Model estimates loan cure rate using Markov chains.
problem Estimating the cure rate of non-performing loans.
method Developed a Markov-chain model for portfolios.
result Efficient and accessible for smaller institutions.
On-device research index
arXiv research
A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
169,291 papers · 148 categories
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48 results for “credit impairment”
The European sovereign debt crisis has impaired many European banks. The distress on the European banks may transmit worldwide, and result in a large-scale knock-on default of financial institutions. This study presents a computer simulation model to analyze the risk of insolvency of banks and defaults in a bank credit…
After the release of the final accounting standards for impairment in July 2014 by the IASB, banks will face the next significant methodological challenge after Basel 2. In this paper, first methodological thoughts are presented, and ways how to approach underlying questions are proposed. It starts with a detailed disc…
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.
Axient creates a blockchain protocol for managing leveraged event markets, separating roles and formalizing capital management.
problem Managing credit and losses in leveraged event markets on a blockchain.
method Develops a venue-agnostic on-chain credit architecture, formalizing roles and capital management.
result Establishes a balanced accounting system, settlement-confirmed debt priority, and loss-allocation mechanisms.
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.
New algorithms mitigate impairment effect in stochastic bandits.
problem Impairment effect in stochastic multi-armed bandits.
method Developed two novel algorithms with bucketing to address impairment and temporal constraints.
result Achieved sublinear regret, explicitly capturing impairment cost.
Deep learning ensemble improves Alzheimer vs. Mild Cognitive Impairment diagnosis.
problem Differentiating Alzheimer Disease from Mild Cognitive Impairment.
method Hybrid deep learning ensemble framework using MRI slices, pretrained models, and stacked ensemble learning.
result State-of-the-art accuracy (99.21%) for Alzheimer vs. Mild Cognitive Impairment classification.
Model predicts cognitive health risks based on smartphone usage patterns.
problem Identifying cognitive health risks through smartphone usage.
method Structured models of smartphone interactions analyzed over 12 weeks.
result AUROC of 0.79 in discriminating between healthy and symptomatic subjects.
DNNs improve localization from channel estimates, overcoming practical impairments.
problem Improving localization accuracy from channel estimates in Massive MIMO systems.
method Principled feature design for DNNs invariant to practical impairments.
result DNN achieves high localization accuracy and generalization capability.
Mobile app uses CNN to help visually impaired cross streets.
problem Helping visually impaired cross streets safely.
method LytNetV2 deep learning model for pedestrian traffic light detection.
result 96% classification accuracy and 6.15 degree angle error.
Light neural network detects modulation in noisy signals.
problem Efficiently detecting modulation in noisy signals.
method Light neural network architecture invariant to impairments.
result Network achieves accuracy under realistic impairments.
Enhances prediction credibility for Alzheimer's conversion risk.
problem Lack of prediction credibility in machine learning for medical applications.
method Combines Ensemble learning with Conformal Predictors.
result Proposed approach outperforms standard ensemble methods.
Develops a climate risk model for asset managers.
problem Climate-related risks affecting asset performance and productivity.
method Uses the Vasicek model with downward jumps to represent climate impacts on asset dynamics.
result Expected losses increase over time due to climate-related extreme events.
Autoencoder shapes QAM symbols for improved GMI performance.
problem Improving Geometric Mean Information (GMI) in QAM constellation shapes.
method End-to-end learning of bit mappings using autoencoders.
result Achieved up to 0.2 bits/QAM symbol gain in GMI.
Personalized navigation system for visually impaired users improves accuracy and adaptability.
problem Inconsistent navigation guidance for visually impaired users.
method Model-based reinforcement learning framework with weighted experts model for personalization and transfer learning.
result Improved long-term human behavior prediction and quick adaptation for new users.
The study reveals fundamental limits of fraud detection in card payment networks.
problem Fraud detection in card payment networks is challenging due to structural information impairments.
method Formalized card authorization as a sequential decision problem with delayed feedback, derived minimax regret lower bound.
result Improving issuer reporting quality or reducing censorship can yield larger reductions in the regret floor than increasing model complexity.
Improving Mild Cognitive Impairment Prediction via Reinforcement Learning and Dialogue Simulationcs.LG
Paper proposes RL for efficient MCI diagnosis from dialogue data.
problem Efficiently diagnose MCI from conversational data with minimal interaction.
method Reinforcement learning framework trained on clinical trial transcripts.
result Significantly outperforms supervised learning approaches with minimal conversation turns.
Paper proposes RL algorithms for optimizing cellular network performance.
problem Improving cellular network performance against wireless impairments.
method Formulated as a reinforcement learning problem and developed two algorithms: PC and SON.
result RL algorithms outperform industry standards in simulated environments.
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.
Scoping review finds EEG key in MCI research, identifying ERP/EEG, QEEG, and machine learning.
problem Identifying MCI early and accurately.
method Scoping review with co-occurrence analysis and PAGER framework.
result Main research themes identified: ERP/EEG, QEEG, and EEG-based machine 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.
Global feature model improves early AD diagnosis accuracy.
problem Early diagnosis of mild cognitive impairment (MCI) using structural MRI images.
method Gaussian discriminant analysis (GDA) with dual high-dimensional decision spaces for global feature extraction.
result Achieved F1 score of 91.06% for MCI vs. CN group.
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.
This study models credit default swap premiums with a stochastic recovery rate.
problem Analyzing credit default swap premiums with varying recovery rates.
method Develops a model using stochastic recovery rates.
result Models credit default swap premiums effectively with a stochastic recovery rate.
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.
Modelling China's Credit System with Complex Network Theory for Systematic Credit Risk Controlq-fin.RM
Complex network theory models China's credit system to control systemic risk.
problem Insufficient understanding of China's credit network structure during financial crises.
method Constructed bipartite financial institution-firm network and analyzed its typological properties.
result Credit network structure can amplify local risks to the whole economy.
Study shows statistical biases can mislead transformer models, impairing their generalization.
problem Statistical biases in transformers affect their ability to generalize.
method Evaluated transformer models on synthetic algorithmic tasks with varying statistical biases.
result Statistical biases lead to overestimation of transformer models' generalization capabilities.
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.
The Giroux correspondence and the notion of a near force-free magnetic field are used to topologically characterize near force-free magnetic fields which describe a variety of physical processes, including plasma equilibrium. As a byproduct, the topological characterization of force-free magnetic fields associated with…
Study dynamic hedging of credit risk using a new model.
problem Dynamic hedging of counterparty risk for credit derivatives.
method Empirically driven credit model with interacting default intensities; Galtchouk-Kunita-Watanabe decomposition; closed-form risk minimizing strategy.
result Closed-form representation for risk minimizing strategy in nonlinear recursive systems.
DIM shows promise in predicting Alzheimer's progression.
problem Predicting Alzheimer's disease progression from brain imaging.
method Used variants of Deep InfoMax (DIM) for brain imaging analysis.
result DIM outperforms supervised AlexNet and ResNet in Alzheimer's progression prediction.
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.
Cluster analysis of credit card accounts helps assess risk levels.
problem Assessing risk levels for credit accounts.
method Parametric modelling of account behavior, behavioral cluster analysis with a new dissimilarity measure.
result Interesting clusters and superior prediction of account default.
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.
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.
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 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…
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.
CNN improves AMC accuracy under multipath fading channels.
problem Difficulty in designing robust AMC classifiers under multipath fading.
method Proposes a CNN classifier for AMC, robust to channel impairments.
result CNN outperforms existing methods in accuracy and training time.
Interbank Credit and the Money Manufacturing Process. A Systemic Perspective on Financial Stabilityq-fin.GN
Interbank credit can create money, leading to financial instability.
problem Systemic instability caused by coordination failures in interbank credit.
method Developed a model of interbank credit coordination under minimal institutions, analyzed through simulation.
result Interbank credit can lead to unbound monetary systems and financial instability.
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.