Computer-implemented method for evaluating improvement plans for a plant facility

Abstract

A computer-implemented method of evaluating an improvement plan for a plant facility can include: selecting at least one plant-related operational measure, the at least one plant-related operational measure including at least one production efficiency improvement; wherein the at least one plant-related operational measure includes at least one first target value for engineering unit production; measuring at least one first actual value, the at least one first actual value including engineering unit production measurements recorded over a predetermined time period; comparing the at least one first target value to the at least one first actual value; generating an action plan to achieve the at least one first target value no later than a target date based on the at least one first actual value; determining a probability of achieving the at least one first target value from a Monte Carlo analysis of a range of possible action plan outcomes based on the at least one first actual value; and implementing the at least one operational measure according to the action plan when the probability of achieving the at least one first target value is greater than a limiting probability of success.

Description

[0001] Cross-reference to related applications This application is a divisional application of a PCT international application filed on March 17, 2014 (application number PCT / US2014 / 030236, entitled "AN INSURANCE PRODUCT, RATING AND CREDIT ENHANCEMENT SYSTEM AND METHOD FOR INSURING PROJECT SAVINGS"), which entered the Chinese national phase on October 29, 2015 (application number 201480024381.5, entitled "Insurance Products, Rating and Credit Enhancement System and Method for Insurance Project Savings"). The aforementioned PCT international application is a partial continuation of a previous U.S. patent application sequence No. 13 / 458,598, filed on April 24, 2012, entitled "INSURANCE PRODUCT, RATING SYSTEM AND METHOD," which in turn was filed on June 15, 2005, entitled "INSURANCE PRODUCT, RATING..." The divisional patent application of the previous U.S. patent application sequence No. 11 / 153,305 for “SYSTEM AND METHOD” is incorporated herein in its entirety.

[0002] Statement regarding federally funded research or development: Not applicable.

[0003] References to the microfilm appendix: Not applicable. Technical Field

[0004] This disclosure relates to a computer-implemented method, and more specifically, to a computer-implemented method for evaluating improvement plans for plant facilities. Background Technology

[0005] Pricing and rating methods for property and property-related asset performance insurance products can be categorized into two types: value-based (VB) rating and frequency-severity (FS) rating. In both cases, the insurance cost is directly related to potential financial loss, but the calculation method reflects the characteristics of the insured property or asset.

[0006] VB ratings are typically used when the risk or potential loss can be characterized by a range of variables. For example, the potential loss and pricing for a new car can be determined by the type of vehicle, the type of loss (e.g., collision, liability, windshield), mileage and type driven, the insured's driving record, geographic location, and other possible variables. Given these variables, potential losses can be analyzed and a table can be generated, allowing the insurer to look up the rate expressed in USD premium / USD coverage. The insurer typically multiplies customer-specific variables by the corresponding rate and then adds company-specific administrative costs to calculate the total policy premium.

[0007] For property VB insurance, some commonly used underwriting variables are business type, building activity (e.g., hospital, office building, laboratory, etc.), other attributes such as square footage or dimensions, structural attributes, automatic sprinkler coverage, number of stories, location, and age. The expressed premium rate is typically categorized using these variables and generated together. This value is multiplied by the building value to produce the policy premium. The actual premium value will vary depending on historical pricing precedents, market demand, policy term and conditions, coverage type, and property replacement value.

[0008] FS pricing is a rating and pricing methodology used in situations where there can be significant differences among insureds in the same type of industry and geographic region. In this methodology, the probability or frequency of insurance claims or failures (accidents / year) can be modeled or obtained directly from available data.

[0009] Engineering and underwriting risk adjustment factors are factors applied to calculate premiums based on the adjusted loss costs for specific customer attributes occurring under current circumstances. For example, a 10% engineering risk adjustment factor increasing loss costs could be applied to customers with poor record-keeping and facility cleanliness processes. Engineering inspectors have identified these behaviors as highly correlated with customers who will likely have insurance claims. A 10% underwriting risk adjustment factor can reduce policy premiums if a high deductible and limited coverage have been negotiated with the customer. These engineering and underwriting risk adjustment factors make detailed premium changes based on the customer's specific attributes and the policy terms and conditions.

[0010] An example of the FS pricing method used for customers is applied in... Figure 1The equipment breakdown premium development for power plant 100 is shown in the figure. The power plant has two (2) simple-cycle GE 7FA turbine generators 102, 104 and two (2) transformers 106, 108, as well as various types of electrical switchgear and equipment (only switch 110 is shown). The first part of the premium calculation includes determining the frequency and severity of the loss cost components of the premium. Risk correction factors exist to tailor the loss cost components for the specific customer being analyzed. These factors can increase or decrease credit and debit percentages, allowing the insurer to modify the loss cost to reflect the customer's essential attributes (e.g., engineering factors), such as housekeeping, record keeping, reliability programs, the quantity of available spare parts, and underwriting factors such as the selected deductible value.

[0011] The next part of premium calculation determines customer-specific fees, costs, and profits. Another component of premium calculation—potential excess loss—refers to the premium component that accounts for the cost of loss events that are very infrequent but of very high severity and are suitable for that customer. Examples of such events include once-in-500-year earthquakes, tsunamis, and hurricanes. The severity of these events can be determined using specialized disaster modeling software. A portion of the insurer's total potential losses can be allocated to each customer as the potential excess loss component of the premium.

[0012] Clients may also be subject to engineering inspections linked to judicial requirements by the state or other government agencies. The underwriting process also includes certain client-specific costs associated with meetings, travel, etc.

[0013] Costs considered during the underwriting process may also include reinsurance costs, which are typically added when the underwriter purchases authorized reinsurance—reinsurance on a specific account. However, other costs involving portfolio, scope of business, divisional, or account-level apportionments may also be added. Other premium costs are typically taxes, brokerage commissions, profit margins, and other premium cost additions specified in the company's underwriting guidelines.

[0014] The following shows the FS pricing used in the above example, which is used to break down insurance prices for the equipment built for a simple cycle gas turbine power generation facility:

[0015] Policy rating and pricing applied to property-related insurance typically employ a combination of the VB (Business Value) and FS (Fixed Value) methods. The insured's (customer's) property often comprises highly specialized equipment and a mix of other activities common to many similar types of locations. A customer's power generation company may have a small number of highly specialized power generation locations that can be rated and priced using the FS method, but also several branch offices where premiums can be calculated using the VB method.

[0016] Furthermore, improvement projects take many forms. Some improvement projects involve new construction activities designed to generate sufficient returns to cover capital costs and ongoing expenses such as pipelines, toll roads, or bridges. Other improvement projects may involve the modification and reconstruction of existing structures to increase output, reduce energy costs, lower operating costs, meet emission standards, improve reliability, increase safety, or other activities that benefit the organization.

[0017] The design and implementation of these activities typically involve applying for capital in some form. Some companies fund these projects from internal resources. However, many projects seek external funding, which involves requesting loans from banks, investors, and other financial institutions for the projects that will result.

[0018] Financial underwriters closely examine many aspects of a project developer's creditworthiness and financial history. Depending on the lender's experience and understanding of technology, they also review project specifications in terms of internal rate of return, return on investment, and other performance indicators.

[0019] Generally, lenders require projects to be covered by standard insurance risks widely available in the market, such as builder's risk, all risks of property, general liability insurance, and others. These risk transfer instruments help reduce the lender's repayment risk and are considered a fundamental attribute of many project financing transactions. Therefore, while they effectively reduce project risk and thus credit risk, these insurances are considered a necessary prerequisite for financing transactions and are therefore not taken into account in credit rating analysis.

[0020] The various embodiments disclosed herein are generally related to the embodiments disclosed in U.S. Patent No. 8,195,484 entitled "Insurance Product, Rating System and Method," which is incorporated herein in its entirety and relates to a rating and pricing system for quantifying and insuring that annual savings will not fall below a specified level associated with achieving and maintaining economic growth. Specifically, the various embodiments disclosed herein relate to credit enhancement aspects and functions that include the insured's savings flow in loan repayment schedules. Summary of the Invention

[0021] The embodiments disclosed herein relate to credit enhancement systems and methods, and generally to ways in which an insured project savings base (i.e., a minimum level of investment return for a given enhancement project) can be achieved through a better credit rating for bonds or loans associated with the enhancement project. The project savings base or minimum level of outcome can refer to individual and different annual returns for a multi-year policy or cumulative returns over the multi-year policy term.

[0022] As used herein, “savings” can be tangible or intangible and include, but is not limited to, increased revenue; reduced operating costs, maintenance costs, and capital expenditures; increased production throughput; reduced energy consumption; reduced emissions; increased emission allowances; and so on. These savings will generate additional benefits for customers in the form of enhanced credit, which leads to increased availability of financing and reduced financing costs. Those skilled in the art will recognize that the scope of the various embodiments disclosed herein includes other savings and benefits not specifically set forth in the above list.

[0023] The disclosed embodiments are not limited to implying a higher rating for a specific project loan solely due to the effective reduction of repayment pressure resulting from the inclusion of project savings in credit risk calculations. More precisely, the disclosed embodiments also pertain to quantifying credit enhancement solely based on the inclusion of project savings and insured principal. Other factors beyond these can also influence the rating process and require consideration by those skilled in the art in applying the specific rating methodology to the project under consideration.

[0024] Insurance pricing systems that may have a large amount of available risk and loss data use standard statistical and probabilistic methods. Policies are typically standardized in format and simplified to the point where the underwriter constructs the premium from a table, where risk attributes such as the insured's age, vehicle type, location, or building value are key elements used to find the appropriate rate. Other insurance policies, such as property insurance policies, may include premium components developed based on catastrophic models, where the models estimate losses due to natural disasters such as earthquakes.

[0025] Insurance pricing systems are typically designed for products sold on an annual basis to a large number of customers, each with a relatively small potential loss. Conversely, disclosed embodiments often include insurance product rating and pricing systems designed for a relatively small number of insured individuals, each with a relatively large risk over an annual or multi-year period. The latter approach cannot rely on the statistical principle of the law of large numbers, but instead applies as much knowledge and real-world performance data as possible to risk analysis development and subsequent premium development.

[0026] The insurance policy rating and pricing systems and methods disclosed herein, according to some embodiments, are based on risk analysis, in which actual performance data, technological uncertainties, and other factors are combined to form input information to the pricing system. An input file, referred to as the annual cumulative risk distribution, quantifies the net performance risk of all measures used to achieve net annual savings for each year of the policy term. For example, improvement procedures may include labor reallocation, process redesign, installation of advanced process controls, and energy efficiency capital projects. However, the various embodiments disclosed herein are not limited to this. As another example, it is also applicable to other methods capable of quantifying the total net annual savings risk and the credit enhancement attributes of hundreds of potential measures. These risk distributions quantify the probability of exceeding a given net annual savings value and are used as basic input files, data, or equations according to the disclosed embodiments. Therefore, the embodiments disclosed herein facilitate risk-based decision-making systems and methods to help assess project savings risk, credit risk improvement based on project savings, and value created due to insuring a minimum savings level.

[0027] The cumulative risk distribution, similar to that used in developing property insurance, is defined for each location. Underwriting can be performed first at the location level and then observed annually or cumulatively over the project period at the customer level. At least one novel aspect of some embodiments involves enabling the insurer to develop pricing under different levels and time periods. At the location level, a cumulative risk distribution is formed for a subset of all measures designed to be implemented at that location. At the customer level, only one cumulative risk distribution is generated for each year or other time period.

[0028] While the disclosed embodiments are generally discussed from the perspective of pricing at a single location or at a single customer tier, multi-customer pricing systems are also within the scope of the disclosed embodiments. As used herein, "multi-customer" includes, but is not limited to, one or more investors in any arrangement or combination of ownership and / or geographic location in one or more facilities such as power, oil refining, chemical, manufacturing facilities, etc.

[0029] One embodiment is a computer-implemented method for evaluating credit rating upgrades for project loans associated with an improvement plan for a facility, embodied in computer-executable instructions stored in a non-transitory computer-readable medium, which, when executed, cause a computer system having memory and a processor specifically programmed by the computer-executable instructions to perform a method for pricing an insurance policy. The method includes: receiving loan interest rate and default data; receiving a user-specified loan amount, loan term, and payment schedule; receiving project savings distribution data; receiving the project savings value required by the insured; determining an equivalent credit risk relationship; calculating the probability of achieving the required project savings to achieve an equivalent credit rating; and calculating credit risk savings based on the probability of possible credit enhancement.

[0030] Another embodiment is a computerized system for assessing credit rating upgrades for project loans associated with an improvement plan for a facility, comprising: a display device for prompting a user to input loan interest rates, default data, loan amounts, loan terms, and payment schedules; a storage device for receiving and storing project savings distribution data and the insured's minimum savings level; and a processor specifically programmed to: determine equivalent credit risk relationships, calculate the probability of achieving the required project savings to achieve an equivalent credit rating, and calculate credit risk savings based on the probability of possible credit enhancement.

[0031] Other embodiments utilize a computer system to assess credit enhancements associated with the implementation of an insured's improvement plan, wherein the computer system includes an input unit, a processor, a storage unit, and an output unit. The method includes: developing a project improvement plan; compiling project improvement distribution data; receiving input data from the input unit, the input data including loan interest rates, rating categories corresponding to the loan interest rates, probability of default, loan amounts, terms, and payment schedules; and using the storage unit to store the project improvement plan data and the input data. These embodiments also include using the processor to: calculate the insured savings base; calculate the project savings distribution based on the insured savings base and the project improvement distribution data; calculate net periodic loan payments based on the project savings distribution and the input data; calculate the default amount for each rating category at each segment level of the applied project savings; and calculate the average loss due to default for each segment level of the applied project savings for each rating category, for periodic payments. Some embodiments also include using the storage unit to store the average loss due to default and using the output unit to display the calculated data.

[0032] Other embodiments relate to a method of operating a computer system to calculate possible equivalent risk relationships, the computer system comprising: an input unit for inputting data into the system, a processor for calculating the input data, an output unit for displaying the calculated data, and a storage system for storing the calculated data. The method includes the steps of: inputting data via the input unit, wherein the input data includes loan interest rates and default probabilities, user-specified loan amounts, terms and payment schedules, and project savings distribution; calculating, via the processor, net term loan payments in combination with different confidence levels of project savings based on the input data; calculating, via the processor, the default amount for each rating category at each segment level of the applied project savings; calculating, via the processor, the average loss due to default for each segment level of the applied project savings for term payments at each rating category; storing the average loss due to default via a storage device; and displaying the calculated data via the output unit.

[0033] Other embodiments relate to a computer system including an input unit for inputting data into the system, a processor for calculating the input data, an output unit for displaying the calculated data, a storage system, and a computer program stored in the storage system including instructions, wherein when the instructions are executed, the processor calculates, based on the input data, net term loan payments in combination with different confidence levels of project savings, calculates the amount of default for each rating category at each segment level of the applied project savings, and calculates the average loss in default for each rating category at each segment level of the applied project savings for term payments.

[0034] Other embodiments involve a computer program for calculating possible equivalent risk relationships, the computer program being stored in a storage system of a computer system including an input unit for inputting data into the system, a processor for calculating the input data, and an output unit for displaying the calculated data. The computer program includes instructions that, when executed, cause the processor to calculate, based on the input data, net term loan payments combined with different confidence levels of project savings, calculate the default amount for each rating category at each segment level of the applied project savings, and calculate the average loss in default for each rating category at each segment level of the applied project savings for term payments.

[0035] The disclosed systems and methods provide a fast and reliable way to provide data on project savings bases or minimum savings levels that can be achieved in the form of better credit ratings for bonds or loans associated with the improvement project. The benefits obtained from embodiments of the various disclosed computer systems are the same as those described with respect to embodiments of the disclosed methods. Attached Figure Description

[0036] A better understanding of the invention can be obtained by considering the following detailed description of preferred embodiments in conjunction with the accompanying drawings, in which: Figure 1 This is a block diagram of a power plant.

[0037] Figure 2 It is a flowchart of an embodiment of the product, system, or method for which protection is claimed.

[0038] Figure 3 It is a flowchart of an embodiment of the product, system, or method for which protection is claimed.

[0039] Figure 4 It is a flowchart of an embodiment of the product, system, or method for which protection is claimed.

[0040] Figure 5A It is a flowchart of an embodiment of the product, system, or method for which protection is claimed.

[0041] Figure 5B It is a flowchart of an embodiment of the product, system, or method for which protection is claimed.

[0042] Figure 6A It is a flowchart of an embodiment of the product, system, or method for which protection is claimed.

[0043] Figure 6B It is a flowchart of an embodiment of the product, system, or method for which protection is claimed.

[0044] Figure 7A is a spreadsheet of embodiments of the claimed product, system, and method.

[0045] Figure 7B is a spreadsheet of embodiments of the claimed product, system, and method.

[0046] Figure 8 It is a flowchart of an embodiment of the product, system, or method for which protection is claimed.

[0047] Figure 8 A is a table of embodiments of the claimed product, system, and method.

[0048] Figure 8 B is a diagram of an embodiment of the claimed product, system, or method.

[0049] Figure 8C A diagram is a representation of an embodiment of a product, system, or method for which protection is claimed.

[0050] Figure 9 A diagram is a representation of an embodiment of a product, system, or method for which protection is claimed.

[0051] Figure 10It is a system block diagram of one embodiment of the claimed product, system, and method.

[0052] Figure 11 It is a diagram of an insurance policy that protects the products, systems, and methods required to be protected.

[0053] Figures 12A-12D This is a table of embodiments of the products, systems, and methods for which protection is claimed.

[0054] Figure 13 This is a flowchart illustrating an example of an insurance product, rating, and credit enhancement system and method used to insure project savings.

[0055] Figure 14 A flowchart is shown for determining the insured savings base for a project.

[0056] Figure 15 An example of how to determine the insured savings base is shown.

[0057] Figure 16 An example is shown of how insurance can modify the calculated cumulative project savings risk distribution.

[0058] Figure 17 This is a flowchart showing the integrated calculation of the risk distribution of the accumulated project savings of the insured principal and the project credit risk.

[0059] Figure 18 An example of a graphical representation of project information arranged in a tabular format is shown.

[0060] Figure 19 This is a spreadsheet illustrating an example of converting default loss (i.e., credit risk) values ​​into credit enhancement values.

[0061] Figure 20A This is an example spreadsheet that shows a table of probability values ​​for savings exceeding the amount required for a given credit rating upgrade.

[0062] Figure 20B This is an example spreadsheet illustrating the credit risk reduction (i.e., savings) value for a given credit rating upgrade.

[0063] Figure 20C This is an example spreadsheet illustrating the project savings value required to achieve the corresponding credit rating upgrade.

[0064] Figure 21 This is an exemplary diagram used in the embodiments, which illustrates the total project-level cumulative savings risk distribution for determining project savings.

[0065] Figure 22A and Figure 22BThis is an exemplary spreadsheet of the embodiment, which shows the credit enhancement probabilities based on savings in projects with and without insured bases.

[0066] Figure 23A and Figure 23B This is an exemplary spreadsheet of the embodiment, which shows the possible credit enhancement values ​​based on project savings with and without an insured base.

[0067] Figure 24 Examples of insurance products, rating and credit enhancement systems for insuring project savings are shown. Detailed Implementation

[0068] The insurer first determines the dollar value of the insured amount for each year, such as in Figure 2 As shown in step 200. This can be performed by specifying a confidence level used to return the minimum insured savings value indicated or calculated for all years or confidence levels, and can be applied on an annual basis. A unique feature of this invention is the selection of the insured base by first specifying an explicit confidence level. For the purposes of this invention, the “confidence level” is defined as the probability that annual savings will exceed the insured base value. Performing this function is called risk acceptance. For each policy year, the insurer selects, in step 202, the risk acceptance level that they consider to represent insurable positions under the terms and conditions of the policy. The insured base is also referred to as the risk acceptance threshold because, at steps 204 and 206 respectively, if the insured’s annual savings result is lower than these values ​​and the insured meets the terms and conditions of the policy, the insurer will pay the insured the difference between the actual result achieved and the insured base value. Under the insurance policy, the insurer accepts the risk of paying up to the risk acceptance threshold amount in US dollars annually.

[0069] These risk tolerance values ​​are also related to the frequency of claims, such as in Figure 3 The method is illustrated in the diagram. It begins at step 300, where a confidence level percentage is determined at step 302. At step 304, the difference between 100% and the confidence level percentage constitutes the probability that savings may be less than the risk acceptance value. For example, a 90% confidence level indicates that savings are expected to be less than the indicated acceptance value 10% of the time. Although an additional claims frequency reduction element is applied in this invention, 100 minus the confidence level can be an upper limit on the expected annual claims frequency.

[0070] Another unique feature of this invention is the use of a confidence level approach, which allows insurers to apply different risk acceptance judgments for different policy years. This is likely a major advantage of setting deductibles through confidence levels rather than directly based on absolute dollar values. However, insurers can directly select a risk acceptance value and apply the input annual cumulative risk distribution to determine the corresponding risk acceptance confidence level. Both methods are included within this invention. Applying the input annual cumulative risk distribution to help specify multi-year deductibles is also unique to this invention.

[0071] The ability to flexibly specify annual or overall confidence levels allows insurers to set higher risk acceptance values ​​for years they perceive as high-risk and lower values ​​when the risk is within normal tolerance. This is particularly relevant if the insurer believes the insured's implementation and schedule will or will not meet expected savings targets, or if the project schedule is too aggressive, meaning the insured's savings will be achieved but not in the policy year indicated by the implementation and schedule. This feature gives insurers the flexibility to adjust their risk acceptance analysis to consider factors beyond the insured's project performance, such as available personnel, project management, and several other key factors.

[0072] As an example of how this process is carried out, suppose a potential insured person's cumulative savings project project forecasts $20M in year 1, $30M in year 2, and $35M in year 3, as follows: Figure 4 As illustrated in step 400. After a detailed review of the implementation and schedule, the insurer deemed the progress forecast for year 1 overly optimistic. The insurer believed that the savings projected for year 1 would be achieved, but some of these measures would carry over to year 2. For the remaining measures, the insurer further believed that the savings target would be achieved on the timeline indicated in the implementation and schedule for years 2 and 3.

[0073] In this scenario, at step 402, the insurer can apply a higher confidence level for year 1 than for years 2 and 3. A 95% confidence level can be applied to year 1, and a 90% confidence level to years 2 and 3. The resulting risk acceptance values ​​might be $10M for year 1, $22M for year 2, and $25M for year 3. As appropriate underwriting, for example, to mitigate potential moral hazard, the risk acceptance values ​​can be expected to be lower than the prescribed project forecast.

[0074] After selecting the risk acceptance value, the next underwriting decision is to select a confidence level associated with the loss cost analysis at step 404. For example, if the underwriter selects a 95% confidence level, the corresponding loss cost actually incurred should be less than this value 95% of the time. A unique feature of the present invention is that the underwriter can select the loss cost confidence level on an annual basis, or default to using the same value for all years.

[0075] Another unique feature of the present invention is the ability to apply different savings measurement criteria as claim triggers. Although one embodiment of the present invention includes two types of savings measurement criteria, combinations or other methods can also be applied.

[0076] The underwriter selects the measurement method, and for this example of the present invention, the method is Escrow or NoEscrow. The Escrow method accumulates the excess of savings in a policy year that is above the risk acceptance value (if any). If there is a shortfall in the policy year, the escrow account can be debited first. When the escrow account is zero and the annual savings target is not met, a claim occurs. The NoEscrow method simply compares the actual realized value A and the insured savings value B, and if A < B, a claim for the difference of $B - A is made.

[0077] Although the underwriter selects the measurement method in the system, the input is not necessarily determined by the underwriting function. The claim measurement method can be identified as part of the policy and agreed upon by the insured, the insurer, and other interested parties such as investment companies, banks, or rating agencies (e.g., S&P).

[0078] At this point, Figure 5A and Figure 5B shows a system for calculating loss costs using a stochastic model, where the stochastic model utilizes the input annual cumulative risk distribution, risk acceptance value, claim measurement method, and required loss cost confidence level, as shown at steps 500 and 502. This is a dynamic system where the stochastic model is re-run at step 504 when any of these inputs change. Generating loss costs with this combination of policy-specific attributes and risk data is a unique feature of the present invention.

[0079] When performing a stochastic analysis that may require thousands of different samples to accumulate a sufficient distribution of loss costs, loss costs at the insurer-specified level are automatically placed into the pricing form in step 506. In step 508, these values ​​are summed over the policy term (e.g., in a range of 1 to 7 years) and compared in step 510 with the company-specific rate-on-line requirement. The rate-on-line is defined as loss costs (or premiums) divided by the insurer's total dollar risk exposure. For example, for a total risk exposure of $1 million, a 5% rate-on-line requirement would result in a premium of $50,000. In step 512, the maximum of the sum of loss cost values ​​from the stochastic model and the rate-on-line estimated premium is input as the loss cost component of the multi-year policy premium.

[0080] After the loss costs are determined, in step 514, the insurer adds insurance costs for the engineering and underwriting premiums required to manage the policy over the policy term. These costs include, for example, on-site engineering reviews of work practices, progress of measures implementation, and savings measurement and verification processes. These activities will typically vary depending on the type of industry, facility location, policy term, policy terms, and several other factors. It should be noted that the premium reflects the true cost of policy management as well as the potential costs involved in actual losses. At step 516, these costs are entered separately for each policy year, inflation is calculated using the provided annual inflation rate, and summed to produce the total engineering and underwriting (insurance) components. Most of these costs are well-defined expenses and are generally neither risk-based nor have a significant random component. At this point in premium development, these costs are placed in the year and category (underwriting or engineering). Additional analysis of the factors affecting the loss cost premium components is typically required before the expense items can be used further.

[0081] Along with the quantitative aspects of underwriting and premium development, step 518 also includes subjective factors designed to modify the calculated loss cost premium using the insurer's intuition and experience, if necessary. These factors can increase or decrease the loss cost component within a specified percentage range. To facilitate the use of these subjective factors by the insurer, they are categorized into engineering and underwriting categories. The actual list and range of risk adjustment factors will vary between industries and clients, but they can all include some of the items listed below.

[0082] Credit is interpreted as enhancing risk quality, which then translates into a reduction in loss costs. Debit is configured as a reduction in risk quality, which increases the loss cost component of the policy premium.

[0083] Construction quality insurance: [Debit, Credit] 1) Organizational / Corporate Culture: [+15%, -10%] Risk exposure, hazards, and human behavior are interconnected. A company's safety, environmental, and reliability strategies, as well as its fundamental cultural risk acceptance attitude, are important attributes for inferring how the company and its employees will mitigate risk on a daily basis and respond to unforeseen events.

[0084] 2) New Technology Application: [+10%, -10%] depends on the robustness of the new technology design; operational and short-term financial advantages can be offset by reduced long-term reliability and availability. In multi-year insurance policies where minimum performance and savings levels are desired, underwriters need to consider these factors.

[0085] 3) Management Motivation: [+15%, -10%] Underwriters need to understand how the company's management intends to utilize the financial applications of the overall implementation and schedule. Multi-year plans will require long-term commitment from management, and the financial applications of the plan will provide underwriters with valuable insights into assessing the sustainability of savings.

[0086] 4) Supervisor Incentives: [+15%, -10%] The underwriting risk assessment for facility supervisors can be similar to that required for management. At the employee level, supervisors need to be committed to the success of implementation and schedule planning and its sustainability over multi-year policy terms. One way underwriters can assess supervisor (and management) commitments is to determine how the execution of savings implementation and schedule planning is linked to employee incentive programs.

[0087] 5) Complexity: [+10%, -10%] Complexity refers to the difficulty of implementing the plan. Some issues to consider in this assessment are the technical difficulty of the measures, volumetric interdependence, and schedule interdependence.

[0088] 6) Housekeeping and Record Keeping: [+5%, -5%] The cleanliness, layout, and organization of insured assets are valuable, observable indicators of employee reliability and safety awareness. Numerous studies have shown that strong productivity and reliability are associated with the cleanliness and organization of facilities and assets. This characteristic is easily observable, and the inference of improved reliability can be a factor in the engineering aspects of the policy underwritten. Additionally, the level and accuracy of production and operational record keeping can be another visible indication of employee and management commitment to process compliance and attention to detail, reflecting the engineering quality risks of the insured facility.

[0089] In general, the sum of debits and credits in one embodiment is typically limited to a total of 20% credit (premium reduction) or 25% debit (premium increase).

[0090] Underwriting quality refers to the terms and conditions of an insurance policy negotiated under given implementation and schedule conditions for operation and engineering. These risk correction factors measure risk quality from a written contractual rather than a technical perspective.

[0091] Credit and debit allocations follow the same conventions as the engineering risk adjustment factor. Credit is interpreted as an enhancement of risk quality, which then translates into a reduction in loss costs. Debit is allocated as a decrease in risk quality, which manifests as an increase in the loss cost component of the policy premium.

[0092] Quality coverage: [Debit, Credit] 1) Exclusions: [+10%, -10%] These policy terms refer to situations where the insurance policy will not respond to events that result in savings realization below the insured's minimum value. These events include war, worker strikes, weather events, events covered by other insurance, the insured's failure to comply with policy conditions, and the signatory's performance errors.

[0093] 2) Deductible / Deductible: [+10%, 10%] The deductible or deductible determines the insured's total financial risk exposure. If the insured is willing to take on a higher level of annual savings, then from an underwriting perspective, the risk quality can be increased because the insured accepts a larger annual savings deficit before the insurance policy will respond.

[0094] 3) Savings Measurement and Verification: [+15%, 10%] The type of savings and the process used for measurement and verification are fundamental to insurance underwriting. These factors are necessary to determine the quality of measure implementation in time- and capacity-based savings realization. However, there are different ways to achieve these functions. For example, measurement and verification can be performed by the insured and audited by the insurer, or a third party can perform these tasks for a fee. Without adequate oversight, problems and moral hazard arise in these actions involving the insured. Savings measurement and verification can also provide proactive guidance on measures after the implementation of objectives. Underwriters need to assess the type of measurement used to measure savings, the frequency of measurement, the ability to access trends in this data, and any tendency to obfuscate actual measure performance.

[0095] Overall, the sum of debits and credits is capped at 20% for credit (premium reduction) or 25% for debits (premium increase).

[0096] Depending on the type of insurance (personal, injury, property, etc.) and the nature of the insured's business, the above factors are routinely applied to policy underwriting and premium development. The actual number and type of engineering and underwriting risk correction factors will vary depending on the type and nature of the asset performance considered in the policy.

[0097] Now, in step 520, the "adjusted premium" is calculated. This term is defined as the cumulative policy term loss cost multiplied by the engineering and underwriting risk correction factors. If E = cumulative engineering risk correction factor, U = cumulative underwriting risk correction factor, and L is the loss cost, then the adjusted premium Paj is determined as follows:

[0098] The final stage of premium development is step 522, which adds the premium components associated with the insurance pricing elements. These items typically include engineering and administrative costs, profit, reinsurance costs, taxes, and commissions.

[0099] Several variations and combinations of these factors can be applied to insurance products, rating systems, and methods. Perhaps the most notable variation concerns the determination of how to interpret engineering costs. Some insurance policies of this invention may include all engineering costs in the policy premium, while others may exclude these charges from the policy premium and charge them as consulting fees separate from the insurance policy.

[0100] As an example of how to implement insurance policy pricing according to the invention, the following example illustrates premium development according to the invention for a three-year policy in which engineering costs are incorporated into the premium calculation and used to develop loss costs.

[0101] Subsequently Figure 6A and Figure 6B The above is an embodiment of the subject matter for which protection is sought.

[0102] Step 600 inputs basic customer data into the system. At step 600, the user inputs: insured's name, lending institution, country and region, address of the coverage location, occupancy status, location dimensions measured in product output, and the application of the insured savings policy. This basic data can be integrated with the client database, allowing other key variables required by the system to be automatically identified from this basic data.

[0103] 610. Develop numerical or analytical distributions of annual savings. In this step, compile overall annual probability distributions and arrange them in a dynamically accessible format. This distribution describes the probability of exceeding annual savings with respect to savings values. This distribution can be designed for analytical methods that calculate the probability of exceeding cumulative savings. For each year, there are separate distributions for each location, plant, unit, or other part being analyzed. These distributions consist of savings values ​​and the corresponding probabilities of exceeding these values.

[0104] 620. Input market and company pricing standard data. In this step, representative inflation rates for the policy term and company-specific minimum to maximum premium liability ratios are entered into the system.

[0105] 630 Select the probability of exceeding the insured's base benefit threshold: insured savings levels grouped by year, location, or other criteria. In this step, the amount of risk the insurer is willing to accept is determined by setting an exceedance probability threshold for the insurance liability. This can be done in two ways, depending on the underwriting information: the user can choose an exceedance probability for all years or a different value for each year. These probabilities are matched against the probability distribution compiled in step 610, and the corresponding savings value is identified. For example, suppose the insurer is willing to accept a 90% exceedance probability (as a percentage) for a given location in a given year. This value is matched against the appropriate probability distribution discussed in step 610, and the corresponding savings value is found to be $15M. This means there is a 90% chance that the annual savings for that location in that year will be greater than $15M. If the realized annual savings are less than this $15M value, an insurance claim is triggered.

[0106] 640. Savings levels are recorded in the loss cost component of the pricing development system by year, location, or other grouping. In this step, the resulting savings values ​​calculated or accessed based on the probability distribution compiled in step 610 are input into the loss cost component of the pricing system. These values ​​are the resulting insured levels corresponding to the probabilistic values ​​input into the system in step 630.

[0107] 650 The development logic tests the annual savings results selected from the annual probability distribution compiled in step 610 to measure the frequency and severity of loss and excess events. In this step, for each year or other grouping, the logic is developed to compare the sampled distribution savings value from the probability distribution compiled in step 610 with the recorded value—the savings base savings value. If the sampled savings value is greater than the insured base, an excess occurs for that year. If the value is less than the insured level as given in step 640, a loss event occurs for that year.

[0108] 660. Develop escrow account and claim trigger logic. In this step, comparison logic is developed to accumulate all or part of the savings results exceeding the insured savings value. For example, in a given year, if the calculated savings are $50 and the insured amount is $40, then $10 will be credited to the escrow account. On the other hand, if the calculated savings are $35, then the escrow account will first be debited $5 to reach the insured level. If the escrow account does not contain sufficient funds, then an insurance claim will be triggered for the difference between the insured level and the actual savings result, and the sum of any funds that can be withdrawn from the escrow account.

[0109] 670. Develop logic for claims count, claims amount, and claims risk distribution. In this step, logic is developed to accumulate the number and financial amount of claims for both the escrow and non-escrow accounting methods. The financial amount of the claims is referred to as the loss cost. This information is used to calculate a numerical distribution of the cumulative loss probability as a function of the loss amount. These distributions are called claims risk distributions.

[0110] 680. Run a stochastic model to develop a claims risk distribution. In this step, commercial software or specialized programming used to accumulate sufficient loss data in steps 640, 650, and 660 is applied to a numerical process to develop a numerical distribution of the loss probability as a function of the loss amount for both escrow and non-escrow accounting methods.

[0111] 690. Determine the premium liability ratio. In this step, the prescribed premium liability ratio standard selected in step 620 is applied to each annual excess threshold selected in step 640. The premium liability ratio calculation can be performed by multiplying the excess threshold, the minimum insured savings, or the base amount by the decimal value of the premium liability ratio. For example, if the insured base amount is $10,000 and the premium liability ratio is 10%, then the premium requirement is $10,000. 0.10 or $1,000. These calculations are applied to the insured annual savings base calculated as in step 640. These results are summed and placed in an entry in the system's loss cost summary section.

[0112] 700 determines the confidence level and value of the loss cost. In this step, the insurer enters a percentage of the probability that the loss cost obtained from the system will be substantially less than the identified value. These percentages are then applied to the annual cumulative distribution of claims risk to determine the corresponding value of the annual loss cost to the total multi-year premium contribution. The resulting value is placed in the annual loss cost field. This process can be performed for claims risk distributions with and without escrow accounting.

[0113] 710 Calculate the loss cost premium component. In this step, the premium liability ratio premiums for each year are summed to calculate the total policy premium via the premium liability ratio method. Next, for both managed and non-managed pricing methods, the annual loss cost determined in step 680 is summed over the policy year. The system user then selects which managed pricing method might be required for the customer. The system then calculates the policy loss cost premium component as the maximum of the following: (1) the prescribed premium liability ratio and (2) the total loss cost via the selected managed method.

[0114] 720. Determine Underwriting Costs. In this step, enter the company costs required to perform underwriting analysis and risk monitoring. These costs incur during the review of monthly, quarterly, and annual savings reports, as well as during regular meetings with client management at client locations. The insurer's responsibility is to ensure that clients meet their contractual obligations and savings goals. If the client complies, the insurance coverage continues as defined in the policy. If the client does not comply, the insurer is responsible for notifying the company's engineering department and client management in writing. If the compliance recommendations and other policy conditions are not met within the time constraints specified in the policy, the insurer has the responsibility and authority to terminate the insurance coverage. Enter the costs incurred in performing these activities into the system for each policy year.

[0115] 730. Determine Engineering Costs. In this step, technical engineering, project management, and savings monitoring activities are reviewed to prepare their associated policy expense costs. Engineering activities provide technical data to support underwriting activities, provide periodic loss prevention and savings reports, provide technical guidance for the implementation of measures, and serve as field communication between the insurer and the insured. The costs incurred in performing these activities are entered into the system for each policy year.

[0116] 740. Determine the underwriting credits and debits related to the project. In this step, pricing adjustment factors that increase or decrease the premium are determined based on project-related attributes of the insured value, as selected in step 620, to implement savings. These factors include, but are not limited to, the insured's organization and corporate culture, the application of new technologies, management incentives to achieve savings targets, executive incentives, and the complexity of the plant. The range of adjustment factors will vary depending on the application, but is generally 10% for each factor, with the cumulative factor not less than -20% and not greater than +25%. The project risk adjustment factors are entered into the system for each policy year, and the cumulative adjustment factor is calculated.

[0117] 750. Determine underwriting-related credits and debits. In this step, pricing adjustment factors for increasing or decreasing premiums are determined based on underwriting-related attributes of the savings insured value selected as in step 620. These risk modification factors include, but are not limited to, appropriate policy exclusions, self-insured retention, deductibles, limits, and the quality of savings measurement and verification procedures. The range of adjustment factors will vary depending on the application, but is generally 10% for each factor, with the cumulative factor not less than -20% and not greater than +25%. For each policy year, the underwriting premium adjustment factors are entered into the system and the cumulative adjustment factor is calculated.

[0118] 760. Calculate the adjusted policy premium. In this step, the numerical results determined in previous steps are combined to generate the basic policy premium. Several versions and combinations of the steps described exist in this claims process. An example of such an embodiment is: Adjusted policy premium = Step 710 (loss cost policy premium) [1 + Step 740 (Engineering Correction Factor) + Step 750 (Underwriting Correction Factor)]. This result is stored in the premium: the system's insurance-adjusted premium portion.

[0119] 770 Calculates Policy Underwriting and Engineering Costs. In this step, the inflation rate input into the system in step 620 is used to calculate and sum the inflation over the policy term for the underwriting costs determined in step 720 and the engineering costs determined in step 730, to calculate the total policy-level underwriting and engineering costs. These results are stored in the premium: the system's insurance and engineering costs section.

[0120] 780 Calculate Engineering and Underwriting Profits. In this step, based on the expenses calculated in steps 760 and 770, company-specific criteria are applied to calculate insurance and engineering profits. These results are stored in the premium: Profit - Insurance and Engineering Portion.

[0121] 790. Calculate the allocated reinsurance costs. In this step, reinsurance costs, whether authorized or contractual, are entered into the reinsurance section of the system.

[0122] 800 Calculate Tax. In this step, the tax is calculated in the relevant section of the system's premium section and entered into the system's Premium - Insurance and Premiums & Projects: Tax section.

[0123] 810 Calculate Commission. In this step, the insurance-related commission is calculated in the relevant section of the premium part of the system and entered into the premium-insurance:commission section of the system.

[0124] 820 Calculate the total policy engineering cost. In this step, all premium costs entered into the premium-related section are summed to calculate the total policy engineering cost.

[0125] 830. Calculate the total policy premium. In this step, all premium costs entered into the premium-related section are summed to calculate the total policy premium. Additionally, based on policy requirements and related accounting processes, the total policy premium may also include total engineering costs. In this scenario, all risk transfer and direct engineering costs required to support the policy are included in the total policy premium, which is then divided by the policy term to determine the annual premium. Depending on the insurance terms, the insured may pay the full premium at the beginning of the policy term or pay it at an annual rate.

[0126] Figures 7A and 7B show a spreadsheet containing the steps disclosed in Figure 6.

[0127] The methods disclosed above can be used to determine the securitization rating VB, and the FS rating can be based on, for example, Figure 1 Benchmark data for specific assets of the power plant.

[0128] For example, Figure 8 This method is illustrated. In step 900, engineering data such as increasing output 940 or other measures 950 are collected for each desired asset, both of which are... Figure 8 Figure A is drawn. In steps 910 and 920, the engineering data is compared with the baseline data to create an action plan and financial objectives. Figure 8 B provides an example action plan, while Figure 8C The financial objectives are shown.

[0129] For example, Figure 8 B includes various actions to be initiated by employees 960, such as detailed process assessments 970 and operator training 980. Figure 8CThis demonstrates how to use risk curves to select annual insurance levels and provides information for choosing financial targets for overall improvement plans. For example, following general insurance company guidelines, a company selects a 90% probability of exceeding the target and moves the curve horizontally until it crosses the annual #1 risk curve at 990. Here, the 90% risk acceptance value for insurance purposes is $20M at 995. This typically means there is a 90% chance the actual outcome will be greater than $20M. Companies can also use these risk curves to set their internal financial targets at more aggressive risk acceptance values. For example, company management could target a 60% or 70% level for the business unit, which would be a target of approximately $22-$25M for annual #1. The same process is applied to annual #2. The percentage of insurance risk acceptance intersects the risk acceptance curve at 1000, which corresponds to an annual savings of $26.7MNCM at 1005. This amount will be selected as the insured base. For the company's internal financial objectives, using the same 60-70% criterion as for Year #1, the company's financial objectives for Year #2 would be between $28-$29M. The securitization rating can be determined based on the action plan and financial objectives outlined in step 930. Figure 8 ).

[0130] The implementation of this invention can also improve the insured's bond rating. Figure 9 Cost savings as a result of reduced credit risk are illustrated. For example, suppose that improving operations using this method could increase the insured’s savings by $700 million over a ten (10)-year period. In developing the credit risk of this company for the purpose of developing bond issuance, the lending and / or credit institutions involved can extend credit to the company’s enhanced operating and financial condition by applying marginal profits to the reduced risky principal. This may be a subjective decision. However, applying this method to this situation provides a transfer of risk of principal from the customer to the insurer, thereby securitizing at least a portion of the principal. Suppose that the customer has a credit rating of BB- by S&P. A policy for this customer using the present invention can have the effect of reducing the risky principal, thereby also reducing the credit risk of the transaction. By transferring the risk of this principal to the insurer, the initial transaction (now actually a smaller amount of principal) can have the equivalent credit risk of the full amount of bonds at a higher quality credit rating.

[0131] For example, if a customer has a $600M policy on a ten (10)-year $800M bond according to the present invention, the reduced effective risk-bearing principal ($200) makes the transaction appear slightly above investment grade BBB- from a credit risk perspective. This means that mathematically, the credit risk of a $200BB- bond may be roughly equivalent to the credit risk of an $800M bond rated BBB-. This situation is in Figure 9 This is shown at position 1010. This example assumes the insurance company's credit rating is at least BBB-.

[0132] refer to Figure 10 The diagram illustrates a computer system used to implement part or all of the present method and system. This computer system includes a microprocessor-based system 1100, which includes a system memory 1110 for performing numerical calculations. A video and storage controller 1120 enables operation of a display 1130, a floppy disk unit 1140, internal / external disk drives 1150, an internal CD / DVD player 160, a magnetic tape unit 1170, and other types of electronic storage media 1180. These storage media 1180 are used to input risk distributions into the system, store numerical risk results, store calculation reports, and store pricing forms generated by the system. Risk distributions can be entered in spreadsheet format, for example, using Microsoft Excel. Risk calculations are generally performed either using custom programs designed for company-specific system implementations or using commercially available Excel-compatible software employing stochastic methods such as Monte Carlo simulations. The system can also interface with proprietary external storage medium 1210 to link with other insurance databases to automatically input specified fields into pricing forms, such as customer name, location address, location size, location occupancy, and risk quality attributes applied in the "Credit and Debit" section. Output devices include telecommunications equipment 1190 (e.g., a modem) to send pricing forms and reports generated by other systems to management or other underwriters via an intranet or the Internet, printer 1200, and electronic storage media similar to those mentioned as input devices 1180 that can be used to store pricing results in proprietary insurance databases or other files and formats.

[0133] Figure 11 This is a block diagram illustrating the terms and conditions of an insurance policy 1300 according to the present invention. The insurance policy 1300 includes insured information 1310, such as the insured's name and the geographical or physical location of one or more insured persons covered by the policy. The policy 1300 also includes the policy term 1320. The policy term 1320 can be one year, several years, or some other defined period. Policy terms 1330 include, for example, savings criteria. Savings criteria are typically prepared by a third-party company (e.g., HSBSolomon Associates). The insurer uses benchmark information when creating the savings standard based on the specific circumstances of the insured's business. The savings standard includes a process that establishes a certain amount of savings for the insured if the insured achieves it. A third-party company may act as a facilitator in the execution of the process, enabling the insured to improve its business performance (resulting in savings). If the process is achieved and the insured fails to achieve the required savings during the policy term (with certain exceptions stipulated in the policy), the insurer will pay the insured the difference (referred to as a deficit). Certain exceptions include, but are not limited to, hostile or war activities, riots, rebellions, civil war, nuclear reactions or radiation, the insured's default or insolvency, sabotage, riots, failure of the contracting party to implement the process, modifications or alterations to the process without the insurer's approval, or other terms listed in the policy. Other policy terms 1330 include the insurer's and the insured's responsibilities, such as executing the process in a timely manner, cooperating with third-party companies, preparing status reports, allowing other parties to audit the insured's accounts, performance records and data logs, and other matters. Furthermore, if savings are determined annually and the policy is a multi-year policy, and a deficit arises as a result of the insured's process transfer, this deficit can be maintained in an escrow account (referred to herein as a surplus account). The escrow account can be increased or decreased on a multi-year policy. Any surplus at the end of the policy term can be paid to the insured. Other provisions may include cancellation clauses, representations and warranties, and distributive obligations and effects arising from the sale or assignment of the covered position.

[0134] Policy 1300 also includes policy restrictions (i.e., liability restrictions) 1340 on the time period 1320, premiums paid by the insured 1350, and endorsements 1360. These endorsements may include a market price index and operating baseline unique to the insured's industry, implementation plans and schedules, agreed measurement plans, savings calculation processes and baseline values, exclusions, definitions, and conditions for liabilities and additional liabilities.

[0135] Figure 1 2A- Figure 12D The agreed-upon measurement plan is shown. The agreed-upon measurement plan provides a top-level task list with an implementation plan and timeline. For illustrative purposes, the plan is divided into four parts: Measures 1400, Benefits and Measurement 1402, Implementation 1404, and Savings 1406.

[0136] For example, in Figure 12A , 12B In 12C and 12D, the agreed measurement schemes involve chemical industry policies. According to the implementation plan and timetable, in Figure 12AVarious top-level measures 1400 are listed. For example, some measures from chemical industry policies may include moving the analyzer to a pallet for a specific plant 1410 and modifying regulatory controls in the final product column 1408 (Measure #1). Columns titled "Region" indicate the geographical or functional location of the measure, such as Plant 1. Another measure for an insured site may include reducing pressure in the stripper to save energy 1412 (Measure 2). Furthermore, another measure for another plant may include reducing the time for drying the catalyst after regeneration 1414 (Measure #3). Documentation or other deliverables 1418 are provided to document the results of the measure transfer. An example of such documentation 1420 may include a report describing the savings achieved as a result of the transfer of Measure #3.

[0137] exist Figure 12B The document provides an example of an agreed-upon measurement plan's benefits and measurement section. The implementation of the measures will produce certain benefits described in this section. For example, for measure #1, one benefit 1422 could be an increase in productivity. The plan includes various measurements and methodologies for the results of the measures. These values ​​and methodologies may relate to engineering units (e.g., tons / hour) and time periods (e.g., measurements are taken daily and then averaged over a certain time period). Furthermore, the plan includes the start date of the measures (target date and actual date) and the completion date of the measures. The agreed-upon measurement plan typically requires negotiation and signature (i.e., initials of the insured and the insurer) 1424 (i.e., the negotiation section) for each measure and its results.

[0138] The plan also includes, for example, in Figure 12C The target date and actual date are shown in the figure. Each measure can have a target date of completion 1426, an actual date of completion 1428, and the number of days for completion 1430.

[0139] The plan also includes information on economic savings 1432 as a result of the measures. This information may include target savings 1434 and actual savings 1436 achieved as a result of the measures.

[0140] As discussed above, the various embodiments disclosed herein include credit enhancement systems and methods, and generally relate to ways in which insurance against a project savings base (i.e., the minimum level of return on investment for a given improved project) can be achieved through better credit ratings for bonds and loans associated with the improved project. See, for example, Figure 8 A-8D and Figure 9 A better understanding of these embodiments can be obtained through the following detailed description of various preferred embodiments and... Figure 13-11 get.

[0141] Figure 13 This is a flowchart illustrating an embodiment of an insurance product, rating system, and method for insuring project savings to enhance the credit rating of the insured. It generally relates to how insuring project savings principal can be achieved through better credit ratings for bonds or loans associated with the improved project. This embodiment includes a credit enhancement system and method based on an insurance product designed for a relatively small number of insured individuals, each with a relatively large risk exposure over an annual or multi-year period. This situation cannot rely on the law of large numbers in statistics, but rather applies as much knowledge and real-world performance data as possible to the development of risk analysis. Therefore, each case is handled individually.

[0142] refer to Figure 13 In step 1500, the user calculates the insured savings base for a given project. In this embodiment, various embodiments of the system and method of U.S. Patent No. 8,195,484 are used to calculate the insured savings base (i.e., the minimum return on the investment). At step 1510, the cumulative risk distribution of the project is recalculated to reflect the user's insured savings base decision. In step 1520, credit risk is calculated using the project savings distribution modified by the insured savings base, and in step 1530, the impact on the modified project savings flow is measured based on the reduced credit risk resulting from the insured savings base project savings flow.

[0143] To explain in more detail Figure 13 The steps of the embodiment shown in the figure. Figure 14 A flowchart is shown for determining the insured savings base for a project. In 1600, the user develops a project improvement plan, which includes details about each measure, which, when combined, are designed to generate the planned savings level. Associated with this list and description of measures is the measure implementation schedule 1610, which describes the expected start date and implementation duration. To quantify the uncertainty in the execution of each measure, detailed baseline data 1620 is applied to provide a range of possible outcomes for each project measure in terms of both performance and schedule. In 1630, the measures are compiled into a document that calculates the cumulative project savings distribution. In 1640, the insurer enters the expected confidence level for the insured savings base. As used herein, the “confidence level” is the probability that annual savings will exceed the insured savings base value. A Monte Carlo analysis is then performed in 1650, which uses the confidence level entered in step 1640 and the cumulative measure risks listed in 1630 to generate a cumulative project savings risk distribution. In 1660, the actual value of the insured principal is determined using the calculated cumulative project savings risk distribution along with the confidence level specified by the user.

[0144] Figure 15 illustrates an example of how to determine the insured savings base. In this example, the user specifies a 90% confidence level of 1700. This means there is a 90% chance that the actual project savings will exceed this value. From the expected confidence level (e.g., 90%) on the vertical axis 1700, the user moves vertically across the graph until the line intersects the distribution curve 1710. The risk acceptance value is the corresponding value on the horizontal axis 1720. Performing this functional analysis is called “risk acceptance.” In this example, the insured base of $96,000 per year is displayed with the input 90% confidence level and the corresponding value read from the x-axis 1720. The cumulative probability curve shown in this format indicates a 90% chance that the project savings will exceed $96,000 per year. Or in other words, this format indicates a 10% chance that the annual savings will be lower than this value. In this case, this is the risk accepted by the insurer at a 90% confidence level.

[0145] Figure 16 This example illustrates how insurance modifies the calculated cumulative project savings risk distribution. Note that from the insured's perspective, project savings cannot fall below $96,000. If actual project savings do fall below the minimum, the insurer will pay the difference. This is the purpose of the insurance's risk transfer process.

[0146] exist Figure 17 The diagram illustrates the integration of the insured principal accumulation project savings risk distribution and project credit risk calculation. In step 1800, the credit risk calculation requires the user to input loan interest rates according to rating categories. In one embodiment, these values ​​come from commercial rating agencies like Standard & Poor's or other sources, and include the insured principal accumulation project savings risk distribution as data input 1810. The credit risk model also requires data on the annual or periodic default probabilities for the time intervals considered in the analysis. These values ​​are entered in step 1820. In step 1830, specific loan characteristics are entered: loan amount, term, and payment schedule.

[0147] Now, after compiling this information, in 1840, the net payment amount is calculated for each rating category. The term "net" is used herein to refer to the standard loan payment adjusted to include various project savings amounts. Due to the inherently stochastic nature of project savings, various statistical representations of savings can be applied. In a preferred embodiment, a percentage statistic of project savings is applied. Other statistics or values, such as the average savings of user-input values, can be used. The net term loan payment, utilizing the distribution of insured principal combined with different levels of project savings, is given by a percentage cumulative savings statistic deducted from the basic loan payment amount in 1810.

[0148] In 1850, the credit risk model calculates the default amount for each rating category and for each percentage-modified loan payment. In one embodiment, a Monte Carlo analysis is applied in 1860 to calculate the average default loss for each rating category and each percentage-modified loan payment. In 1870, these values ​​are stored in a table for further analysis in subsequent steps of the invention.

[0149] exist Figure 18 The image shows an example of a graphical representation of information presented in tabular form. In this example, for a customer with an S&P credit rating from B- to A, the loan amount payable over 10 years is $2,500,000. The image shows the savings values ​​for zero percentage (basic loan payments) and 50, 80, and 100% of the project. These values ​​are represented by separate lines.

[0150] This framework can be used to graphically demonstrate how the net project savings distribution can be utilized. Figure 18 To modify the credit risk valuation. For example, consider Company B-, where 100 is applied. th The percentage savings value is used to model net periodic payments. This exercise demonstrates the potential maximum credit risk reduction in this transaction due to project savings.

[0151] At 1900, it shows that credit risk decreased from approximately $1,000,000 to approximately $300,000. The decrease at 1910 moves horizontally across the graph until it matches the underlying loan (0). t The h-percentage credit risk curves intersect. In 1920, move down to the rating axis and note credit rating upgrades.

[0152] This exercise demonstrates that, in the example shown, a B-rated loan with 100% project savings applied to loan payments carries roughly the same credit risk as a standard loan from a BB-rated company. Therefore, from a credit risk perspective, a B-rated company with the assumption that project savings are applied to loan payments is equivalent to a standard BB-rated loan. This process demonstrates how to determine the credit risk equivalent of using project savings to offset some of the standard loan payments.

[0153] Figure 19This illustrates how this process can be applied to an example of the default amounts listed in the table generated in 1870. To analyze possible credit enhancements for Company B- in the example above (2000), note the underlying credit for the next higher level, B. Then locate or circle this default risk amount in the B- column. The associated value in the Project Savings Percent column indicates how much of the project savings flow needs to be attributed to loan payments to obtain a credit risk equivalent to a standard B- loan. The amount used for the B rating equivalent in 2000 shows only 0 and 10. t The percentage of savings in projects between 30 in 2010, with a B+ rating. th A project savings percentage level of -40 is possible; in 2020, a BB- ​​rating was achieved at a project savings percentage level of 70th-80th percentile, and in 2030, a BB rating at 90th percentile. th -100 t The project savings percentage is realized at level h. It should then be noted that for a credit risk rating of BB+, even assuming a project savings level of 100, the underlying credit risk value is not included. This fact limits the limited credit enhancement benefits that a company with an initial rating of B- can realize from associated projects.

[0154] This financial information allows analysts to assess the confidence level they can expect from the project's savings stream. In the application of this percentage result, the lower the value applied to achieve the expected credit enhancement, the higher the confidence level that the project will deliver the expected credit enhancement result.

[0155] To summarize quantitative information about the quantity of credit enhancement as a function of the percentage of project savings distribution, in Figure 19 The process discussed in the middle can be applied to... Figure 17 The credit risk table compiled in 1870. This result is the output of various embodiments disclosed herein and represents important risk-based or probability-based information about credit enhancement that rating analysts, project financers, and underwriters can apply in their work.

[0156] exist Figure 20A Examples of these results are shown below. As illustrated, the probability of exceeding the required level of project savings for each possible rating category is given. The arrows indicate a significant shift from the non-investment grade level BB+ to the investment grade level BBB-. This is a crucial shift and can have benefits beyond the direct credit risk calculations shown here.

[0157] The arrow indicates that, for the BB+ rated company used in this example, considering a $2.5M loan to be repaid over ten years, there is a 92% chance that the actual project savings will be greater than the savings required to achieve the equivalent credit risk transaction for a BBB- rated company. Figure 20B This illustrates that if the transaction actually achieves the BBB- rating designation, the corresponding credit risk would be reduced, which would be a saving. Figure 20C The document provides the savings amount required to achieve the corresponding credit rating upgrade. For a conversion from BB+ to BBB-, the annual amount is shown as $64,233. However, according to... Figure 20A There is a 92% chance that the realized project savings will exceed this amount, which corresponds to a reduction in overall loan credit risk. Figure 20B The figure is $55,956.

[0158] This information from the illustrative embodiments provides all stakeholders with the risk-based information needed to make project management, investment, and rating decisions. Furthermore, this information can help financial analysts analyze project savings risks, as it is particularly relevant to debt repayment. This functionality represents a novel aspect of the embodiments of this disclosure.

[0159] Some embodiments can be used to demonstrate the value created by insuring a minimum savings base. In one embodiment, the insured base is chosen to be applied to project savings expected over its six-year duration rather than annually, as illustrated in the previous example. The project activity is projected to generate savings for the company between $450 million and $750 million over six years. To complete the required activity to realize these savings, the company needs to borrow $75 million, which will be repaid using fixed annual payments over the six-year project duration.

[0160] Table 1 shows typical repayment amounts for this loan amount and duration.

[0161] Table 1: Example Interest Rates and Annual Loan Repayments for S&P Credit Rating Categories

[0162]

[0163] In order to use project savings for loan repayment, Figure 21 The figure presents the cumulative savings risk distribution across the overall project level. This figure is the result of a risk model developed based on the measures and benefit descriptions of the exemplary project, operating baselines and market price indices, savings implementation schedule, agreed metrics, and savings measurement and loss adjustment plans. Applying a 90% confidence level to 2100, the corresponding risk acceptance value appears to be approximately $570M in 2110. Figure 21There is a 90% chance that the actual realized project savings will exceed this amount. The insurer has chosen this amount, $570M, as the insured's base benefit.

[0164] The key problem addressed by this embodiment of the invention is how the insured principal affects the credit value of a loan. The process of measuring this effect involves performing actions such as... Figure 17 and 19 The system and method steps shown in the figure, first with and without the insured principal, then with Figure 20A and Figure 20B The results of the format comparison.

[0165] Figure 22A and Figure 22B The table shows the possible credit risk equivalents for exemplary projects and the corresponding probabilities of the required project savings, categorized by credit rating. Only one row is relevant for any rated company. However, the entire table shown in Figure 22 presents to financial underwriters and investors a broad range of credit enhancement probabilities based on the quantitative results of the disclosed embodiments.

[0166] Two examples are discussed to demonstrate how the illustrated embodiments can provide risk-based information for decision-making. Figure 22A In Case 2200, the example of no insurance base shows that a B- rated company can achieve a B or B+ rating by applying less than 1% of the expected savings. In other words, there is a 100% chance that the actual savings realized will exceed the amount required to achieve the credit risk equivalent stipulated in this case. However, this is the upper limit that can be achieved in this scenario. There is only a 4% chance that the level of projected savings required for this credit upgrade will be achieved. Given the very low probability of success, it is almost impossible for analysts to agree to a credit rating upgrade.

[0167] exist Figure 22B In the 2200, the application of the insured reserve requirement demonstrates a 100% probability of achieving the required level of project savings for a B- to BB- upgrade. This increased probability of achieving the required level of project savings for a given credit rating upgrade provides financial analysts with crucial, risk-based information for credit risk decision-making.

[0168] Reference Figure 22A and Figure 22BSection 2210 describes an important application of the disclosed embodiments. Credit rating enhancement analysis is always important, but it has strategic value when credit enhancement can lead to non-investment grade transactions, i.e., from an investment grade rating below BBB- to a BBB- rating or higher. In 2210, a non-investment grade transaction of BB- has only a 4% probability of realizing the required project savings. However, the probability of realizing the required project savings with an insured principal is 100%, thus making analysts more likely to make a BB- ​​to BBB rating upgrade based on the insured principal.

[0169] Figure 22A and Figure 22B This refers to the details of the risk underlying credit enhancement, but the associated reduction in credit risk is... Figure 23A and 23B As shown in the figure. In 2300, the credit risk reduction in the conversion from B- to BB- is $152,413,179, while there is an insured base in that. Figure 23B The average credit risk savings shown is $153,287,348. These values ​​are statistically consistent, indicating that credit risk savings are unrelated to insurance. Figure 23A and Figure 23B Similar exercises in 2310 show the same results. This invention quantifies the following result: while insurance can aid in analysis and decision-making associated with credit enhancement, actual credit savings are independent of the insured principal if a conversion is made.

[0170] Figure 24An embodiment 2410 of an insurance product, rating, and credit enhancement system for insured project savings, including an input device 2412, a processor 2414, an output device 2416, and a storage subsystem 2418, is generally illustrated. In one embodiment, the input device 2412 is a keyboard used by a user to input input files. In other embodiments, the input device includes a medium on which digital input files are stored, such as a portable hard disk drive. In still other embodiments, the input device 2412 includes a telecommunications device, such as a computer modem, which facilitates the reception of input files from wired or wireless networks. As further discussed herein, the input files include an annual cumulative risk distribution, which quantifies the net performance risk of all measures used to achieve net annual savings for each year of the policy term. For example, input files for improving projects may include information on workforce reallocation, process redesign, installation of advanced process controls, and energy efficiency capital projects. The storage subsystem 2418 can be used to store input files and digital information that facilitates the various functions of the embodiments disclosed herein. The processor 2414, which interacts with the storage system 2418, accesses and executes instructions that include the aspects and functions described in the embodiments disclosed herein. The output device 2416 can be any computer display device, such as a computer monitor or printer.

[0171] The above disclosure and description of the present invention are illustrative and explanatory, and various changes may be made to the details of the illustrated methods without departing from the spirit of the invention.

Claims

1. A computer-implemented method for evaluating improvement plans for factory facilities, comprising: Select at least one factory-related operational measure, wherein the at least one factory-related operational measure includes at least one production efficiency improvement; Wherein, the at least one factory-related operational measure includes at least one first target value produced by the engineering unit; Measure (900) at least one first actual value, said at least one first actual value including engineering unit production measurements recorded within a predetermined time period; Compare (910) the at least one first target value with the at least one first actual value; Based on the at least one first actual value, generate an action plan (920) to achieve the at least one first target value no later than the target date. The action plan includes changes to process conditions, which include one of the following: i) Temperature, ii) Pressure, iii) Material consumption, and iv) Material recycling; Based on a Monte Carlo analysis of the range of possible action plan outcomes based on the at least one first actual value, determine (1650) the probability of achieving the at least one first target value; and When the probability of achieving at least one first target value is greater than the probability of success, at least one of the operational measures (970, 980) shall be executed in accordance with the action plan.

2. The method according to claim 1, wherein, The engineering unit produces performance including at least one process, wherein the at least one process includes at least one chemical reaction.

3. The method according to claim 2, wherein, At least one of the following characteristics is present in the at least one chemical reaction: v) Time, vi) rate, and vii) Product output.

4. The method according to claim 1, wherein, The factory in question is a power plant (100).

5. The method according to claim 1, wherein, The factory in question is a chemical plant.

6. The method according to claim 1, wherein, The factory in question is an oil refinery.

7. The method according to claim 1, wherein, The factory in question is a manufacturing plant.

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