QUANTITATIVE LOSS ESTIMATE FOR ENVIRONMENTAL AND GENERAL LIABILITY
Marco Amaral – Risk Advisor and Consultant (1) Alice De Poli – Senior Consultant (2)
SUMMARY
A lucrative business usually has high stakes as well as high potential gain. In a world of competing resources, specific skill sets, and contracted services, losses are a direct result of factors not only inherent to the business itself but also often of external factors.
The competitive advantages of environmental management are expressed in a global, macroeconomic context. Environmental management, combined with financial management, provides cost reduction, improves the outlook for the public, consumers and global society as a whole.
This article presents the development of loss estimating based on the most potential accidents that can take place in operational assets, leading to consequences on their business, based on quantitative loss estimate, with an emphasis on environmental liability, general liability potential losses; and insurance program placement.
INTRODUCTION
Today, environmental and general liability areas very important issues on several fronts: social, economic, political, etc. In order to manage the concept of damage, it is very important to cross-check data with the various areas of knowledge in order to guide the client, not only in the pre-contractual and defensive issues, but also in the material part of the damage. In this context, values, moral damages, environmental damages and material damages are included, as well as emergent damages and loss of profits.
An analysis of environmental and general liability will motivate the company at all levels bringing new social design, with a view to reducing security costs and not paying for an environmental recovery. This is a strategic issue in many corporations today, given the rising cost of managing the company and the growing scarcity of natural resources.
The third wave, described by Toffler as early as 1995, with the introduction of concepts and new technologies in a holistic way is relevant, in order to seek greater integration and harmony with nature. This will trigger:
A- Community-government-company relationship improvement;
B- Elimination or reduction of costs with materials and personnel;
C- Monitoring of problems in the law and environment;
D- Reduction of costs with insurance;
E- Ensuring greater profit management.
Performing a risk analysis before placing insurance limits on capital projects will do more than save time, effort, and resources down the line.
The importance of loss estimate decisions increases as the performance of the strategy of the business becomes more visible at the board and stakeholder levels. The assessment of potential revenue generation in the context of high-risk business environments helps determine both insurance limits values and the contract types that should be pursued. Furthermore, knowing the risks of a business at an early phase within its lifecycle, by the means of a corporate risk culture will enable the implementation of a continued dissemination of cost and revenue data and intelligence for the business.
The perception of economic attractiveness or lack of it in certain scenarios, shall allow decision makers to form an opinion about business opportunity, knowing the fundamental business threats, i.e., economics, market and structural elements causing negative or positive impact on the opportunity being analyzed.
In regard to business investment, we put a system in place to identify and analyze risks that may impact client business goals in terms of time and budget. Risk analysis being performed before bids, M&A processes and project design, organizations may expect a better perception of unforeseen risks, regarding insurance placement, and more confidence to achieve their business goals.
QUANTITATIVE LOSS ESTIMATE
The methodology used for risk analysis is a programmatic approach, which focuses at analyzing the uncertainties of loss estimate, through a probabilistic assessment of legal processes and negotiations in the court and other legal organizations. It is worth noting these uncertainties can relate to several factors.
The execution of a loss estimate analysis both validates losses forecasts as well as pinpoints the elements of the business that are the biggest risk drivers (opportunities or threats). Business that appears theoretically very lucrative can actually be contributing the most to reduced revenues due to uncertainty.
The Integrated Quantitative Risk Analysis aims to determine the likelihood that the proposed business meets its goals, and provide limits for insurance placement from loss estimate based on accidental scenarios so that improvements can be made to achieve its success, ensuring the expected return on investment.
The analysis comprises scenarios relating to damage to assets, environmental impact, damage to third parties, and potentially legal consequences, on investment goals and time frame of the project.
On a regular basis, the identification of risks is performed through filed surveys, documents analysis, qualitative and quantitative analysis, technical meetings with stakeholders, including staff from different departments with various areas of knowledge.
The input data collection is made by the means of research in jurisprudence and leniency agreements according to available official sources and facts that have already happened.
Based on the selected scenarios and data and information relating to jurisprudence and agreements among parties involved in the legal processes, a framework for Monte Carlo modeling is built to obtain the quantitative loss estimate in terms of financial results.
The importance of legal research focused on accidental scenarios.
Legal research in the country's accidental scenarios, whether in doctrine (theoretical research) or jurisprudence (empirical research), has expanded because of increased access to judicial decisions. The important thing is that this research method is coherent to answer the exact question of what, how and why judges are judging the liability of a particular outside. That is, it specifies data qualitatively and exposes the numbers of civil responsibility quantitatively and graphically!
The research of jurisprudence can be useful since its study helps to understand the ways of applying the legal norms. It has traditionally been qualitative in character. The arguments used in case law are evaluated, as well as the saturation of evidence, the relevance of the interpretive methods used and a series of other criteria.
Equally necessary and relevant is quantitative research. Whether, how and how often a court decides on a relevant issue is of crucial importance to a securitized firm in terms of civil liability. In addition, legal research is necessary to understand the behaviors or conduct and standing of the judging class in decisions, their origins and consequences, cultural, economic or political.
The matters of civil liability are judged by the Brazilian Supreme Court, a court that publishes its decisions integrally, which facilitates the investigation and provides reliable data that provides the basis for any argument and report in the desired area, in the case, accident area. Compilation of this information gives the client greater security at the time of a business transaction.
METHODOLOGY
When collecting data, it is important to look at the mathematical operations that can be performed on values, since not all values are adequate to be manipulated by the various types of operations.
Selection of data for loss estimate is usually associated with the following factors:
The quantitative loss estimate assessment contemplates the quantitative analysis of losses from accidental scenarios that are considered most critical, that is, loss factors that arise from uncertainties that assume values expressed in scales of reason, that is, values that can be added, subtracted, multiplied or divided. Such quantitative risk factors allow the construction of stochastic models to analyze the behavior adopted in the definition of values of losses and indemnities.
In constructing stochastic models, it is important to consider two types of quantitative risk factors:
A) Discrete: values considered in sets of data in which between any two elements there is a finite amount of elements that belong to the set.
B) Continuous: values considered in sets of data in which between any two elements there is an infinite amount of elements that belong to the set.
Given that case-law data and indemnity values from leniency agreements do not allow loss values to be directly obtained for the selected scenarios, correlation analysis should be performed combining the values of product loss, indemnity amounts claimed and / or negotiated, geographic, socioeconomic, political and legal characteristics of the regions from which the data were obtained, and prosecutor knowledge and perception in order to verify the existence of robust correlation between those parameters.
Such issues are mostly subjective. Non-parametric correlation measurements should be analyzed, that is, an arbitrary monotonous function should be evaluated, which can be the description of the relationship between the variables, without making any assumptions about the frequency distribution of the variables.
Unlike the Pearson correlation coefficient (see attachment), for example, the analyzed correlations do not require the assumption that the relationship between the variables is linear, nor does it require that the variables be measured in the class interval; it can be used for the variables measured at the ordinal level.
Through a random selection of values according to a probability distribution defined by the user, the Monte Carlo simulation is a statistical technique, in order to describe the distribution and characteristics of possible behaviors of a dependent variable.
Monte Carlo simulation, or probability simulation, is a technique used to understand the impact of risk and uncertainty in financial, project management, cost, and other forecasting models.
The data obtained from the linkage of risk and input data are fed in the model, so that the foreseen losses are estimated through Monte Carlo simulation.
A probabilistic distribution is developed and the results are displayed by means of Cumulative Probabilistic Curves and percentiles results.
The result of this analysis presents the simulation of potential losses, divided into levels of probability of confidence, according to risks associated with the elected accidental scenarios.
ENVIRONMENTAL AND GENERAL LIABILITY LOSS ESTIMATE IN THE BRAZILIAN MARKET
In order to evaluate potential correlations between the accidental scenarios and jurisprudence, this current study was developed taking into account the following background, bearing in mind compensation and indemnification:
The Brazilian liability legislation covers three types of damages:
In liability insurance there is a warranty obligation, and regarding the other ensuring rights, i.e., the obligation to be guaranteed the payment of a debt (compensation) of responsibility, and the responsibility of the insured. Now the other insurance of damage have to cover a direct loss in the patrimony of the legal entity and third parties who are affected by the accident.
As to the responsibility for injury, it is important to bear in mind that, not only the voluntary fraudulent acts, but also those by negligence and omission will result in the imposition on the author of the damage to repair it. Statistics show that 96 % of the losses are caused by the attitudes and human behavior and that 4 % are caused by several factors, including where he has no control. These data confirm the importance of a systemic approach, based on the root causes of losses, attitudes and in human behavior.
Legally, the economic capacity and the social position of the company indicate the concepts and structures of general liability. In spite of the responsibility in this exclusive theme of damage being taken on the basis of damage in re ipsa (presumed damage), the case law allows the adoption, as a rule, of the possibility of demanding compensation for damages caused by circumstances where the person responsible cannot be regarded as author in the ethical-legal, but rather the ethical, level.
Theoretically, in cases of serious accidents social solidarity, requires that those who caused the damage, support the consequences. However, in Brazil, there is the criminal law of the aggressor, where the victim is vulnerable to major corporate interests, to the rule of time of judgment and of the demand for custody ruling by the judiciary. In such cases, the courts were called upon to decide whether the public interest warrants the application of an implicit exception by eliminating coverage for punitive damages.
EXAMPLE
Case: Petrochemical plant
Objective: Rating the importance indemnification for losses resulting from industrial accidents in terms of Liability
Types of losses to be assessed:
We carried out an analysis of similar systems and regions.
An assessment of possible impacts versus vulnerability in the vicinity is made considering its features:
Traditional Approach
Figure 1 – Worst case (higher impact plus domino effect)
Based on the traditional approach, a worst case presents the following magnitude:
Figure 2 – Worst case (highest impact radius vs highest impact on people)
Based on the traditional approach, a worst case presents the following magnitude:
Selection of the Worst Cases – Relevant Premises
In the traditional approach, each loss has an estimated cost and the total cost of compensation is given simply by the sum of the maximum costs of compensation.
The problem becomes more complex when the cost of each loss has uncertainties and, therefore, the total cost of losses will also contain uncertainties.
The traditional model does not address the inherent uncertainty of loss values for the possible accident scenarios
Traditional Approach Estimate
Loss estimate value by Traditional Approach (Benchmark): $ 83,741,000
Constraints:
According to the results from Quantitative Approach graph, the level of confidence for the Benchmark value is: 9% ($ 83,741,000)
Quantitative Approach Estimate vs Traditional Approach Estimate
Final Comments
Companies, businesses and investments have been more exposed to uncertainties and risks that can jeopardize business. Thus, decision-making processes easily become quite complex due to the wide range of subjective issues, uncertainties, risk factors, management rules, guarantees and types of insurance products.
Liability management is the responsible administration of the liabilities of insurance contracts staff, who struggle to address a tailor made scope of policy, and end up adopting benchmark values, which poses several potential deviations, leading to biased strategic decisions.
In recent years, the need for further understanding of the potential losses that a business might be exposed to has attracted more and more attention as it relates to financial uncertainties more realistically than an analysis of a small number of deterministically given scenarios. Additional importance arises from the current need of insurance companies to move from an accounting based on book values to a market-based.
The approach presented earlier offers decision-makers a robust background of the risks inherent to their business, through the careful evaluation of the most critical accidental scenarios, in order to estimate the most likely losses for the worst cases in the system under assessment.
Unlike the traditional and benchmark analysis, this approach includes the assessment of the indemnity values considering the complexity of the accidental scenarios, the representativeness of each element of risk and loss in the composition of the total amount of indemnity.
As the loss estimate considers the possible uncertainties in the estimate of losses, by means a quantitative analysis the loss estimate is underpinned by values of confidence for risk appetite, leading to a more accurate risk-based decision making and a more efficient cash flow management.
ATTACHMENT
Pearson Correlation Coefficient
The Pearson correlation coefficient is a measure of the strength of a linear association between two variables. Basically, a Pearson correlation attempts to draw a line of best fit through the data of two variables, and the Pearson correlation coefficient, r, indicates how far away all these data points are to this line of best fit (i.e., how well the data points fit this new model/line of best fit).
The Pearson correlation coefficient, r, can take a range of values from +1 to -1. A value of 0 indicates that there is no association between the two variables. A value greater than 0 indicates a positive association; that is, as the value of one variable increases, so does the value of the other variable. A value less than 0 indicates a negative association; that is, as the value of one variable increases, the value of the other variable decreases. This is shown in the diagram below:
The Pearson correlation does not take into consideration whether a variable has been classified as a dependent or independent variable. It treats all variables equally. For example, you might want to find out whether basketball performance is correlated to a person's height. You might, therefore, plot a graph of performance against height and calculate the Pearson correlation coefficient. Lets say, for example, that r = .67. That is, as height increases so does basketball performance. This makes sense. However, if we plotted the variables the other way around and wanted to determine whether a person's height was determined by their basketball performance (which makes no sense), we would still get r = .67. This is because the Pearson correlation coefficient makes no account of any theory behind why you chose the two variables to compare. This is illustrated below:
It is important to realize that the Pearson correlation coefficient, r, does not represent the slope of the line of best fit. Therefore, if you get a Pearson correlation coefficient of +1 this does not mean that for every unit increase in one variable there is a unit increase in another. It simply means that there is no variation between the data points and the line of best fit. This is illustrated below:
There are five assumptions that are made with respect to Pearson's correlation:
(1) Marco Amaral – Risk Advisor Aon Global Risk Consulting
Marco holds a degree in Chemical Engineering from the Polytechnic School of the University Of Sao Paulo (EPUSP), a postgraduate degree in Safety Engineering from Universidade Paulista (UNIP), an MBA in Executive Management from COPPEAD Federal University of Rio de Janeiro (UFRJ). He works in business development and solutions for Risk Management of Projects and Business Risk Management, Process Safety and Reliability for business, design, engineering, manufacturing and construction, Risk Management for Due Diligence and Research for Risk Management corporate. Marco has extensive experience in Project Risk Management, and Business Process, experience with simulation of Capital Expenditure (CapEx), Schedule, Operating Expenditure through qualitative analysis, Monte Carlo simulation, 3-Point Analysis and Sensitivity Analysis, solid experience in coordination and evaluation of qualitative and quantitative techniques, experience in auditing of Safety Management System (PSM and RBPs), regulatory works under national and international regulatory bodies (INEA, ANP, etc.). He participated in capital projects in mining (iron ore, coal and potash), logistics (railways, pipelines, ports and export corridors continental), tankage, energy (power plants, hydroelectric), Oil & Gas in Brazil Argentina, Peru, Kuwait, Saudi Arabia, Dubai, South Korea, Mozambique, Malawi, South Africa and Trinidad and Tobago. Marco is Risk Management Training instructor and develops wide range of workshops on it.
(2) Alice Elisa De Poli – Senior Consultant, Aon Global Risk Consulting
Alice holds a bachelor's degree in law from the Federal University of Paraná (UFPR), a master's degree in Political Sociology from UFPR, a post-graduate degree in Political Science from the IBPEX (master's degree), specialization and updating in Lato Sensu by the Brazilian Institute of Legal Studies (IBEJ), and specialization in Real Estate Law by FMU - São Paulo / SP; She is a lawyer in the Civil and Public Law areas. Alice is a post-graduate professor in Logistics at the Centro Universitário de Maringá (Consumer Law), a postgraduate professor at the ICPG in Santa Catarina (Corporate Tax Law), an IBPEX MBA professor in Curitiba (Company Law), a professor of the MBA at UNIUV (Tax Law, Social Security and Business Law), UNINTER Graduate Professor (Corporate Tax Law). Alice carries out technical-legal consultancy in Civil Responsibility and Environmental Responsibility for application in Risk Management of Projects, Processes, Business, and Insurance. It conducts research and analysis of indemnity amounts to estimate losses for modeling of loss estimate and insurance coverage limits. Alice performs contract analysis and legal solutions (Action Plan), with emphasis on General Liability and Environmental Liability, for application in Capital and Business Project Management. Alice teaches a portfolio of dozens of courses in Law.
Marco Amaral – Risk Advisor and Consultant (1) Alice De Poli – Senior Consultant (2)
SUMMARY
A lucrative business usually has high stakes as well as high potential gain. In a world of competing resources, specific skill sets, and contracted services, losses are a direct result of factors not only inherent to the business itself but also often of external factors.
The competitive advantages of environmental management are expressed in a global, macroeconomic context. Environmental management, combined with financial management, provides cost reduction, improves the outlook for the public, consumers and global society as a whole.
This article presents the development of loss estimating based on the most potential accidents that can take place in operational assets, leading to consequences on their business, based on quantitative loss estimate, with an emphasis on environmental liability, general liability potential losses; and insurance program placement.
INTRODUCTION
Today, environmental and general liability areas very important issues on several fronts: social, economic, political, etc. In order to manage the concept of damage, it is very important to cross-check data with the various areas of knowledge in order to guide the client, not only in the pre-contractual and defensive issues, but also in the material part of the damage. In this context, values, moral damages, environmental damages and material damages are included, as well as emergent damages and loss of profits.
An analysis of environmental and general liability will motivate the company at all levels bringing new social design, with a view to reducing security costs and not paying for an environmental recovery. This is a strategic issue in many corporations today, given the rising cost of managing the company and the growing scarcity of natural resources.
The third wave, described by Toffler as early as 1995, with the introduction of concepts and new technologies in a holistic way is relevant, in order to seek greater integration and harmony with nature. This will trigger:
A- Community-government-company relationship improvement;
B- Elimination or reduction of costs with materials and personnel;
C- Monitoring of problems in the law and environment;
D- Reduction of costs with insurance;
E- Ensuring greater profit management.
Performing a risk analysis before placing insurance limits on capital projects will do more than save time, effort, and resources down the line.
The importance of loss estimate decisions increases as the performance of the strategy of the business becomes more visible at the board and stakeholder levels. The assessment of potential revenue generation in the context of high-risk business environments helps determine both insurance limits values and the contract types that should be pursued. Furthermore, knowing the risks of a business at an early phase within its lifecycle, by the means of a corporate risk culture will enable the implementation of a continued dissemination of cost and revenue data and intelligence for the business.
The perception of economic attractiveness or lack of it in certain scenarios, shall allow decision makers to form an opinion about business opportunity, knowing the fundamental business threats, i.e., economics, market and structural elements causing negative or positive impact on the opportunity being analyzed.
In regard to business investment, we put a system in place to identify and analyze risks that may impact client business goals in terms of time and budget. Risk analysis being performed before bids, M&A processes and project design, organizations may expect a better perception of unforeseen risks, regarding insurance placement, and more confidence to achieve their business goals.
QUANTITATIVE LOSS ESTIMATE
The methodology used for risk analysis is a programmatic approach, which focuses at analyzing the uncertainties of loss estimate, through a probabilistic assessment of legal processes and negotiations in the court and other legal organizations. It is worth noting these uncertainties can relate to several factors.
The execution of a loss estimate analysis both validates losses forecasts as well as pinpoints the elements of the business that are the biggest risk drivers (opportunities or threats). Business that appears theoretically very lucrative can actually be contributing the most to reduced revenues due to uncertainty.
The Integrated Quantitative Risk Analysis aims to determine the likelihood that the proposed business meets its goals, and provide limits for insurance placement from loss estimate based on accidental scenarios so that improvements can be made to achieve its success, ensuring the expected return on investment.
The analysis comprises scenarios relating to damage to assets, environmental impact, damage to third parties, and potentially legal consequences, on investment goals and time frame of the project.
On a regular basis, the identification of risks is performed through filed surveys, documents analysis, qualitative and quantitative analysis, technical meetings with stakeholders, including staff from different departments with various areas of knowledge.
The input data collection is made by the means of research in jurisprudence and leniency agreements according to available official sources and facts that have already happened.
Based on the selected scenarios and data and information relating to jurisprudence and agreements among parties involved in the legal processes, a framework for Monte Carlo modeling is built to obtain the quantitative loss estimate in terms of financial results.
The importance of legal research focused on accidental scenarios.
Legal research in the country's accidental scenarios, whether in doctrine (theoretical research) or jurisprudence (empirical research), has expanded because of increased access to judicial decisions. The important thing is that this research method is coherent to answer the exact question of what, how and why judges are judging the liability of a particular outside. That is, it specifies data qualitatively and exposes the numbers of civil responsibility quantitatively and graphically!
The research of jurisprudence can be useful since its study helps to understand the ways of applying the legal norms. It has traditionally been qualitative in character. The arguments used in case law are evaluated, as well as the saturation of evidence, the relevance of the interpretive methods used and a series of other criteria.
Equally necessary and relevant is quantitative research. Whether, how and how often a court decides on a relevant issue is of crucial importance to a securitized firm in terms of civil liability. In addition, legal research is necessary to understand the behaviors or conduct and standing of the judging class in decisions, their origins and consequences, cultural, economic or political.
The matters of civil liability are judged by the Brazilian Supreme Court, a court that publishes its decisions integrally, which facilitates the investigation and provides reliable data that provides the basis for any argument and report in the desired area, in the case, accident area. Compilation of this information gives the client greater security at the time of a business transaction.
METHODOLOGY
When collecting data, it is important to look at the mathematical operations that can be performed on values, since not all values are adequate to be manipulated by the various types of operations.
Selection of data for loss estimate is usually associated with the following factors:
- Hazardous materials;
- Location of assets;
- Neighboring assets and population vulnerability;
- Regulatory agency competence;
- Prosecutor competence;
- Accidents and losses jurisprudence.
The quantitative loss estimate assessment contemplates the quantitative analysis of losses from accidental scenarios that are considered most critical, that is, loss factors that arise from uncertainties that assume values expressed in scales of reason, that is, values that can be added, subtracted, multiplied or divided. Such quantitative risk factors allow the construction of stochastic models to analyze the behavior adopted in the definition of values of losses and indemnities.
In constructing stochastic models, it is important to consider two types of quantitative risk factors:
A) Discrete: values considered in sets of data in which between any two elements there is a finite amount of elements that belong to the set.
B) Continuous: values considered in sets of data in which between any two elements there is an infinite amount of elements that belong to the set.
Given that case-law data and indemnity values from leniency agreements do not allow loss values to be directly obtained for the selected scenarios, correlation analysis should be performed combining the values of product loss, indemnity amounts claimed and / or negotiated, geographic, socioeconomic, political and legal characteristics of the regions from which the data were obtained, and prosecutor knowledge and perception in order to verify the existence of robust correlation between those parameters.
Such issues are mostly subjective. Non-parametric correlation measurements should be analyzed, that is, an arbitrary monotonous function should be evaluated, which can be the description of the relationship between the variables, without making any assumptions about the frequency distribution of the variables.
Unlike the Pearson correlation coefficient (see attachment), for example, the analyzed correlations do not require the assumption that the relationship between the variables is linear, nor does it require that the variables be measured in the class interval; it can be used for the variables measured at the ordinal level.
Through a random selection of values according to a probability distribution defined by the user, the Monte Carlo simulation is a statistical technique, in order to describe the distribution and characteristics of possible behaviors of a dependent variable.
Monte Carlo simulation, or probability simulation, is a technique used to understand the impact of risk and uncertainty in financial, project management, cost, and other forecasting models.
The data obtained from the linkage of risk and input data are fed in the model, so that the foreseen losses are estimated through Monte Carlo simulation.
A probabilistic distribution is developed and the results are displayed by means of Cumulative Probabilistic Curves and percentiles results.
The result of this analysis presents the simulation of potential losses, divided into levels of probability of confidence, according to risks associated with the elected accidental scenarios.
ENVIRONMENTAL AND GENERAL LIABILITY LOSS ESTIMATE IN THE BRAZILIAN MARKET
In order to evaluate potential correlations between the accidental scenarios and jurisprudence, this current study was developed taking into account the following background, bearing in mind compensation and indemnification:
The Brazilian liability legislation covers three types of damages:
- Damage caused by death or personal injury;
- Damage to, or a destruction of, any item of assets of third property;
- Moral and material damage.
In liability insurance there is a warranty obligation, and regarding the other ensuring rights, i.e., the obligation to be guaranteed the payment of a debt (compensation) of responsibility, and the responsibility of the insured. Now the other insurance of damage have to cover a direct loss in the patrimony of the legal entity and third parties who are affected by the accident.
As to the responsibility for injury, it is important to bear in mind that, not only the voluntary fraudulent acts, but also those by negligence and omission will result in the imposition on the author of the damage to repair it. Statistics show that 96 % of the losses are caused by the attitudes and human behavior and that 4 % are caused by several factors, including where he has no control. These data confirm the importance of a systemic approach, based on the root causes of losses, attitudes and in human behavior.
Legally, the economic capacity and the social position of the company indicate the concepts and structures of general liability. In spite of the responsibility in this exclusive theme of damage being taken on the basis of damage in re ipsa (presumed damage), the case law allows the adoption, as a rule, of the possibility of demanding compensation for damages caused by circumstances where the person responsible cannot be regarded as author in the ethical-legal, but rather the ethical, level.
Theoretically, in cases of serious accidents social solidarity, requires that those who caused the damage, support the consequences. However, in Brazil, there is the criminal law of the aggressor, where the victim is vulnerable to major corporate interests, to the rule of time of judgment and of the demand for custody ruling by the judiciary. In such cases, the courts were called upon to decide whether the public interest warrants the application of an implicit exception by eliminating coverage for punitive damages.
EXAMPLE
Case: Petrochemical plant
Objective: Rating the importance indemnification for losses resulting from industrial accidents in terms of Liability
Types of losses to be assessed:
- Loss of neighboring businesses assets;
- Loss of assets in residential areas;
- Loss of profits in neighboring companies;
- Fatalities on the general public;
- Fatalities among workers and third part neighboring;
- Damage to the environment;
- Costs of legal implications;
We carried out an analysis of similar systems and regions.
An assessment of possible impacts versus vulnerability in the vicinity is made considering its features:
- urban / country
- residential / industrial
Traditional Approach
- Vulnerable area with larger radius
- Vulnerability on the 3rd part
- Vulnerable area with larger radius
- Vulnerability of the 3rd party
- Causality regardless of the magnitude (ex .: domino effect, failure mode, etc.)
- Influence and interdependence on other factors (eg. Leak into rivers, transport, etc.)
- Specific levels of vulnerability for people and assets
Figure 1 – Worst case (higher impact plus domino effect)
Based on the traditional approach, a worst case presents the following magnitude:
- Larger effect radius (blue – 349yd) presents major individual vulnerability
- Domino effect (yellow – 218yd) has a high probability of occurrence increases the exposure of people
Figure 2 – Worst case (highest impact radius vs highest impact on people)
Based on the traditional approach, a worst case presents the following magnitude:
- Larger effect radius (Poolfire – 211yd - blue) no vulnerability on industrial assets
- Effect reaching 164yd (Jetfire - yellow) was the one considered for analysis
Selection of the Worst Cases – Relevant Premises
- The selection of the worst case is not a rule for the most far-reaching scenario
- The worst case should be associated with the largest impact
- Conditional probabilities should be evaluated and when representing the estimated losses should consider the sum of interrelated scenarios
- The underestimation of the worst case leads to underestimation of losses for compensation
- The underestimate of loss leads to an inadequate insurance coverage and impact on the business cash flow
In the traditional approach, each loss has an estimated cost and the total cost of compensation is given simply by the sum of the maximum costs of compensation.
The problem becomes more complex when the cost of each loss has uncertainties and, therefore, the total cost of losses will also contain uncertainties.
The traditional model does not address the inherent uncertainty of loss values for the possible accident scenarios
Traditional Approach Estimate
Loss estimate value by Traditional Approach (Benchmark): $ 83,741,000
Constraints:
- Potential underestimated or overestimated losses for worst case scenario
- Uncertainties not considered for loss estimate
According to the results from Quantitative Approach graph, the level of confidence for the Benchmark value is: 9% ($ 83,741,000)
Quantitative Approach Estimate vs Traditional Approach Estimate
Final Comments
Companies, businesses and investments have been more exposed to uncertainties and risks that can jeopardize business. Thus, decision-making processes easily become quite complex due to the wide range of subjective issues, uncertainties, risk factors, management rules, guarantees and types of insurance products.
Liability management is the responsible administration of the liabilities of insurance contracts staff, who struggle to address a tailor made scope of policy, and end up adopting benchmark values, which poses several potential deviations, leading to biased strategic decisions.
In recent years, the need for further understanding of the potential losses that a business might be exposed to has attracted more and more attention as it relates to financial uncertainties more realistically than an analysis of a small number of deterministically given scenarios. Additional importance arises from the current need of insurance companies to move from an accounting based on book values to a market-based.
The approach presented earlier offers decision-makers a robust background of the risks inherent to their business, through the careful evaluation of the most critical accidental scenarios, in order to estimate the most likely losses for the worst cases in the system under assessment.
Unlike the traditional and benchmark analysis, this approach includes the assessment of the indemnity values considering the complexity of the accidental scenarios, the representativeness of each element of risk and loss in the composition of the total amount of indemnity.
As the loss estimate considers the possible uncertainties in the estimate of losses, by means a quantitative analysis the loss estimate is underpinned by values of confidence for risk appetite, leading to a more accurate risk-based decision making and a more efficient cash flow management.
ATTACHMENT
Pearson Correlation Coefficient
The Pearson correlation coefficient is a measure of the strength of a linear association between two variables. Basically, a Pearson correlation attempts to draw a line of best fit through the data of two variables, and the Pearson correlation coefficient, r, indicates how far away all these data points are to this line of best fit (i.e., how well the data points fit this new model/line of best fit).
The Pearson correlation coefficient, r, can take a range of values from +1 to -1. A value of 0 indicates that there is no association between the two variables. A value greater than 0 indicates a positive association; that is, as the value of one variable increases, so does the value of the other variable. A value less than 0 indicates a negative association; that is, as the value of one variable increases, the value of the other variable decreases. This is shown in the diagram below:
The Pearson correlation does not take into consideration whether a variable has been classified as a dependent or independent variable. It treats all variables equally. For example, you might want to find out whether basketball performance is correlated to a person's height. You might, therefore, plot a graph of performance against height and calculate the Pearson correlation coefficient. Lets say, for example, that r = .67. That is, as height increases so does basketball performance. This makes sense. However, if we plotted the variables the other way around and wanted to determine whether a person's height was determined by their basketball performance (which makes no sense), we would still get r = .67. This is because the Pearson correlation coefficient makes no account of any theory behind why you chose the two variables to compare. This is illustrated below:
It is important to realize that the Pearson correlation coefficient, r, does not represent the slope of the line of best fit. Therefore, if you get a Pearson correlation coefficient of +1 this does not mean that for every unit increase in one variable there is a unit increase in another. It simply means that there is no variation between the data points and the line of best fit. This is illustrated below:
There are five assumptions that are made with respect to Pearson's correlation:
- The variables must be either interval or ratio measurements.
- The variables must be approximately normally distributed.
- There is a linear relationship between the two variables.
- Outliers are either kept to a minimum or are removed entirely.
- There is homoscedasticity of the data.
(1) Marco Amaral – Risk Advisor Aon Global Risk Consulting
Marco holds a degree in Chemical Engineering from the Polytechnic School of the University Of Sao Paulo (EPUSP), a postgraduate degree in Safety Engineering from Universidade Paulista (UNIP), an MBA in Executive Management from COPPEAD Federal University of Rio de Janeiro (UFRJ). He works in business development and solutions for Risk Management of Projects and Business Risk Management, Process Safety and Reliability for business, design, engineering, manufacturing and construction, Risk Management for Due Diligence and Research for Risk Management corporate. Marco has extensive experience in Project Risk Management, and Business Process, experience with simulation of Capital Expenditure (CapEx), Schedule, Operating Expenditure through qualitative analysis, Monte Carlo simulation, 3-Point Analysis and Sensitivity Analysis, solid experience in coordination and evaluation of qualitative and quantitative techniques, experience in auditing of Safety Management System (PSM and RBPs), regulatory works under national and international regulatory bodies (INEA, ANP, etc.). He participated in capital projects in mining (iron ore, coal and potash), logistics (railways, pipelines, ports and export corridors continental), tankage, energy (power plants, hydroelectric), Oil & Gas in Brazil Argentina, Peru, Kuwait, Saudi Arabia, Dubai, South Korea, Mozambique, Malawi, South Africa and Trinidad and Tobago. Marco is Risk Management Training instructor and develops wide range of workshops on it.
(2) Alice Elisa De Poli – Senior Consultant, Aon Global Risk Consulting
Alice holds a bachelor's degree in law from the Federal University of Paraná (UFPR), a master's degree in Political Sociology from UFPR, a post-graduate degree in Political Science from the IBPEX (master's degree), specialization and updating in Lato Sensu by the Brazilian Institute of Legal Studies (IBEJ), and specialization in Real Estate Law by FMU - São Paulo / SP; She is a lawyer in the Civil and Public Law areas. Alice is a post-graduate professor in Logistics at the Centro Universitário de Maringá (Consumer Law), a postgraduate professor at the ICPG in Santa Catarina (Corporate Tax Law), an IBPEX MBA professor in Curitiba (Company Law), a professor of the MBA at UNIUV (Tax Law, Social Security and Business Law), UNINTER Graduate Professor (Corporate Tax Law). Alice carries out technical-legal consultancy in Civil Responsibility and Environmental Responsibility for application in Risk Management of Projects, Processes, Business, and Insurance. It conducts research and analysis of indemnity amounts to estimate losses for modeling of loss estimate and insurance coverage limits. Alice performs contract analysis and legal solutions (Action Plan), with emphasis on General Liability and Environmental Liability, for application in Capital and Business Project Management. Alice teaches a portfolio of dozens of courses in Law.