The book introduces a structured method for the modeling of major operational risk events (i.e. cyber, fraud, etc.) considered in scenario analysis. The XOI approach emphasizes the identification of loss drivers in a scenario and provides a quantification framework to estimate risk exposure and the overall financial impact.
A "must read" for all risk practitioners in the operational risk modeling.
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A rigorous, scientific but applicable-in-Real-Life method for quantifying uncertainties
The authors have combined smoothly more than ten years of practice in building risk models for large corporations with scientific application of probabilities, to deliver an essential handbook for making exploitable Risk models in complex situations.On the way to explaining XOI (Exposure, Occurence, Impact) method, they pass on to you understandable, boiled-down to core, concepts of math (Bayesian networks, probability distributions, convolutions of laws ... ). And you effortlessly pick it all up, so that in the end, you wonder how risk models could be worked out otherwise. As the core of the method stays true to the nature of risk and uncertainties, the method is applicable in a wide array of fields from cyber attacks to road safety, portfolio hedge to credit default, macro economics to bank finance, guaranteeing an unbiaised and explicit quantification of uncertainties.In this way, risks or threat-laden situations can be dealt with efficiency in circumstances where you had once to rely on the hunch or intuition of "experts". These experts now serve even more usefully as their inputs, and not their "in petto" opaque estimations, are explicited. Their scenarios have been "math-ified".It is also remarkable that the authors so generously share their know-how in modelling difficult situations by dispensing practical and life-tested advice on implementing variables and indicators. Risk consultants might as well take benefit from this.In short, this is an Operational book on risk models, as much as a book on modelling Operational Risks.
Top reviews from the United States
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Reviewed in the United States on November 1, 2019
Reviewed in the United States on October 22, 2019
Reviewed in the United States on June 5, 2019
On the way to explaining XOI (Exposure, Occurence, Impact) method, they pass on to you understandable, boiled-down to core, concepts of math (Bayesian networks, probability distributions, convolutions of laws ... ). And you effortlessly pick it all up, so that in the end, you wonder how risk models could be worked out otherwise. As the core of the method stays true to the nature of risk and uncertainties, the method is applicable in a wide array of fields from cyber attacks to road safety, portfolio hedge to credit default, macro economics to bank finance, guaranteeing an unbiaised and explicit quantification of uncertainties.
In this way, risks or threat-laden situations can be dealt with efficiency in circumstances where you had once to rely on the hunch or intuition of "experts". These experts now serve even more usefully as their inputs, and not their "in petto" opaque estimations, are explicited. Their scenarios have been "math-ified".
It is also remarkable that the authors so generously share their know-how in modelling difficult situations by dispensing practical and life-tested advice on implementing variables and indicators. Risk consultants might as well take benefit from this.
In short, this is an Operational book on risk models, as much as a book on modelling Operational Risks.
A rigorous, scientific but applicable-in-Real-Life method for quantifying uncertainties
Reviewed in the United States on June 5, 2019
The authors have combined smoothly more than ten years of practice in building risk models for large corporations with scientific application of probabilities, to deliver an essential handbook for making exploitable Risk models in complex situations.Reviewed in the United States on June 5, 2019
On the way to explaining XOI (Exposure, Occurence, Impact) method, they pass on to you understandable, boiled-down to core, concepts of math (Bayesian networks, probability distributions, convolutions of laws ... ). And you effortlessly pick it all up, so that in the end, you wonder how risk models could be worked out otherwise. As the core of the method stays true to the nature of risk and uncertainties, the method is applicable in a wide array of fields from cyber attacks to road safety, portfolio hedge to credit default, macro economics to bank finance, guaranteeing an unbiaised and explicit quantification of uncertainties.
In this way, risks or threat-laden situations can be dealt with efficiency in circumstances where you had once to rely on the hunch or intuition of "experts". These experts now serve even more usefully as their inputs, and not their "in petto" opaque estimations, are explicited. Their scenarios have been "math-ified".
It is also remarkable that the authors so generously share their know-how in modelling difficult situations by dispensing practical and life-tested advice on implementing variables and indicators. Risk consultants might as well take benefit from this.
In short, this is an Operational book on risk models, as much as a book on modelling Operational Risks.
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Reviewed in the United States on May 8, 2019
