Paper proposes an intelligent credit limit management system using causal inference.
problem Traditional credit limit management strategies are heuristic and not data-driven.
method Conditional independence testing, response model, log transformation, GBDT encoding, non-linear transformation on features, well-designed metric.
result The proposed approach effectively manages credit limits and incorporates diminishing marginal effects.
The use of CVA to cover credit risk is widely spread, but has its limitations. Namely, dealers face the problem of the illiquidity of instruments used for hedging it, hence forced to warehouse credit risk. As a result, dealers tend to offer a limited OTC derivatives market to highly risky counterparties. Consequently, …
Study uses generative models to assess credit risk and determine loan sizes in e-commerce supply chain finance.
problem Credit risk assessment and loan size determination for small- and medium-sized sellers in e-commerce supply chain finance.
method Proposes a unified framework using Quantile-Regression-based Generative Metamodeling (QRGMM) integrated with Deep Factorization Machines (DeepFM) to capture complex covariate interactions in e-commerce sales data.
result Validates the model's efficacy for credit risk assessment and loan size determination on synthetic and real-world data.
Study evaluates SHAP for credit card default model consistency.
problem Model transparency and fairness in credit card default prediction models.
method Evaluates SHAP stability in credit card default prediction models via a case study.
result SHAP consistency is related to variable importance level.
AI enhances bank credit risk management through deep learning and data analysis.
problem Inaccurate credit decisions and potential risks in bank credit risk management.
method Innovative application of AI technology, including deep learning and big data analysis.
result AI provides more accurate and comprehensive credit decision support, reducing risks and losses.
Study shows how to better estimate credit provisions and economic capital.
problem Estimating credit provisions and economic capital accurately.
method Using supermodularity ordering properties and elliptically distributed latent factors.
result Convex risk measures of credit losses are nondecreasing w.r.t. various covariances.
Study uses RL to optimize credit card limits, achieving better results than traditional methods.
problem Optimizing credit card limit adjustments in banking.
method Reinforcement learning with offline learning strategy.
result Double Q-learning agent outperforms other strategies in generating optimal policy.
A scalable model estimates revenue uncertainty for SMEs.
problem Estimating revenue uncertainty for SMEs to manage credit limits.
method Scalable Natural Gradient Boosting Machines.
result The method distinguishes accurate from inaccurate revenue forecasts.
Bayesian and simulation methods predict credit default probabilities.
problem Assessing credit risk in large customer portfolios.
method Two-phase approach: Bayesian estimation followed by Monte Carlo simulations.
result Estimation of true default rates through simulations.
Credibility theory provides tools to obtain better estimates by combining individual data with sample information. We apply the Credibility theory to a Uniform distribution that is used in testing the reliability of forecasting an interest rate for long term horizons. Such empirical exercise is asked by Regulators (CRR…
We propose a hybrid model of portfolio credit risk where the dynamics of the underlying latent variables is governed by a one factor GARCH process. The distinctive feature of such processes is that the long-term aggregate return distributions can substantially deviate from the asymptotic Gaussian limit for very long ho…
MRC improves credit assignment in multi-agent LLM systems, achieving high returns and transparency.
problem Lack of principled credit assignment in multi-agent LLM decision systems, vulnerability to regime shifts, and limited transparency.
method Market Regime Council (MRC) computes exact Shapley credits, uses exponentially weighted performance histories, Bayesian adaptive mixture, and regime-dependent multipliers.
result MRC achieves a Sharpe ratio of 1.51 and a cumulative return of 440.1% over 1,037 trading days, ranking first on CR, SR, and IR.
AI framework predicts invoice dilution in supply chain finance.
problem Invoice dilution risk in supply chain finance.
method AI, machine learning, dynamic credit limits, real-time projections.
result Supplemental AI model improves prediction accuracy.
Credit risk management in Italy is characterized, in the period June 2008 to June 2012, by frequent (frequency=0.5 cycles per year) and intense (peak amplitude: mean=39.2 billion Euros, s.e.=2.83 billion Euros) quarterly contractions and expansions around the mean (915.4 billion Euros, s.e.=3.59 billion Euros) of the n…
Modeling firm default with a variable threshold based on management decisions.
problem Estimating default probability with asymmetric information.
method Generalized structural model with a variable default threshold.
result The information level significantly impacts default probability and credit yield spread.
This paper enhances credit risk management using explainable AI techniques.
problem Lack of transparency and explainability in AI models for credit risk management.
method Implement LIME and SHAP for explaining ML-based credit scoring models.
result Demonstrates practical challenges and solutions for XAI methods in finance.
Transfer learning improves loan recovery rate forecasting under data scarcity.
problem Data scarcity in loan portfolios limits RR modeling accuracy.
method Introduces FT-MDN-Transformer, a mixture-density tabular Transformer architecture for TL.
result FT-MDN-Transformer outperforms baseline models in RR forecasting, especially under covariate and conditional shifts.
This paper builds a machine learning model to predict credit defaults for unsecured lending.
problem High credit defaults and delinquency rates in unsecured lending due to imbalanced data.
method Employing machine learning techniques, particularly SMOTE for imbalanced data, and evaluating models like LGBM Classifier.
result LGBM Classifier model outperforms other models in predicting credit defaults.
We give a detailed account of correlations between credit sector/quality and treasury curve factors, using the robust framework of the Barclays POINT Global Risk Model. Consistent with earlier studies, we find a strong negative correlation between sector spreads and rate shifts. However, we also observe that the correl…
Enhances credit card limit adjustments by considering treatment uncertainty and prediction criteria.
problem Optimal treatment selection under multitreatment scenarios.
method Proposes a comprehensive methodology incorporating conditional value-at-risk and prediction criterion for continuous outcomes.
result Significantly improved policy performance in credit card limit adjustments.
New RBM model outperforms copula models in credit risk management.
problem Approximating credit portfolio losses accurately and efficiently.
method Restricted Boltzmann Machines for universal approximation of loss distributions.
result RBM model outperforms parametric copula models in various credit risk tasks.
We study the problem of finding the worst-case joint distribution of a set of risk factors given prescribed multivariate marginals and a nonlinear loss function. We show that when the risk measure is CVaR, and the distributions are discretized, the problem can be conveniently solved using linear programming technique. …
The paper analyzes XVA reduction strategies in financial crises using Mandatory Breaks, Restructuring, and Resets.
problem Challenges in client XVA management during crises when continuous collateralization is not feasible.
method Compares multiple trade strategies including Mandatory Breaks, Restructuring, and Resets.
result Resets can be twice as effective as Mandatory Breaks/Restructuring if there is no credit recovery. When recovery is at least 1/3, Mandatory Breaks/Restructuring can be more effective.
Optimizes liquidations in decentralized finance to manage credit risk.
problem Managing and liquidating positions in decentralized finance exchanges.
method Formulated as an ergodic optimal control problem, derived closed-form solutions for optimal liquidation strategies.
result Closed-form solutions balance immediate executions with price impacts and long-term rewards.
We propose a diffusion process to describe the global dynamic evolution of credit operations at a national level given observed operations at a subnational level in a sovereign country. Empirical analysis with a unique dataset from Brazilian federate constituents supports the conclusions. Despite the heterogeneity obse…
This study improves credit risk management using advanced reinforcement learning.
problem Sub-optimal hedging of credit losses due to bid-ask costs and model limitations.
method Risk-averse stochastic-horizon reinforcement learning for dynamic risk management.
result Efficacy demonstrated through numerical study of a single FX forward contract portfolio.
For credit risk management purposes in general, and for allocation of regulatory capital by banks in particular (Basel II), numerical assessments of the credit-worthiness of borrowers are indispensable. These assessments are expressed in terms of probabilities of default (PD) that should incorporate a certain degree of…
The basic financial purpose of an enterprise is maximization of its value. Trade credit management should also contribute to realization of this fundamental aim. Many of the current asset management models that are found in financial management literature assume book profit maximization as the basic financial purpose. …
This paper proposes a deep learning model combining CNN and Transformer for improved credit default prediction.
problem Traditional machine learning models struggle with complex financial data and risk patterns.
method Combines CNN for local feature extraction and Transformer for global dependency modeling.
result The CNN+Transformer model outperforms traditional models in accuracy, AUC, and KS value.
Hybrid QML model improves recovery rate prediction accuracy.
problem Complex nonlinear dependencies, high-dimensional feature spaces, and limited sample sizes in recovery rate forecasting.
method Hybrid Quantum Machine Learning (QML) with Amplitude Encoding, leveraging PQC and qubit data compression.
result Significantly lower RMSE (0.228) compared to classical models.
The paper examines how decentralized credit curators have taken over risk management from traditional protocols.
problem Risk management in decentralized credit has shifted from centralized protocols to decentralized curators.
method Analysis of ERC 4626 vaults and third-party curators, focusing on capital utilization, concentration, and fee margins.
result Curators have a significant impact on the risk profile of decentralized credit systems, with a small set of curators handling a disproportionate share of system TVL.
Deep Evidence Regression improves credit risk prediction uncertainty.
problem Quantifying uncertainty in credit risk predictions.
method Applying Deep Evidence Regression to credit risk settings.
result Demonstrated improved prediction of Loss Given Default.
New method reduces CVA-VaR computation complexity.
problem Efficiently estimating CVA-VaR for financial risk management.
method Multilevel nested simulation for probabilities.
result 3 orders of magnitude reduction in computational complexity.
We propose a unified framework for equity and credit risk modeling, where the default time is a doubly stochastic random time with intensity driven by an underlying affine factor process. This approach allows for flexible interactions between the defaultable stock price, its stochastic volatility and the default intens…
Derives metrics for DeFi vaults, addressing credit risk.
problem Credit risk in DeFi lending vaults.
method Three-level decomposition of vault risk; six structural features identified.
result Estimation architecture for credit risk metrics.
The credit crisis of 2007 and 2008 has thrown much focus on the models used to price mortgage backed securities. Many institutions have relied heavily on the credit ratings provided by credit agency. The relationships between management of credit agencies and debt issuers may have resulted in conflict of interest when …
The classical reduced-form and filtration expansion framework in credit risk is extended to the case of multiple, non-ordered defaults, assuming that conditional densities of the default times exist. Intensities and pricing formulas are derived, revealing how information driven default contagion arises in these models.…
Productivity and credit limits affect aggregate production in non-monotonic ways.
problem Understanding how aggregate production is influenced by individual characteristics and financial constraints.
method Analytical proof of non-monotonic effects of productivity and credit limits on aggregate production in a general equilibrium model.
result Equilibrium aggregate production can be non-monotonic in both individual productivity and credit limit.
This paper provides an alternative approach to Duffie and Lando [Econometrica 69 (2001) 633-664] for obtaining a reduced form credit risk model from a structural model. Duffie and Lando obtain a reduced form model by constructing an economy where the market sees the manager's information set plus noise. The noise makes…
Quantum MC simulations generate financial risk distributions efficiently.
problem High computational cost in traditional Monte Carlo simulations.
method Integrates quantum amplitude estimation with stochastic models for equity, rate, and credit risk factors.
result Quantum advantage in scenario generation for financial risk analytics.
We study the pricing of credit derivatives with asymmetric information. The managers have complete information on the value process of the firm and on the default threshold, while the investors on the market have only partial observations, especially about the default threshold. Different information structures are dis…
Paper compares AI models for credit scoring and explains them.
problem Lack of interpretability in advanced AI models hinders credit risk management.
method Comparison of logistic regression, AI algorithms, and techniques to interpret AI models.
result Advanced tree-based models provide the best prediction of client default.
Paper classifies institutions based on credit, debit, and funding adjustment paradigms.
problem Classifying institutions based on credit, debit, and funding adjustment paradigms.
method Mathematical framework based on the principle of invariance.
result Improved solution of principle of invariance equations for accurate metrics calculation.
We present a model of credit card profitability, assuming that the card-holder always pays the full outstanding balance. The motivation for the model is to calculate an optimal credit limit, which requires an expression for the expected outstanding balance. We derive its Laplace transform, assuming that purchases are m…
We use a simple agent based model of value investors in financial markets to test three credit regulation policies. The first is the unregulated case, which only imposes limits on maximum leverage. The second is Basle II and the third is a hypothetical alternative in which banks perfectly hedge all of their leverage-in…
Large corporate credit models may be adapted for small business risk assessment.
problem Limited data and lack of credit analysts for small businesses.
method Adapting large corporate credit risk models for small businesses.
result Adapted models can predict small business credit risk effectively.
Study uses neural networks to predict credit risk in banks.
problem Credit risk management in commercial banks.
method Backpropagation neural network model.
result Neural network model improves credit risk prediction.
Sustaining efficiency and stability by properly controlling the equity to asset ratio is one of the most important and difficult challenges in bank management. Due to unexpected and abrupt decline of asset values, a bank must closely monitor its net worth as well as market conditions, and one of its important concerns …