AutoML enhances credit decisions with XAI for better transparency.
problem Transparency in AI-driven financial decisions.
method Combining AutoML and XAI (SHAP) for credit scoring.
result Improved efficiency and accuracy in credit decisions with enhanced transparency.
The paper explains credit decisions using Shapley decomposition for adverse actions.
problem Identifying predictors responsible for adverse credit decisions.
method Develops a simple and intuitive approach based on Shapley decomposition for models with low-order interactions.
result Shows the approach generalizes to Shapley decomposition and Baseline Shapley.
The paper explores fairness in credit scoring using machine learning.
problem The lack of research on fair machine learning in credit scoring.
method Revisits statistical fairness criteria, catalogs algorithmic options, and empirically compares fairness processors.
result Multiple fairness criteria can be approximately satisfied at once, and fair processors deliver a good balance between profit and fairness.
This study compares neural networks, SVM, and decision trees for corporate credit rating predictions.
problem Predicting corporate credit ratings using machine learning methods.
method Applied four machine learning techniques (Bagged Decision Trees, Random Forest, SVM, MLP) to credit rating datasets.
result Decision tree-based models outperformed other techniques in terms of 'Notch Distance' measure.
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.
Paper uses RL for better credit scoring and underwriting.
problem Traditional underwriting methods are ungeneralizable in complex scenarios.
method Adapts RL principles for credit scoring, incorporating action space renewal and multi-choice actions.
result RL-based algorithms outperform traditional methods in aligned data scenarios.
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.
New framework for modular reinforcement learning reduces sample complexity.
problem Achieving independent credit assignment in reinforcement learning.
method Defining modular credit assignment as minimizing algorithmic mutual information, introducing modularity criterion for causal analysis.
result Single-step temporal difference action-value methods meet the modularity criterion, improving sample efficiency.
Improved credit scoring model with explainability.
problem Making financial decisions based on loan applications.
method Extreme Gradient Boosting (XGBoost) model with 360-degree explanation framework.
result Model achieves state-of-the-art performance and provides understandable explanations.
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.
Paper uses LightGBM for mobile user credit assessment.
problem Improving credit evaluation methods for communication operators.
method Data preprocessing, feature engineering, multiple machine learning models integration.
result Established a suitable fusion model for operator user credit evaluation.
KACDP model improves credit default prediction with enhanced interpretability.
problem Insufficient interpretability and limited performance in credit default prediction.
method Kolmogorov-Arnold Networks (KANs) for handling complex multi-dimensional data.
result KACDP model outperforms mainstream models in performance metrics.
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…
We consider the problem of efficient credit assignment in reinforcement learning. In order to efficiently and meaningfully utilize new data, we propose to explicitly assign credit to past decisions based on the likelihood of them having led to the observed outcome. This approach uses new information in hindsight, rathe…
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.
Over-the-counter derivatives have contributed significantly to the effectiveness and efficiency of the international financial system but also entail significant counterparty credit risk. Collateralization is one of the most important and widespread credit risk mitigation techniques used in derivatives transactions. Ho…
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.
The financial services industry has unique explainability and fairness challenges arising from compliance and ethical considerations in credit decisioning. These challenges complicate the use of model machine learning and artificial intelligence methods in business decision processes.
Paper tackles transparency and auditability of machine learning in credit scoring.
problem Missed potential in using modern machine learning for credit scoring due to lack of transparency.
method Develops a framework for making black box machine learning models transparent, auditable, and explainable.
result Comparable interpretability can be achieved with machine learning while maintaining predictive power.
Study uses AI to refine loan assessments, improving credit default predictions.
problem Improving credit default prediction accuracy using AI-refined text.
method Comparative analysis of human-written and AI-refined loan assessments using deep learning techniques.
result AI-refined texts significantly enhance credit default predictions, especially when combined with structured data.
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.
BSAC improves credit scoring models by leveraging autoencoders and addressing imbalanced datasets.
problem Imbalanced and heterogeneous credit scoring datasets.
method Bagging Supervised Autoencoder Classifier (BSAC) that uses autoencoders and undersampling.
result BSAC improves classification of loan applicants, demonstrating robustness and effectiveness.
CCI combines Bayesian and gradient boosting to create fair, reliable credit risk scores.
problem Tackles high-stakes lending decisions with changing data distributions and fairness constraints.
method Combines Bayesian neural risk scorer and fairness-constrained gradient boosting with shift-aware fusion.
result CCI achieves best trade-off between discrimination, calibration, stability, and fairness.
Eigenoptions improve credit assignment in reinforcement learning.
problem Improving credit assignment in reinforcement learning models.
method Investigated eigenoptions for credit assignment in model-free RL, comparing pre-specified and online discovery methods.
result Pre-specified eigenoptions aid exploration and credit assignment, while online discovery can hinder learning.
FSL-BDP models time-to-default without centralizing data, improving privacy mechanisms in federated settings.
problem Traditional credit risk models ignore default timing and violate data-protection rules.
method Federated Survival Learning with Bayesian Differential Privacy (FSL-BDP).
result FSL-BDP improves privacy mechanisms in federated settings, outperforming classical DP in most clients.
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.
Study integrates climate and text data to improve credit default prediction.
problem Improving credit risk assessment for mSEs with limited financial histories.
method Multimodal framework using LSTM, GRU, and transformer models.
result Integration of multiple data modalities improves credit default prediction.
Credit risk modelling is an integral part of the global financial system. While there has been great attention paid to neural network models for credit default prediction, such models often lack the required interpretation mechanisms and measures of the uncertainty around their predictions. This work develops and compa…
The paper reduces estimation error in predicting borrower repayment by accounting for lender's credit decisions.
problem Estimation error in predicting borrower repayment due to confounding effects.
method Proposes new estimators to reduce estimation error, combining theoretical analysis and numerical testing.
result The proposed estimators are unbiased, consistent, and robust, showing substantial reduction in estimation error.
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…
Deep learning improves credit risk assessment without new data.
problem Improving credit risk assessment in banking without new data.
method Sequential deep learning using temporal convolutional networks.
result Sequential deep learning outperformed tree-based models in credit risk assessment.
New technique reduces gender discrimination in credit lending models.
problem Bias and unfairness in credit lending predictions.
method Subgroup Threshold Optimizer (STO) technique.
result Reduces gender discrimination by over 90%.
Unified framework models credit cycles and systemic risk.
problem Inadequate classical models for bubbles, crises, and credit cycles.
method Marshall-Walras price formation process and mathematical formalism.
result Unified framework reflects different economic states and systemic risk.
Deep learning has achieved impressive prediction accuracies in a variety of scientific and industrial domains. However, the nested non-linear feature of deep learning makes the learning highly non-transparent, i.e., it is still unknown how the learning coordinates a huge number of parameters to achieve a decision makin…
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.
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…
TransCORALNet uses transformer and CORAL for supply chain credit assessment with cold start.
problem Supply chain credit assessment for new borrowers with limited data.
method Two-stream transformer CORAL networks with domain adaptation and LIME.
result TransCORALNet outperforms state-of-the-art models in accuracy.
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.
In this paper we propose a method to obtain global explanations for trained black-box classifiers by sampling their decision function to learn alternative interpretable models. The envisaged approach provides a unified solution to approximate non-linear decision boundaries with simpler classifiers while retaining the o…
Paper analyzes deep learning models for credit rating prediction using text and numerical data.
problem Improving credit rating prediction using multi-modal deep learning.
method Testing different deep learning models and fusion strategies for structured and unstructured datasets.
result CNN-based multi-modal model with two fusion strategies outperformed other models.
We propose a Markov chain model for credit rating changes. We do not use any distributional assumptions on the asset values of the rated companies but directly model the rating transitions process. The parameters of the model are estimated by a maximum likelihood approach using historical rating transitions and heurist…
Paper uses BERT to assess P2P borrowers' credit risk from loan descriptions.
problem Information asymmetry in P2P lending due to lack of borrower data.
method Fine-tunes BERT on Lending Club dataset to generate risk scores from loan descriptions.
result BERT-generated risk scores improve XGBoost classifier's performance in loan granting.
This paper compares ML algorithms for PD prediction, finding XGBoost to be the most effective.
problem Predicting the probability of default in loan portfolios.
method Comparison of five ML algorithms (Random Forests, Decision Trees, XGBoost, Gradient Boosting, AdaBoost) with logistic regression.
result XGBoost outperforms other ML algorithms for PD prediction.
Paper compares ML methods for credit scoring, highlighting feature selection and scaling impacts.
problem Determining default risk in credit scoring models.
method Eight ML methods (SVM, Naive Bayes, DT, RF, XGBoost, KNN, MLP, LR) with feature selection and scaling.
result Feature selection and scaling improve model performance in credit scoring.
Machine learning (ML) can automate decision-making by learning to predict decisions from historical data. However, these predictors may inherit discriminatory policies from past decisions and reproduce unfair decisions. In this paper, we propose two algorithms that adjust fitted ML predictors to make them fair. We focu…
AI random forest model improves credit risk scoring for Azerbaijani SMEs.
problem Improving accuracy in identifying defaulters for SME loans.
method Used Python to compare a Delphi model with a random forest model, measuring accuracy, precision, recall, and F-1 scores.
result Significant improvements in model performance (e.g., from 0.69 to 0.83 in accuracy).
The paper introduces new processors for fair credit scoring.
problem Fairness in credit scoring with multiple sensitive variables.
method Logical processors (LP) and Multistage processors (MP).
result Logical processors are effective for handling multiple sensitive variables.
The study examines how climate risk influences sovereign debt default decisions.
problem The relationship between climate risk and sovereign debt default decisions.
method Calibration of a model to analyze the credit spreads of sovereign bonds and the impact of climate vulnerability on bond spreads.
result Climate risk does not significantly influence the decision to default on sovereign debt.