.e., the client’s perspective, from the standpoint of the project. In the literature, there are several risk interpretations … and classifications. We assumed it could be interesting and valuable to approach investment projects from a consumer … depending on the perceived project risk. We have established the orientation directions along which these segments may well be …

risk assessment based on behavior for the health insurance sector. To do so we built a platform to collect real-world data … built classifiers to predict variations in important quantities for the lifestyle of a person. We offer a risk assessment …

The PRISM method is a risk assessment approach that focuses on hidden-risk identification and ranking. The combined AHP … alternatives without losing its ability to focus on identifying hidden risk. The method was tested on data related to strategic …

method and empirical analysis were employed to achieve a more accurate risk assessment. APIS and MS Excel softwares were … external and internal threats. To streamline and improve project efficiency, a risk management algorithm for high-tech projects … enhancements in risk reduction, and therefore cost savings, efficiency, safety, and quality, while fostering a culture of …

One of the key components of financial risk management is risk measurement. This typically requires modeling …

This study addresses the critical challenge of predicting liquidity risk in the banking sector, as emphasized by the … Basel Committee on Banking Supervision. Liquidity risk serves as a key metric for evaluating a bank's short-term resilience … risk, especially in Iranian banks with high liquidity risk, this study aimed to develop an AI-based model to predict the …

This article reviews two leading measures of financial risk and an emerging alternative. Embraced by the Basel accords …, value-at-risk and expected shortfall are the leading measures of financial risk. Expectiles offset the weaknesses of value-at-risk … (VaR) and expected shortfall. Indeed, expectiles are the only elicitable law-invariant coherent risk measures. After …

risk model CreditRisk+. This allows exact risk aggregation via an efficient numerically stable Panjer recursion algorithm …. Furthermore, the model allows exact (without Monte Carlo simulation error) calculation of risk measures and their sensitivities … with respect to model parameters for P&L distributions such as value-at-risk and expected shortfall. Numerous examples …

The present study aims at modelling market risk for four commodities, namely West Texas Intermediate (WTI) crude oil … Conditional Auto-Regressive Logit (CARL) models to predict risk measures for the futures return series of the considered … models in order to predict the probability of tail events and the Value-at-Risk and the Expected Shortfall risk measures for …

We introduce a neural network approach for assessing the risk of a portfolio of assets and liabilities over a given … value-at-risk and expected shortfall, but the approach also works for other risk measures. …