framework is model-free and allows for stresses on the output such as (a) the mean and variance, (b) any distortion risk measure … including the Value-at-Risk and Expected-Shortfall, and (c) expected utility type constraints, thus making the reverse … sensitivity analysis framework suitable for risk models. …

the impact of major risk factors on the response variables, such as claim frequency or claim severity. In this work, we … consider the modelling problems of how claim counts, claim amounts and average loss per claim are related to major risk factors … dominant risk factors are identified. The contribution of this work is in making advanced mathematical models possible for …

As the complexity of banking technology systems increases, the prevention of technological risk becomes an endless … for technological risk management, which does not effectively reduce the frequency of technology-related incidents …. Through an analysis of mainstream risk management models, this study proposes a technology-based risk assessment system based …

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. …

retirement years, mostly betting on stocks or indexes with low risk/low volatility. However, every stock-based investment … portfolio has an inherent risk to lose money through negative progression and crash. This study presents a novel technique to …

The risk-based capital (RBC) ratio, an insurance company's financial soundness system, evaluates the capital adequacy …