Current auto loans converge to super-prime credit despite remaining underwater.
problem Inefficient consumer behavior in auto loans leading to suboptimal credit risk.
method Large-sample statistical hypothesis test on transition matrix between risk bands.
result All current risk bands converge to super-prime credit, despite remaining underwater.
Model assesses loan profitability under changing credit conditions.
problem Financial institutions face risks of default and prepayment.
method Develops a Random Net Present Value (RNPV) model to evaluate profitability.
result Mean and variance of RNPV calculated at individual and portfolio levels.
Credit Scores are ubiquitous and instrumental for loan providers and regulators. In this paper we showcase how micro-loan credit system can be developed in real setting. We show what challenges arise and discuss solutions. Particularly, we are concerned about model interpretability and data quality. In the final sectio…
Extends ASRF model for green and brown loans, accounting for systematic and idiosyncratic risks.
problem Credit risk assessment for portfolios of green and brown loans.
method Two-factor copula structure, skewed distributions for systematic risk, Gaussian for idiosyncratic risk, non-uniform exposure setting.
result Portfolio loss convergence to a limit reflecting green and brown loan characteristics.
System designs for analyzing and pricing non-performing consumer credit portfolios.
problem Technical challenges in analyzing and pricing portfolios of non-performing consumer credit loans.
method Bottom-up architecture, simultaneous quantile regression, R-copula, Gaussian one-factor copula model.
result Successfully developed a methodology for analyzing credit portfolio risks of consumer loans.
An integrated and extendable approach for stress-testing loan portfolios
problem Stress-testing loan portfolios
method Simulate completed portfolios, generate uncertain cash flow history, compute credit risk metrics
result Enhanced stress-testing practices within any bank
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.
Quantum mechanics applied to credit loans for better repayment schedules.
problem Improving repayment schedules for credit loans.
method Introducing quantum mechanics concepts to credit loans, defining operators for debt, amortization, interest, and installments, and using SO(M) symmetry to optimize periodic payments.
result Optimized repayment schedules for borrowers without altering lender's earnings.
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.
Retail investors set interest rates for P2P loans based on borrower characteristics.
problem Understanding how individual investors price credit risk in online consumer loan auctions.
method Reverse auction framework, analyzing interest rate variance and borrower characteristics.
result Retail investors exhibit strong predictability in pricing, with gender and marital status influencing interest rates.
We find that factors explaining bank loan recovery rates vary depending on the state of the economic cycle. Our modeling approach incorporates a two-state Markov switching mechanism as a proxy for the latent credit cycle, helping to explain differences in observed recovery rates over time. We are able to demonstrate ho…
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.
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.
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.
The paper analyzes Lending Club's loan applicants to predict default risk.
problem Predicting default risk in loan applicants of Lending Club.
method Exploratory data analysis and machine learning (Logistic Regression, Random Forest) were used.
result A credit derivative based on Credit Default Swap was designed to hedge default risk.
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.
Examines how extending home loan durations affects French households financially.
problem Financial implications for households with extended home loan durations.
method Analysis of French and international home loan systems, including bullet loans and Japanese home loans.
result Extending home loan durations can reduce monthly payments but raises financial risks.
5D AI model detects bad loans without biased features, improving consumer protection.
problem Detecting bad loans without biased features and improving consumer protection.
method Machine learning, BiMOPT features, European Banking Authority principles, AI principles, historical and validation datasets.
result 5D correctly detected 1,461 bad loans out of 1,613 (Sensitivity = 0.91, Prevalence = 0.0253, Positive Predictive Value = 0.19).
AXI assesses bank funding costs transparently, improving loan pricing and reducing financial risk.
problem Lack of credit-sensitive funding benchmarks after LIBOR transition.
method AXI aggregates unsecured funding transactions across maturities, producing a daily credit spread.
result AXI correlates with financial conditions and market stress, reducing funding risk and offering spread discounts.
This paper proposes a two-stage scoring approach to help lenders decide their fund allocations in the peer-to-peer (P2P) lending market. The existing scoring approaches focus on only either probability of default (PD) prediction, known as credit scoring, or profitability prediction, known as profit scoring, to identify…
A new method detects and removes false trailing balances in credit data.
problem False trailing balances in credit data corrupt risk event timing.
method TruEnd-procedure defines and removes false trailing balances.
result Improved accuracy in predicting risk events and reducing credit losses.
Derivatives impact U.S. banking sector's systemic risk, but loan and leverage ratios are more significant.
problem Systemic risk in U.S. banking sector due to derivatives and loans.
method Analysis of derivatives and loan data to assess systemic risk.
result Loan and leverage ratios are more influential in systemic risk than derivatives holdings.
Model improves mortgage credit risk prediction with spatio-temporal machine learning.
problem Improving accuracy of default probabilities and loan portfolio loss distributions in mortgage credit risk.
method Combines tree-boosting with a latent spatio-temporal Gaussian process model.
result Predictive models outperform conventional methods due to non-linear and spatio-temporal effects.
The paper is aware of the importance of certain figures that are essential to an understanding of Credit Scoring models in credit acceptance process optimization, namely if the power of discrimination measured by Gini value is increased by 5% then the profit of the process can be increased monthly by about 1 500 kPLN (…
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 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.
Credit risk prediction is an effective way of evaluating whether a potential borrower will repay a loan, particularly in peer-to-peer lending where class imbalance problems are prevalent. However, few credit risk prediction models for social lending consider imbalanced data and, further, the best resampling technique t…
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.
Model predicts loan default risk using dynamic multilayer graph neural networks.
problem Credit risk assessment through borrower connections.
method Dynamic multilayer graph neural network with attention mechanism.
result Attention mechanism improves model performance.
This research investigated the potential for improving Peer-to-Peer (P2P) credit scoring by using "private information" about communications and travels of borrowers. We found that P2P borrowers' ego networks exhibit scale-free behavior driven by underlying preferential attachment mechanisms that connect borrowers in a…
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.
Optimizes loan recovery timing by forecasting cash flows.
problem Minimizing overall credit loss in loan portfolios.
method Forecast future cash flows using probabilistic and Markov chain models.
result Empirical illustration of loss-optimal recovery timing.
New measure predicts Dutch housing market downturns.
problem Understanding causes of Dutch housing boom and bust.
method Modelled household lending capacity using bank formulas.
result New measure outperforms traditional measures in forecasting housing prices.
Client appraisal improves efficiency in microfinance banks in Adamawa State.
problem Increasing loan defaults and losses in microfinance institutions.
method Survey method with primary and secondary data collection, multi-stage sampling, questionnaires, descriptive and inferential statistics.
result Client appraisal positively affects efficiency and productivity.
Optimizes loan recovery timing across various portfolios.
problem Comparing and evaluating bank's loan recovery decision rules.
method Simulation-based expert system considering time value of money and costs.
result Threshold optima exist across different risk scenarios and portfolio compositions.
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.
Agents buy and sell services. All services are of equal quality. Buyers choose sellers at random. Monetary and fiscal policies are imposed by a central bank and a central government. Credit is supplied by a commercial banking system. Propensities to buy, sell, and lend depend on account balances, interest rates, tax ra…
The paper proposes a new method to improve microcredit decisions by modeling sequential loan interactions.
problem Improving microcredit decision-making by addressing population bias and model generalization.
method The authors introduce a multi-stage interaction sequence (MSIS) method that models sequential loan interactions and uses a hierarchical attention module to leverage interaction information.
result The MSIS method effectively remedies population bias and improves model generalization on a real loan data set.
We study a simple, solvable model that allows us to investigate effects of credit contagion on the default probability of individual firms, in both portfolios of firms and on an economy wide scale. While the effect of interactions may be small in typical (most probable) scenarios they are magnified, due to feedback, by…
Analysis of the 2007-8 credit crisis has concentrated on issues of relaxed lending standards, and the perception of irrational behaviour by speculative investors in real estate and other assets. Asset backed securities have been extensively criticised for creating a moral hazard in loan issuance and an associated incre…
Survival analysis models predict loan write-off risk under IFRS 9.
problem Estimating loan write-off probabilities in credit risk modeling.
method Discrete-time hazard model and conditional inference survival tree compared to cross-sectional logistic regression.
result Discrete-time hazard model outperforms other two-stage LGD-models.
Examines climate financing for renewable energy projects using structured funds.
problem Valuation of structured climate financing on diverse renewable energy asset pools.
method Bottom-up Gaussian copula framework with LH++ model for diversification analysis.
result Shows how the mix of indirect and direct RE investments affects the sensitivity of the senior tranche.
A Markov-chain model is developed for the purpose estimation of the cure rate of non-performing loans. The technique is performed collectively, on portfolios and it can be applicable in the process of calculation of credit impairment. It is efficient in terms of data manipulation costs which makes it accessible even to…
The importance of adequately modeling credit risk has once again been highlighted in the recent financial crisis. Defaults tend to cluster around times of economic stress due to poor macro-economic conditions, {\em but also} by directly triggering each other through contagion. Although credit default swaps have radical…
XPER methodology decomposes credit scoring model performance.
problem Monitoring and understanding the key drivers of credit scoring model performance.
method XPER methodology based on Shapley values, decomposing performance metrics into feature contributions.
result A small number of features explain a large part of model performance.
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,…
We redefine SICR-events for better loan classification under IFRS 9.
problem Ambiguity in SICR-event definition under IFRS 9.
method Proposed alternative framework with three parameters: delinquency, stickiness, and outcome period. Varying these parameters, we generated 27 unique SICR-definitions and fitted logistic regression models.
result The proposed SICR-models outperform the PD-comparison approach as an early-warning system for credit losses.
Online Peer to Peer Lending (P2PL) systems connect lenders and borrowers directly, thereby making it convenient to borrow and lend money without intermediaries such as banks. Many recommendation systems have been developed for lenders to achieve higher interest rates and avoid defaulting loans. However, there has not b…