Bayesian networks improve product risk assessment by handling uncertainty and causality.
problem Limited handling of uncertainty and inability to incorporate causal explanations in existing methods.
method Bayesian Networks (BNs) for improved systematic product risk assessment.
result BN approach provides more powerful and flexible risk assessments.
Neural networks assess asset-liability risk over time.
problem Challenging valuation of portfolios with complex products.
method Neural network approach for conditional portfolio valuation.
result Effective risk assessment for banking and insurance portfolios.
The paper provides an algorithm for the risk estimation when a company selects an outsourcing service provider for innovation product. Calculations are based on expert surveys conducted among customers and among providers of outsourcing. The surveys assessed the degree of materiality of species at risk.
TinyXRA assesses financial risks from 10-K reports using a lightweight transformer model.
problem Comprehensive risk assessment from financial reports, distinguishing between upside and downside risk.
method Lightweight transformer model with dynamic attention, incorporating skewness, kurtosis, and Sortino ratio.
result State-of-the-art predictive accuracy and transparent risk assessments.
Assessment of risk levels for existing credit accounts is important to the implementation of bank policies and offering financial products. This paper uses cluster analysis of behaviour of credit card accounts to help assess credit risk level. Account behaviour is modelled parametrically and we then implement the behav…
New method quantifies systemic risk of firms in supply networks.
problem Quantifying economic systemic risk of firms from supply networks.
method Unique value-added tax dataset; novel approach for computing ESR.
result A tiny fraction of companies have high systemic risk impacting 23% of national production.
Models assess how USDA orange production forecasts impact FCOJ market decisions.
problem High volatility in FCOJ futures due to limited U.S. orange production.
method Developed models to assess the impact of USDA October orange production forecasts on FCOJ market participants.
result Probabilistic forecasts of USDA production forecast error can reduce FCOJ price volatility.
Study assesses risks of European Safe Bonds using credit risk models.
problem Risks associated with European Safe Bonds and related securities.
method Affine credit risk model with regime switching.
result ESBies are not truly risk-free, impacting market and policy implications.
Develops a framework to assess systemic risk in the economy using bank-firm network data.
problem Measuring systemic risk in the economy using multilayer network data.
method Unified framework combining techniques to reconstruct multilayer economy structure from bank and firm balance sheets, and dynamics of shock propagation.
result Identifies systemically important firms and banks, and assesses systemic risk determinants.
Study shows how firms adapt to systemic risk during crises, revealing key players and trade volume predictors.
problem Understanding systemic risk in local production networks during crises.
method Analyzing Hungarian production network dynamics from 2015 to 2022 using a null model and empirical data.
result Firms' adaptive behavior during crises leads to more resilient economies, with trade volume being a significant predictor.
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.
Model assesses how supply chain disruptions affect financial stability.
problem Systemic risk in production networks and its financial implications.
method Data-driven econo-financial stress-testing framework combining supply chain and interbank networks.
result Increase of up to 28% in financial systemic risk due to production network contagion.
Enhances early risk assessments for pediatric outcomes using contrastive learning.
problem Improving risk assessments in early stages of pediatric development.
method Contrastive multi-modal framework that treats each time window as a distinct modality, training on all available data.
result Consistent improvements in early-stage risk assessments validated on real-world tasks.
Researchers infer firm-level supply chain networks from sector-level data to assess systemic risk.
problem Estimating systemic risk in economic systems using firm-level data.
method Maximum-entropy algorithms applied to input-output tables and firm-level aggregate output data.
result The most realistic systemic risk content is retrieved by models incorporating disaggregated firm-specific inputs by sector.
Unified framework for CVA sensitivities, hedging, and risk assessment.
problem Computing and managing Credit Value Adjustment (CVA) sensitivities and risks.
method Probabilistic machine learning and refined regression on simulated data, validated by Monte Carlo methods.
result Identification of optimal sensitivities for practical tasks like hedging and risk assessment.
New method assesses financial and cyber risks under uncertainty.
problem Uncertainty in risk assessment for financial and cyber systems.
method Combines stochastic approximation and distorted mix method to compute worst case average value at risk.
result Efficient algorithm for tail uncertainty in multivariate distributions.
Paper proposes model to assess financial risk of grid-ignited wildfires.
problem Financial risk and solvency threats from grid-ignited wildfires.
method Integrated model to evaluate damage costs and risk levels.
result Identifies high-risk areas for preemptive actions.
This research develops a dynamic risk management system for industrial companies.
problem Risk assessment and management in industrial enterprises.
method Qualitative and quantitative analysis, systematic risk classification, dynamic system development.
result Effective risk management strategies formed through dynamic risk management system and risk assessment methods.
The study examines how formal index insurance compares to informal risk sharing in managing natural disasters.
problem The challenges of natural disasters and the effectiveness of index insurance in risk management.
method A three-strategy evolutionary game model to analyze the competitive relationship between formal index insurance, informal risk sharing, and non-insurance.
result Basis risk and loss ratio significantly impact the adoption rate of index insurance, with different strategies preferred under varying conditions.
Network theory assesses systemic risk in the insurance sector.
problem Detecting critical insurance companies in systemic risk.
method Complex network approach with weighted effective resistance centrality.
result Identifies companies with significant influence on network robustness.
The paper introduces ESE scores for farmers to assess climate change risks.
problem Assessing climate change risks in individual farmers' credit evaluations.
method Integrating ESG variables into joint liability models and using a mean-variance utility function.
result Optimal group sizes and individual-ESE score relationships under various climatic conditions.
Optimizes risk assessment tools using mixed-integer programming.
problem Challenges in healthcare risk assessment due to label scarcity and asymmetric misclassification costs.
method Jointly optimizes scoring weights and category thresholds via mixed-integer programming (MIP).
result Prevents label-scarce category collapse and achieves more accurate risk categorization.
Paper proposes a new model to assess risks in energy storage systems considering both exogenous and endogenous uncertainties.
problem Current risk assessment ignores the stochastic nature of energy storage availability.
method Data-driven unified model with exogenous and endogenous uncertainty description for four types of generic energy storage.
result Comparative results show more severe risks for endogenous uncertainty, suggesting new strategies for system operators.
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 combines quantum and classical deep learning for better credit risk assessment.
problem Enhancing accuracy and efficiency in credit risk evaluation.
method Hybrid Quantum-Classical Deep Neural Network for Row-Type Dependent Predictive Analysis.
result Proposed framework enhances predictive models for different loan categories.
Enhances Transformers for better risk assessment in finance.
problem Transformer models lack sensitivity to extreme financial losses.
method Integrates Loss-at-Risk function with Value at Risk (VaR) and Conditional Value at Risk (CVaR).
result Improves risk prediction and management in financial datasets.
Fast risk assessment for autonomous vehicles using learned agent futures.
problem Risk assessment for autonomous vehicles given probabilistic predictions of other agents' futures.
method Non-sampling based methods using deep neural networks for probabilistic predictions, with Gaussian and non-Gaussian mixture models for agent positions and controls.
result Effective risk assessment for low probability events using learned models of agent futures.
Paper proposes a natural hedging framework with graphical assessment for longevity risk management.
problem Lack of a unified framework for natural hedging and graphical risk assessment.
method Structured natural hedging framework integrated with a graphical risk metric.
result Demonstrates flexibility, interpretability, and practical value for longevity risk management.
Study uses CNN to analyze images of SMEs for bankruptcy risk.
problem Lack of data for risk analysis of SMEs.
method Created images for each SME, trained CNN on these images.
result CNN achieved 97.8% accuracy in predicting bankruptcy.
This study analyzes how carbon pricing affects credit risk measures in a portfolio.
problem Impact of carbon pricing on credit risk measures in a portfolio.
method Adapted stochastic multisectoral model to account for GHG emissions costs and carbon prices.
result Carbon pricing distorts firm value distributions, increases banking fees, and reduces profitability.
The paper identifies and critiques problems with risk matrices using ordinal scales.
problem Problems with risk matrices using ordinal scales.
method Overview of risk assessment process, explanation of fallacies, and suggestions for improvement.
result The paper proposes avoiding risk matrices and using fully quantitative methods instead.
The paper introduces a new risk assessment framework using φ-divergence.
problem Assessing risk and decision-making in uncertain conditions.
method Introduces a novel framework called the φ-Divergence Quadrangle.
result Provides a more nuanced understanding of risk through φ-divergence.
Paper offers a fast method to assess DeFi liquidation risk.
problem Assessing liquidation risk in DeFi stablecoin lending.
method Modeling collateral exchange rate as zero-drift geometric Brownian motion.
result Derives an exact formula for liquidation probability.
Paper assesses risks of stablecoins, from lending to business-to-business.
problem Credit risks in decentralized stablecoin issuance.
method Examines mechanisms, risks, and mitigation strategies at each layer.
result Potential for scaling stablecoins while maintaining systemic health.
Paper introduces risk assessment for contextual bandits without experiments.
problem Evaluate policies using logged data in context bandits.
method Lipschitz risk functionals and Off-Policy Risk Assessment (OPRA) framework.
result OPRA provides finite sample guarantees for various risk estimates.
Risk, including economic risk, is increasingly a concern for public policy and management. The possibility of dealing effectively with risk is hampered, however, by lack of a sound empirical basis for risk assessment and management. The paper demonstrates the general point for cost and demand risks in urban rail projec…
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.
Dynamic risk assessment method for WUI fires improves upon static frameworks.
problem Static risk assessment methods fail to capture dynamic changes in WUI fire risks.
method Dynamic evaluation matrix, grey incidence analysis, optimization model.
result The proposed method effectively captures dynamic risk evolution patterns.
Introduces factor risk measures to assess risk relative to multiple factors.
problem Measuring risk relative to multiple factors.
method Introduces a double-argument mapping as a risk measure to assess risk relative to a vector of factors.
result Characterizes various types of factor risk measures including distortion, quantile, linear, and coherent measures.
Improves pre-trial risk assessments by making them safer without changing existing rules.
problem Improving pre-trial risk assessments while maintaining deterministic rules.
method Developed a maximin robust optimization approach to find a safer policy.
result Can safely improve certain components of the risk assessment instrument.
Assessing systemic risk in financial markets is of great importance but it often requires data that are unavailable or available at a very low frequency. For this reason, systemic risk assessment with partial information is potentially very useful for regulators and other stakeholders. In this paper we consider systemi…
The paper analyzes the risk of investing in a basket of 27 cryptocurrencies using statistical distributions.
problem Risk assessment of capital allocation in a basket of cryptocurrencies.
method Used statistical tests to determine the most appropriate distribution (SDI) for modeling returns, and adapted the generalized Pareto distribution for tail risk assessment.
result Found that a combination of stable and generalized Pareto distributions provides a more accurate risk assessment for the basket of cryptocurrencies.
Develops framework for valuing and assessing risk of renewable PPAs.
problem Valuation and risk assessment of non-standard renewable PPAs.
method Formalizes payoff structures, derives fair contract prices, proposes market risk-assessment methodology.
result Fair prices and risk profiles vary across technologies and contractual structures.
We provide a new extension of Breiman's Theorem on computing tail probabilities of a product of random variables to a multivariate setting. In particular, we give a complete characterization of regular variation on cones in [0,∞)d under random linear transformations. This allows us to compute probabilities of a…
We study the risk assessment of uncertain cash flows in terms of dynamic convex risk measures for processes as introduced in Cheridito, Delbaen, and Kupper (2006). These risk measures take into account not only the amounts but also the timing of a cash flow. We discuss their robust representation in terms of suitably p…
Study predicts risk of true-lumen narrowing after ATAAD surgery using CT data.
problem Early post-surgery risk assessment for aortic dissection patients.
method Retrospective study with CT data, derived cross-sectional shapes, form factor (FF) for morphology assessment, linear discriminant analysis (LDA) for risk classification, LOPO-CV for prediction.
result Machine-learning model accurately predicts risk for all high-risk patients and low-risk patients, potentially reducing hospital visits.
With the deployment of online monitoring systems in distribution networks, massive amounts of data collected through them contains rich information on the operating states of the networks. By leveraging the data, an unsupervised approach based on bidirectional generative adversarial networks (BiGANs) is proposed for op…
Unified model predicts stock and systemic risks from diverse financial data.
problem Isolating financial tasks leads to missed cross-scale dependencies.
method Shared Transformer backbone with modular task heads for cross-modal attention and multi-task optimization.
result Uni-FinLLM significantly outperforms baselines in stock forecasting, credit-risk assessment, and systemic-risk detection.