Survey examines machine learning for credit rating predictions.
problem Manual loan approvals are slow and error-prone.
method Examines sentiment analysis techniques in credit rating.
result Machine learning improves credit rating predictions.
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48 results for “credit approval”
RMT-Net tackles biased credit scoring data by learning from both default/non-default and rejection/approval tasks.
problem Missing-not-at-random selection bias in financial credit scoring data.
method Reject-aware Multi-Task Network (RMT-Net) that leverages the correlation between default/non-default and rejection/approval tasks.
result RMT-Net improves credit scoring models by learning from both default/non-default and rejection/approval tasks.
Machine learning models are increasingly used in the industry to make decisions such as credit insurance approval. Some people may be tempted to manipulate specific variables, such as the age or the salary, in order to get better chances of approval. In this ongoing work, we propose to discuss, with a first proposition…
XGBoost predicts bank loan defaults with improved accuracy.
problem Predicting bank loan defaults to reduce bad loans.
method Used XGBoost algorithm on loan data.
result Improved accuracy metrics in loan default prediction.
One of the key elements in the banking industry rely on the appropriate selection of customers. In order to manage credit risk, banks dedicate special efforts in order to classify customers according to their risk. The usual decision making process consists in gathering personal and financial information about the borr…
The study examines how social biases are reinforced in machine learning models used for credit scoring.
problem Reinforcement of societal biases in machine learning algorithms for credit scoring.
method Analysis of machine learning models predicting gender or ethnicity based on loan applications data.
result Machine learning models can reflect and reinforce social biases present in the data.
Credit scoring models support loan approval decisions in the financial services industry. Lenders train these models on data from previously granted credit applications, where the borrowers' repayment behavior has been observed. This approach creates sample bias. The scoring model (i.e., classifier) is trained on accep…
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.
The paper compares ML models for credit scoring and investment decisions using explainable AI.
problem The opacity of machine learning models in financial services.
method Comparison of various machine learning models (single classifiers, ensembles, neural networks) and explainability techniques (LIME, SHAP).
result Ensemble classifiers and neural networks outperform in credit scoring models.
Workflow improves credit default prediction using machine learning.
problem Assessing creditworthiness and risk management in lending.
method Data preprocessing with Weight of Evidence, ensemble learning, and hyperparameter optimization.
result Enhanced accuracy in predicting credit default.
PerfGD solves model-induced data shifts by finding optimal points.
problem Model-induced data shifts where model choice changes data distribution.
method Performative Gradient Descent (PerfGD) which explicitly captures model-data interactions.
result PerfGD provably converges to performatively optimal point.
Approves updates to machine learning models in healthcare based on accumulating data.
problem Designing policies to autonomously approve updates to machine learning algorithms in non-stationary settings.
method Learning-to-approve (L2A) approach that uses accumulating monitoring data to learn how to approve modifications.
result L2A learns to abstain when performance drops are common and approves beneficial modifications quickly when the distribution is stable.
Study proposes a new approval policy for ML-based medical devices to prevent gradual performance degradation.
problem Gradual deterioration in machine learning model performance over time in medical devices.
method Formulated an automatic algorithmic change protocol (aACP) as an online hypothesis testing problem, considering both error-rate guarantees and non-guaranteed policies.
result Controlled the rate of gradual deterioration (biocreep) in machine learning models without significantly impacting approval of beneficial modifications.
Some recent studies have suggested using GANs for numeric data generation such as to generate data for completing the imbalanced numeric data. Considering the significant difference between the dimensions of the numeric data and images, as well as the strong correlations between features of numeric data, the convention…
Machine learning models are increasingly used to automate decisions that affect humans - deciding who should receive a loan, a job interview, or a social service. In such applications, a person should have the ability to change the decision of a model. When a person is denied a loan by a credit score, for example, they…
We discovered secular trend bias in a drug effectiveness study for a recently approved drug. We compared treatment outcomes between patients who received the newly approved drug and patients exposed to the standard treatment. All patients diagnosed after the new drug's approval date were considered. We built a machine …
Paper explores a consumer-friendly approach to explain machine learning decisions.
problem Challenges in providing understandable explanations for machine learning predictions.
method Consumer-driven approach called TED that asks for explanations in training data.
result TED is robust to increasing numbers of explanations, noisy explanations, and missing explanations.
The Impact of Bitcoin ETF Approval on Bitcoin's Hedging Properties Against Traditional Assetsq-fin.GN
ETF approval boosts Bitcoin's correlation with equities, stabilizes with gold, and maintains negative correlation with fiat currencies.
problem Impact of Bitcoin ETF approval on Bitcoin's relationships with traditional assets.
method Rolling correlation analysis, Chow tests, and DCC-GARCH models.
result Bitcoin's correlation with equities increased significantly post-ETF approval, while its relationship with gold stabilized and remained negatively correlated with fiat currencies.
Fairness has become a central issue for our research community as classification algorithms are adopted in societally critical domains such as recidivism prediction and loan approval. In this work, we consider the potential bias based on protected attributes (e.g., race and gender), and tackle this problem by learning …
The paper breaks down AUC into cluster-level components for better model diagnostics.
problem Global AUC masks weaknesses in specific subpopulations, leading to financial or operational risks.
method Formal decomposition of AUC into intra- and inter-cluster components, comparing with other performance metrics.
result Allows practitioners to evaluate and diagnose model performance within and across clusters.
We propose a fair principal component analysis method that balances reconstruction error and subgroup fairness.
problem Fairness and robustness in principal component analysis for consequential domains.
method Distributionally robust optimization over the Stiefel manifold with a Riemannian subgradient descent.
result The proposed method achieves better performance on real-world datasets compared to state-of-the-art baselines.
Study finds gender bias in human evaluators and shows how machine learning can mitigate it.
problem Gender bias in human decision-making on micro-lending platforms.
method Structural econometric model and machine learning algorithms trained on real-world data.
result Machine learning algorithms can mitigate both preference-based and belief-based biases.
Potential Future Exposure (PFE) is a standard risk metric for managing business unit counterparty credit risk but there is debate on how it should be calculated. The debate has been whether to use one of many historical ("physical") measures (one per calibration setup), or one of many risk-neutral measures (one per num…
Logistic Regression and Support Vector Machine algorithms, together with Linear and Non-Linear Deep Neural Networks, are applied to lending data in order to replicate lender acceptance of loans and predict the likelihood of default of issued loans. A two phase model is proposed; the first phase predicts loan rejection,…
Less Discriminatory Alternative and Interpretable XGBoost Framework for Binary Classificationstat.ML
LDA-XGB1 balances fairness and accuracy in lending models.
problem Fair lending practices and model interpretability in binary classification.
method Biobjective optimization using binning and information value, leveraging XGBoost.
result Achieves effective balance between accuracy, fairness, and interpretability.
Back cover text: Megaprojects and Risk provides the first detailed examination of the phenomenon of megaprojects. It is a fascinating account of how the promoters of multibillion-dollar megaprojects systematically and self-servingly misinform parliaments, the public and the media in order to get projects approved and b…
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 shows how macroprudential policies affect credit growth in Israel, especially in housing and business sectors.
problem Impact of macroprudential policies on credit growth in Israel.
method Bank-level panel data analysis for Israel, 2004-2019; interaction of monetary and macroprudential policies.
result Accommodative monetary policy interacts with macroprudential policies to increase total credit growth.
Unified view on selective credit assignment for reinforcement learning.
problem Efficient credit assignment in reinforcement learning.
method Unified temporal-difference algorithms with selective weightings.
result New algorithms for backward credit assignment and off-policy learning.
We apply Geometric Arbitrage Theory to obtain results in mathematical finance for credit markets, which do not need stochastic differential geometry in their formulation. We obtain closed form equations involving default intensities and loss given defaults characterizing the no-free-lunch-with-vanishing-risk condition …
This paper develops a machine learning model to assess credit risk in UAE commercial banks.
problem Lack of precision in conventional credit rating tools for accurate credit risk prediction.
method Constructs a credit risk assessment model using Linear Discriminant Analysis.
result Demonstrates improved accuracy in predicting good and bad creditors compared to conventional methods.
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.
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.
Credit scores misclassify borrowers, especially minorities, leading to inequitable access.
problem Misclassification of borrowers by credit scores, particularly minorities.
method Benchmarked a widely used credit score against a machine learning model.
result Machine learning model improves predictive accuracy for low-quality data, leading to more equitable access.
New voting strategies show committee-based consensus can scale efficiently.
problem Ensuring honest committees in committee-based consensus protocols.
method Empirical analysis of simpler voting strategies and their convergence to optimality.
result Simpler voting strategies converge to optimality exponentially quickly, ensuring robustness and efficiency.
Extracts credit-relevant information from earnings calls.
problem Investors do not fully internalize credit-relevant information from earnings calls.
method Develops a novel technique to extract credit-relevant information from earnings call text.
result The extracted information forecasts future credit spread changes and firm profitability.
Predicting Credit Default Probabilities Using Bayesian Statistics and Monte Carlo Simulationsstat.AP
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.
Study cost-effective fairness audits with partial feedback, improving over random exploration.
problem Auditing fairness of classifiers with limited true labels.
method Introduces cost model, proposes near-optimal algorithms for black-box and mixture models.
result Significantly lower audit costs compared to natural baselines.
Paper simplifies default process modeling and credit valuation.
problem Modeling and pricing derivative securities with credit risk.
method Integrates default process, probability, and correlation into a unified framework.
result Risky valuation is Martingale in the proposed model.
Study optimizes classifiers for credit card mail campaigns and default prediction.
problem Optimizing classifiers for credit card mail campaigns and default prediction.
method Three distinct models: response, risk, and response-risk. Optimized various performance metrics.
result Random Forest classifier achieves highest accuracy (83.2%) in multi-class response-risk model.
CERM calculates climate risks in bank loans.
problem Estimating climate risks in bank credit portfolios.
method Adapts credit risk models to include physical and transition risks.
result Calculates incremental credit losses due to climate risks.
We consider the problem of constructing an appropriate multivariate model for the study of the counterparty credit risk in credit rating migration problem. For this financial problem different multivariate Markov chain models were proposed. However the markovian assumption may be inappropriate for the study of the dyna…
Contraction or steady state? An analysis of credit risk management in Italy in the period 2008-2012q-fin.RM
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…
We present a dialogue on Counterparty Credit Risk touching on Credit Value at Risk (Credit VaR), Potential Future Exposure (PFE), Expected Exposure (EE), Expected Positive Exposure (EPE), Credit Valuation Adjustment (CVA), Debit Valuation Adjustment (DVA), DVA Hedging, Closeout conventions, Netting clauses, Collateral …
Method debiases alternative data for fair credit underwriting.
problem Bias in alternative data affecting credit underwriting fairness.
method Causal inference applied to machine learning models.
result Improves model accuracy across racial groups without discrimination.
We consider a structural credit model for a large portfolio of credit risky assets where the correlation is due to a market factor. By considering the large portfolio limit of this system we show the existence of a density process for the asset values. This density evolves according to a stochastic partial differential…
A new model uses a Levy-driven process to value credit index swaptions.
problem Valuation of credit index swaptions in financial markets.
method Proposes a Levy-driven Ornstein-Uhlenbeck process to model risk-free rate and default intensities.
result Derives formulas for characteristic function, moments, and stationary distribution.
Model assesses credit risk using behavioral data from Experian and Bank of Italy.
problem Improving credit risk assessment in financial institutions.
method Statistical and machine learning techniques applied to behavioral data from Experian and Bank of Italy.
result Demonstrates transferability of the model from private to central data.