026-04-16): A New Measure: The article introduces the Lambda extension of Rényi entropic value-at-risk (Λ-EVaR), a new risk measure designed for better risk management by allowing adjustable confidence levels and sensitivity to higher moments. - [AI Agents in Financial Markets: Architecture, Applications, and Systemic Implications](https://www.ml-quant.com/papers/arxiv/2603.13942/) (2026-04-16): Recent AI advancements are enhancing financial automation by creating integrated systems that use autonomous agents for better decision-making and processing, highlighting the need for effective agent governance. - [Mean-field approximations in insurance](https://www.ml-quant.com/papers/arxiv/2511.04198/) (2026-04-16): A mean-field model simplifies complex insurance liabilities into manageable solutions, showing that large groups of interdependent individuals can be effectively analyzed in both life and non-life insurance scenarios. - [Asset Prices, Collateral and Bank Lending: The Case of COVID-19 and Real Estate](https://www.ml-quant.com/papers/ssrn/4470421/) (2025-12-28): The paper investigates the euro area's banking system's role in transmitting asset price shocks to credit during the Covid-19 crisis, highlighting significant frictions and a decrease in lending related to real estate collateral. - [Bias in Credit Ratings](https://www.ml-quant.com/papers/ssrn/4478090/) (2025-12-28): Subscription-based credit rating agencies may have biases that lead to overly optimistic ratings, complicating conflict resolution. - [Financial Fragilities and Risk-taking of Corporate Bond Funds in the Aftermath of Central Bank Policy Interventions](https://www.ml-quant.com/papers/ssrn/4463970/) (2025-12-28): It finds that central bank asset purchases during the pandemic led corporate bond fund managers to take more risks, affecting market stability. - [Financial Instruments for Decarbonization: Likely Pathways for the Romanian Economy](https://www.ml-quant.com/papers/ssrn/4440511/) (2025-12-19): The study highlights key financial tools in Romania, like green bonds and loans, which can help transition to a low-carbon economy, with banks playing a major role. - [Extending the application of dynamic Bayesian networks in calculating market risk: Standard and stressed expected shortfall](https://www.ml-quant.com/papers/arxiv/2512.12334/) (2025-12-19): The study enhances dynamic Bayesian networks for estimating expected shortfall, revealing that traditional models struggle in tail predictions and proposing methods for better forecasting. - [Optimal Investment, Consumption, and Insurance with Durable Goods under Stochastic Depreciation Risk](https://www.ml-quant.com/papers/arxiv/1903.00631/) (2025-12-14): An economic agent makes choices to maximize utility by adjusting consumption, investing in safe and risky assets, and insuring against losses on a depreciating good, using a strategy from the Hamilton-Jacobi-Bellman equation. - [Market Reactions and Information Spillovers in Bank Mergers: A Multi-Method Analysis of the Japanese Banking Sector](https://www.ml-quant.com/papers/arxiv/2512.06550/) (2025-12-14): This study analyzes how the market responds to major bank mergers in Japan, finding significant positive abnormal returns and lasting effects, indicating that banks benefit from synergies after merging.