TECHNICAL FIELD
[0001] The present disclosure relates to modeling of losses from natural calamities. More particularly, the present disclosure relates to systems and methods that enable computation and correlation of damage ratio to risk scores, and estimation composite loss from hazards such as floods, earthquakes, and cyclones.

BACKGROUND
[0002] The background description includes information that may be useful in understanding the present invention. It is not an admission that any of the information provided herein is prior art or relevant to the presently claimed invention, or that any publication specifically or implicitly referenced is prior art.
[0003] Insurance industry has been suffering immense losses due to unprecedented Natural (Nat) Catastrophe (Cat) events. As per industry, it has been seeing sustained underwriting losses over the last 5-6 years. This is majorly because of the insurance companies/ entities following a pricing regime that is not risk based, and because they are not utilizing Nat Cat models to their full potential. Traditionally, in India Nat Cat models have been used as a means to validate reinsurance cover. However, dependency on models should not be the only for postmortem of what has already been underwritten but rather they should also be used during underwriting to get a better understanding of Nat Cat risk associated to the policy under consideration and use that information to decide premium pricing. In addition, a holistic view of total exposure to Natural Catastrophes is also important, which currently is not being offered. This can only be accomplished by efficiently modeling losses from natural calamities.
[0004] Out of the three perils of earthquake, flood and cyclone, floods have the highest frequency in India and tend to eat up the underwriting profits of insurance companies. Since India is a large country with so many rivers and fast unplanned development that induces flash floods in all major urban areas so the task for developing a country wide flood model is herculean one. There is therefore a need in the art for a system and method that enables modeling of flood losses and also enables a more realistic estimation of the composite loss from earthquake, flood and cyclone.
OBJECTS OF THE PRESENT DISCLOSURE
[0005] An object of the present disclosure is to provide a system and method for correlating damage ratio to risk score for each hazard.
[0006] Another object of the present disclosure is to provide a system and method for correlating damage ratio to risk score for each hazard for determining composite loss from one or more hazards.
[0007] A still another object of the present disclosure is to provide a system and method for correlating damage ratio to risk score that enables modeling of flood losses.
[0008] A yet another object of the present disclosure is to provide a system and method for correlating damage ratio to risk score for each hazard that aids in determination of realistic estimation of the composite loss from earthquake, flood and cyclone.

SUMMARY
[0009] .Embodiments of the present disclosure provides systems and methods that enable computation and correlation of damage ratio to risk scores, and enables estimating composite loss from flood, earthquake, and cyclone perils.
[00010] An aspect of the present disclosure pertains to a method for correlating damage ratio and risk score of one or more hazards, wherein for each hazard the method comprises: receiving, by one or more processors, a dataset comprising any or a combination of a risk score and a corresponding damage ratio pertaining to each location of one or more locations where data pertaining to the hazard is available; estimating, by the one or more processors, a damage ratio for each of the plurality of probabilistic return periods of the hazard for the one or more locations based on obtaining of pre-determined probabilistic estimates for coverage and occupancy; extracting, by the one or more processors, one or more unique combinations of the plurality of probabilistic return periods, the coverage and the occupancy for each of the one or more of locations; estimating, by the one or more processors, a mean damage ratio and a standard deviation of one or more damage ratios for each of the one or more unique combinations; estimating, by the one or more processors, a minimum damage ratio and a maximum damage ratio based on the estimation of the mean damage ratio and the standard deviation for each of the one or more unique combinations; plotting, by the one or more processors, a plurality of curves between a risk score and any or a combination of the minimum damage ratio, the mean damage ratio and the maximum damage ratio for each of the one or more of locations; identifying, by the one or more processor, an equation for each of the plurality of curves; and estimating, by the one or more processors, the minimum damage ratio, the mean damage ratio, and the maximum damage ratio for the one or more locations where data pertaining to the hazard is not available based on identification of the equation for each of the plurality of curves.
[00011] In an embodiment, the dataset further comprises the risk score for each of the one or more locations where data pertaining to the hazard is not available.
[00012] In an embodiment, the one or more hazards include a flood, an earthquake, and a cyclone.
[00013] In an embodiment, flood based risk score is generated based on any or a combination of rainfall distribution, land use, land cover, topology, elevation, soil characteristics pertaining to each location.
[00014] In an embodiment, the method further comprises a step of generating a loss table based on the damage ratio pertaining to the one or more of locations for the hazard.
[00015] In an embodiment, the method further comprises a step of modeling of a composite loss from the one or more hazards so as to combine losses from the one or more hazards.
[00016] In an embodiment, the modeling is performed based on a randomness technique comprising the steps of: creating a plurality of simulations, wherein each simulation represents an event with randomly generated return period for each of the one or more hazards; estimating loss from each of the one or more hazards based on the loss table and the random return period for each of the one or more hazards; estimating, a combined loss from the one or more hazards for each simulation; and generating a combined loss table based on estimation of the combined loss of the one or more hazards for each simulation.
[00017] Another aspect of the present disclosure pertains to a system for correlating damage ratio to risk score for each hazard of the one or more hazards, said system comprising: a non-transitory storage device having embodied therein one or more routines operable to correlate damage ratio to risk score; and one or more processors coupled to the non-transitory storage device and operable to execute the one or more routines, wherein the one or more routines include: a risk score receive module, which when executed by the one or more processors, receives a dataset comprising any or a combination of a risk score and a corresponding damage ratio pertaining to each location of one or more locations where data pertaining to the hazard is available; a damage ratio estimation module, which when executed by the one or more processors, estimates a damage ratio for each of the plurality of probabilistic return periods of the hazard for the one or more locations based on obtaining of pre-determined probabilistic estimates for coverage and occupancy; a unique combination extraction module, which when executed by the one or more processors, extracts one or more unique combinations of the plurality of probabilistic return periods, the coverage and the occupancy for each of the one or more of locations; a statistical computation module, which when executed by the one or more processors, determines a mean damage ratio and a standard deviation of one or more damage ratios for each of the one or more unique combinations and estimates a minimum damage ratio and a maximum damage ratio based on the estimation of the mean damage ratio and the standard deviation for each of the one or more unique combinations; and a curve generation module, which when executed by the one or more processors, plots a plurality of curves between a risk score and any or a combination of the minimum damage ratio, the mean damage ratio and the maximum damage ratio for each of the one or more of locations and identifies an equation for each of the plurality of curves, wherein the damage ratio estimation module further estimates the minimum damage ratio, the mean damage ratio, and the maximum damage ratio for the one or more locations where data pertaining to the hazard is not available based on identification of the equation for each of the plurality of curves.

BRIEF DESCRIPTION OF THE DRAWINGS
[00018] The accompanying drawings are included to provide a further understanding of the present disclosure, and are incorporated in and constitute a part of this specification. The drawings illustrate exemplary embodiments of the present disclosure and, together with the description, serve to explain the principles of the present disclosure.
[00019] FIG. 1 illustrates exemplary functional modules of a system for correlating damage ratio to risk score for each hazard and determining composite loss from one or more hazards in accordance with an embodiment of the present disclosure.
[00020] FIG. 2 illustrates process utilized for correlating damage ratio to risk score for each hazard in accordance with an embodiment of the present disclosure.
[00021] FIG. 3 illustrates process utilized for determining composite loss from one or more hazards in accordance with an embodiment of the present disclosure.
[00022] FIG. 4 illustrates an exemplary curve representative of risk score and damage ratio in accordance with an embodiment of the present disclosure.

DETAILED DESCRIPTION
[00023] All publications herein are incorporated by reference to the same extent as if each individual publication or patent application were specifically and individually indicated to be incorporated by reference. Where a definition or use of a term in an incorporated reference is inconsistent or contrary to the definition of that term provided herein, the definition of that term provided herein applies and the definition of that term in the reference does not apply.
[00024] As used in the description herein and throughout the claims that follow, the meaning of “a,” “an,” and “the” includes plural reference unless the context clearly dictates otherwise. Also, as used in the description herein, the meaning of “in” includes “in” and “on” unless the context clearly dictates otherwise.
[00025] The following is a detailed description of embodiments of the disclosure depicted in the accompanying drawings. The embodiments are in such detail as to clearly communicate the disclosure. However, the amount of detail offered is not intended to limit the anticipated variations of embodiments; on the contrary, the intention is to cover all modifications, equivalents, and alternatives falling within the spirit and scope of the present disclosure as defined by the appended claims.
[00026] Each of the appended claims defines a separate invention, which for infringement purposes is recognized as including equivalents to the various elements or limitations specified in the claims. Depending on the context, all references below to the "invention" may in some cases refer to certain specific embodiments only. In other cases it will be recognized that references to the "invention" will refer to subject matter recited in one or more, but not necessarily all, of the claims.
[00027] All methods described herein can be performed in any suitable order unless otherwise indicated herein or otherwise clearly contradicted by context. The use of any and all examples, or exemplary language (e.g. “such as”) provided with respect to certain embodiments herein is intended merely to better illuminate the invention and does not pose a limitation on the scope of the invention otherwise claimed. No language in the specification should be construed as indicating any non-claimed element essential to the practice of the invention.
[00028] Various terms are used herein. To the extent a term used in a claim is not defined below, it should be given the broadest definition persons in the pertinent art have given that term as reflected in printed publications and issued patents at the time of filing.
[00029] In the following description, numerous details are set forth. It will be apparent, however, to one skilled in the art, that the present invention may be practiced without these specific details. In other instances, well-known structures and devices are shown in block diagram form, rather than in detail, in order to avoid obscuring the present invention.
[00030] Reference throughout this specification to "one embodiment" or "an embodiment" means that a particular feature, structure, or characteristic described in connection with the embodiment is included in at least one embodiment of the present invention. Thus, the appearance of the phrases "in one embodiment" or "in an embodiment" in various places throughout this specification are not necessarily all referring to the same embodiment. Furthermore, the particular features, structures, or characteristics may be combined in any suitable manner in one or more embodiments.
[00031] Throughout the following discussion, numerous references will be made regarding servers, services, interfaces, engines, modules, clients, peers, portals, platforms, or other systems formed from computing devices. It should be appreciated that the use of such terms is deemed to represent one or more computing devices having at least one processor (e.g., ASIC, FPGA, DSP, x86, ARM, ColdFire, GPU, multi-core processors, etc.) configured to execute software instructions stored on a computer readable tangible, non- transitory medium (e.g., hard drive, solid state drive, RAM, flash, ROM, etc.). For example, a server can include one or more computers operating as a web server, database server, or other type of computer server in a manner to fulfill described roles, responsibilities, or functions. One should further appreciate the disclosed computer-based algorithms, processes, methods, or other types of instruction sets can be embodied as a computer program product comprising a non-transitory, tangible computer readable media storing the instructions that cause a processor to execute the disclosed steps. The various servers, systems, databases, or interfaces can exchange data using standardized protocols or algorithms, possibly based on HTTP, HTTPS, AES, public-private key exchanges, web service APIs, known financial transaction protocols, or other electronic information exchanging methods. Data exchanges can be conducted over a packet-switched network, a circuit- switched network, the Internet, LAN, WAN, VPN, or other type of network.
[00032] The terms "configured to" and "programmed to" in the context of a processor refer to being programmed by a set of software instructions to perform a function or set of functions.
[00033] The following discussion provides many example embodiments. Although each embodiment represents a single combination of components, this disclosure contemplates combinations of the disclosed components. Thus, for example, if one embodiment comprises components A, B, and C, and a second embodiment comprises components B and D, then the other remaining combinations of A, B, C, or D are included in this disclosure, even if not explicitly disclosed.
[00034] As used herein, and unless the context dictates otherwise, the term "coupled to" is intended to include both direct coupling (in which two elements that are coupled to each other contact each other) and indirect coupling (in which at least one additional element is located between the two elements). Therefore, the terms "coupled to" and "coupled with" are used synonymously.
[00035] In some embodiments, numerical parameters expressing quantities are used. It is to be understood that such numerical parameters may not be exact, and are instead to be understood as being modified in some instances by the term "about." Accordingly, in some embodiments, a numerical parameter is an approximation that can vary depending upon the desired properties sought to be obtained by a particular embodiment.
[00036] Unless the context dictates the contrary, ranges set forth herein should be interpreted as being inclusive of their endpoints and open-ended ranges should be interpreted to include only commercially practical values. The recitation of ranges of values herein is merely intended to serve as a shorthand method of referring individually to each separate value falling within the range. Unless otherwise indicated herein, each individual value within a range is incorporated into the specification as if it were individually recited herein. Similarly, all lists of values should be considered as inclusive of intermediate values unless the context indicates the contrary.
[00037] Groupings of alternative elements or embodiments of the inventive subject matter disclosed herein are not to be construed as limitations. Each group member can be referred to and claimed individually or in any combination with other members of the group or other elements found herein. One or more members of a group can be included in, or deleted from, a group for reasons of convenience and/or patentability. When any such inclusion or deletion occurs, the specification is herein deemed to contain the group as modified thus fulfilling the written description of all groups used in the appended claims.
[00038] Embodiments of the present disclosure provides systems and methods that enable computation and correlation of damage ratio to risk scores, and enables estimating composite loss from flood, earthquake, and cyclone perils.
[00039] An aspect of the present disclosure pertains to a method for correlating damage ratio and risk score of one or more hazards, wherein for each hazard the method comprises: receiving, by one or more processors, a dataset comprising any or a combination of a risk score and a corresponding damage ratio pertaining to each location of one or more locations where data pertaining to the hazard is available; estimating, by the one or more processors, a damage ratio for each of the plurality of probabilistic return periods of the hazard for the one or more locations based on obtaining of pre-determined probabilistic estimates for coverage and occupancy; extracting, by the one or more processors, one or more unique combinations of the plurality of probabilistic return periods, the coverage and the occupancy for each of the one or more of locations; estimating, by the one or more processors, a mean damage ratio and a standard deviation of one or more damage ratios for each of the one or more unique combinations; estimating, by the one or more processors, a minimum damage ratio and a maximum damage ratio based on the estimation of the mean damage ratio and the standard deviation for each of the one or more unique combinations; plotting, by the one or more processors, a plurality of curves between a risk score and any or a combination of the minimum damage ratio, the mean damage ratio and the maximum damage ratio for each of the one or more of locations; identifying, by the one or more processor, an equation for each of the plurality of curves; and estimating, by the one or more processors, the minimum damage ratio, the mean damage ratio, and the maximum damage ratio for the one or more locations where data pertaining to the hazard is not available based on identification of the equation for each of the plurality of curves.
[00040] In an embodiment, the dataset further comprises the risk score for each of the one or more locations where data pertaining to the hazard is not available.
[00041] In an embodiment, the one or more hazards include a flood, an earthquake, and a cyclone.
[00042] In an embodiment, flood based risk score is generated based on any or a combination of rainfall distribution, land use, land cover, topology, elevation, soil characteristics pertaining to each location.
[00043] In an embodiment, the method further comprises a step of generating a loss table based on the damage ratio pertaining to the one or more of locations for the hazard.
[00044] In an embodiment, the method further comprises a step of modeling of a composite loss from the one or more hazards so as to combine losses from the one or more hazards.
[00045] In an embodiment, the modeling is performed based on a randomness technique comprising the steps of: creating a plurality of simulations, wherein each simulation represents an event with randomly generated return period for each of the one or more hazards; estimating loss from each of the one or more hazards based on the loss table and the random return period for each of the one or more hazards; estimating, a combined loss from the one or more hazards for each simulation; and generating a combined loss table based on estimation of the combined loss of the one or more hazards for each simulation.
[00046] Another aspect of the present disclosure pertains to a system for correlating damage ratio to risk score for each hazard of the one or more hazards, said system comprising: a non-transitory storage device having embodied therein one or more routines operable to correlate damage ratio to risk score; and one or more processors coupled to the non-transitory storage device and operable to execute the one or more routines, wherein the one or more routines include: a risk score receive module, which when executed by the one or more processors, receives a dataset comprising any or a combination of a risk score and a corresponding damage ratio pertaining to each location of one or more locations where data pertaining to the hazard is available; a damage ratio estimation module, which when executed by the one or more processors, estimates a damage ratio for each of the plurality of probabilistic return periods of the hazard for the one or more locations based on obtaining of pre-determined probabilistic estimates for coverage and occupancy; a unique combination extraction module, which when executed by the one or more processors, extracts one or more unique combinations of the plurality of probabilistic return periods, the coverage and the occupancy for each of the one or more of locations; a statistical computation module, which when executed by the one or more processors, determines a mean damage ratio and a standard deviation of one or more damage ratios for each of the one or more unique combinations and estimates a minimum damage ratio and a maximum damage ratio based on the estimation of the mean damage ratio and the standard deviation for each of the one or more unique combinations; and a curve generation module, which when executed by the one or more processors, plots a plurality of curves between a risk score and any or a combination of the minimum damage ratio, the mean damage ratio and the maximum damage ratio for each of the one or more of locations and identifies an equation for each of the plurality of curves, wherein the damage ratio estimation module further estimates the minimum damage ratio, the mean damage ratio, and the maximum damage ratio for the one or more locations where data pertaining to the hazard is not available based on identification of the equation for each of the plurality of curves.
[00047] Embodiments of the present disclosure relates to a technique that correlates damage ratio to risk scores for example, flood damage ratio to flood risk scores and thereby enabling mapping of behaviour analyzed in one region to another region possessing similar conditions. In an aspect, the present disclosure relates to computation and correlation of damage ratio to risk scores and further modeling of composite losses from hazards or natural calamities such as earthquake, flood and cyclone. In an aspect, the proposed models can be probabilistic models based on stochastic events of various return periods.
[00048] In an exemplary implementation, proposed system can take insurers’ exposure as input, based on which the system can characterize exposure into various structural types and height categories. Once the exposure is characterized, an estimate of hazard intensity for one or more policies from one or more stochastic events can be generated, wherein using the structural distribution and hazard intensity, damage ratios can be calculated using vulnerability curves. Damage ratio can be applied to the sum insured associated to an insurance policy in order to get an estimate of losses. Such sequence of steps can be performed for every stochastic event, wherein losses to all policies from a single event can be aggregated, which results in generation of event loss table that has total losses from every stochastic event.
[00049] In an aspect, once loss tables (say, one each for Earthquake, Flood and Cyclone) have been developed, the event loss table can be obtained/generated in order to provide losses from every stochastic event along with exceedance probability and return period. Using the event loss table, key risk metrics of Average Annual Loss and Loss Exceedance Curves can be generated for every Peril or hazard. Detailed process for generation of said loss table is explained with reference to FIG. 2 and FIG. 3.
[00050] Therefore, it would be appreciated that the proposed system enables correlation of damage ratio to risk scores for each hazard such as flood, earthquake and cyclone, and further enables estimation of composite loss from various hazards.
[00051] FIG. 1 illustrates exemplary functional modules of a system for correlating damage ratio to risk score for each hazard and determining composite loss from one or more hazards in accordance with an embodiment of the present disclosure.
[00052] As illustrated, a system for correlating damage ratio to risk score for each hazard and determining composite loss from one or more hazards (referred to as the system 100, hereinafter) can include one or more processor(s) 102. The one or more processor(s) 102 can be implemented as one or more microprocessors, microcomputers, microcontrollers, digital signal processors, central processing units, logic circuitries, and/or any devices that manipulate data based on operational instructions. Among other capabilities, the one or more processor(s) 102 are configured to fetch and execute computer-readable instructions stored in a memory 104 of the system. The memory 104 can store one or more computer-readable instructions or routines, which may be fetched and executed to create or share the data units over a network service. The memory 104 can include any non-transitory storage device including, for example, volatile memory such as RAM, or non-volatile memory such as EPROM, flash memory, and the like. In an example embodiment, the memory 104 may be a local memory or may be located remotely, such as a server, a file server, a data server, and the Cloud.
[00053] The system can also include an interface(s) 106. The interface(s) 106 may include a variety of interfaces, for example, interfaces for data input and output devices, referred to as I/O devices, storage devices, and the like. The interface(s) 106 may facilitate communication of the system with various devices coupled to the system. The interface(s) 106 may also provide a communication pathway for one or more components of the system 100. Examples of such components include, but are not limited to, processing engine(s) 108 and data 124.
[00054] The engine(s) 108 can be implemented as a combination of hardware and programming (for example, programmable instructions) to implement one or more functionalities of the engine(s). In examples described herein, such combinations of hardware and programming may be implemented in several different ways. For example, the programming for the engine(s) 108 may be processor executable instructions stored on a non-transitory machine-readable storage medium and the hardware for the engine(s) 108 may include a processing resource (for example, one or more processors), to execute such instructions. In the present examples, the machine-readable storage medium may store instructions that, when executed by the processing resource, implement the engine(s) 108. In such examples, the system 100 can include the machine-readable storage medium storing the instructions and the processing resource to execute the instructions, or the machine-readable storage medium may be separate but accessible to system 100 and the processing resource. In other examples, the engine(s) 108 may be implemented by electronic circuitry. The data 124 can include data that is either stored or generated as a result of functionalities implemented by any of the components of the engine(s) 108.
[00055] In an example, the processing engine(s) 108 can include a risk score receive module 110, a damage ratio estimation module 112, a unique combination extraction module 114, a statistical computation module 116, a curve generation module 118, a composite loss determination module 120 and other module(s) 122. The other module(s) 122 can implement functionalities that supplement applications or functions performed by the system 100 or the processing engine(s) 108.
[00056] In an aspect, the risk score receive module 110 can receive a dataset including risk score and corresponding damage ratio for various locations for which information pertaining to the hazard is available. The dataset can also include risk scores pertaining to locations for which information pertaining to the hazard is not available. Further, the hazard can be any of a flood, an earthquake, a cyclone, and the like and location can be indicated by corresponding pincode.
[00057] In an embodiment, the damage ratio estimation module 112 can estimate damage ratio for each of probabilistic return periods of the hazard. The damage ratio for a location can be calculated based on obtaining of pre-determined probabilistic estimates for coverage classified as building, content, etc and occupancy classified as residential, commercial, industrial, etc pertaining to the locations.
[00058] In an embodiment, the unique combination extraction module 114 can extract one or more unique combinations of the return periods of the hazard, the coverage and the occupancy for locations.
[00059] In an embodiment, the statistical computation module 116 can estimate a mean damage ratio and a standard deviation of the estimated damage ratios for each of the unique combinations. Further, the statistical computation module 116 can estimate a minimum damage ratio and a maximum damage ratio based on the estimated mean damage ratio and standard deviation.
[00060] In an embodiment, the curve generation module 118 can plot various curves between risk score and the minimum damage ratio, the mean damage ratio and the maximum damage ratio for the locations and can also identify corresponding equation for said curves.
[00061] In an embodiment, based on the identified equations, the damage ratio estimation module 112 can further estimate the minimum damage ratio, the mean damage ratio, and the maximum damage ratio for locations for which information pertaining to the hazard is not available. The damage ratio estimation module 112 can further generate a loss table based on the damage ratio pertaining to the locations for the hazard.
[00062] In an embodiment, the composite loss determination module 120 can be configured to perform modeling of a composite loss from various hazards that can indicate combined loss from the hazards such as earthquake, cyclone and flood. The modeling of the composite loss can be performed based on a randomness technique that can enable creating various simulations representing an event with randomly generated return period for various hazards, estimating loss from each of the hazards based on the loss table and the random return period for each of the hazards, estimating a combined loss from the hazards for each simulation, and generating a combined loss table based on estimation of the combined loss of the hazards for each simulation.
[00063] FIG. 2 illustrates process utilized for correlating damage ratio to risk score for each hazard in accordance with an embodiment of the present disclosure.
[00064] According to an embodiment, the present disclosure relates to a method for correlating damage ratio and risk score of various hazards. For each hazard, the method includes a step 202 of receiving a dataset comprising risk score and corresponding damage ratio pertaining to each location where data pertaining to the hazard is available. Further, at step 204 damage ratio for each of the probabilistic return periods of the hazard can be estimated for the locations based on obtaining of pre-determined probabilistic estimates for various cases of coverage and occupancy. At step 206, one or more unique combinations of the probabilistic return periods, the coverage and the occupancy for each location can be extracted. Furthermore, at step 208 a mean damage ratio and a standard deviation of one or more damage ratios for each of the unique combinations can be estimated, based on which, at step 210 a minimum damage ratio and a maximum damage ratio based can be estimated. At 212 plurality of curves between risk score and the minimum damage ratio, the mean damage ratio and the maximum damage ratio for each of the location can be plotted so that equation for each curve can be identified. At step 216, the minimum damage ratio, the mean damage ratio, and the maximum damage ratio for the locations for which information pertaining to the hazard is not available can be determined based on the identified equations and the risk scores pertaining to the location.
[00065] The above mentioned embodiments can be explained with the help of following example:

Correlating flood damage ratio to flood risk scores
[00066] According to an example, flood can be a function of hydrologic inputs in the form of rainfall, its distribution, and physical characteristics, wherein physical characteristics typically include land use land cover, topography or elevation, and soil characteristics. In an implementation, flood risk score can be defined as a numerical representation of such factors that show vulnerability of that particular area against probable floods and corresponding probable losses.
[00067] In an example, the Applicant has developed probabilistic flood risk models for 51 major urban agglomerations for 40 probabilistic event sets ranging from 2 year to 500 year return period, based on which models and corresponding losses (damage ratios) to structures and content at locations indicated at pin code level and their corresponding flood risk score, damage ratio have been estimated for the rest of the pin codes that are not part of the 51 urban agglomerations.
[00068] In an example, data sets used for developing flood risk score include, but are not limited to, risk score for all pin codes where hazard is available and their corresponding damage ratio and/or risk score for all the pin codes where hazard is not available.
[00069] In an example, the Applicant has used above-mentioned parameters in deriving flood damages ratios using flood hazard risk score, wherein flood damage ratio estimation based on risk score can be an analytical and complex process using statistical techniques, which includes, in an exemplary implementation, the step 204 of estimating damage ratio for all probabilistic return period events for all the pin codes in agglomerations where probabilistic hazard estimates are available for all the cases of Coverage (Building, Content and BI) and Occupancy (Residential, Commercial, and Industrial), a step 206 of extracting unique combinations of return period, coverage and occupancy, a step 208 of estimating mean damage ratio and standard deviation for each pin code for each unique combination, a step 210 of estimating minimum and maximum damage ratio using mean damage ratio and standard deviation, a step 212 of plotting of a curve between risk score vs damage ratio for series of minimum, mean, and maximum, a step of 214 fitting of the curves for all these the series and identify equations, and a step 216 of using the equations and risk score, estimate minimum, mean, and maximum damage ratio for all the pin codes where hazard in not available.
[00070] FIG. 3 illustrates process utilized for modeling of composite loss from one or more hazards in accordance with an embodiment of the present disclosure.
[00071] According to an embodiment, the method for modeling of composite loss from one or more hazards can be performed based on a randomness technique that can include a step 302 of creating a plurality of simulations, where each simulation represents an event with randomly generated return period for each of hazard. At step 304, loss from each of hazard can be estimated based on the loss table and the random return period for each of the hazard such that at step 306 a combined loss from the hazard can be estimated for each simulation. At step 308, a combined loss table can be generated based on estimation of the combined loss of the hazards for each simulation. The above-mentioned embodiment can be further explained with the help of following example:

Composite Loss from all three Perils
[00072] In an example, three Event Loss Tables generated from Earthquake, Flood and Cyclone Nat Cat Models can be used to create a single Composite Event Loss Table that represents composite loss from all the three perils/hazards. For composite loss, it should be appreciated that the event loss tables from individual perils cannot be added simply as the probability of any peril event’s occurrence is random in nature. Therefore, it cannot be assumed that for all three perils, same return period events will occur in a single year. Thus, the proposed system and method incorporates randomness technique to combine losses from multiple perils.
[00073] In an exemplary implementation, ‘n’ simulations can be created where each simulation represents a combined event loss table. The proposed system can then generate random return periods for all three perils, and estimate losses using individual peril event loss tables. For instance, 1000 random events can be generated in every simulation, and 20000 such simulations can be run. Each event can be a combination of a randomly generated return period loss for earthquake, flood and cyclone perils, respectively, based on which the losses can then be added for every event across all three perils to create a combined event loss table for one simulation. The same process can then be repeated, say for 20000 simulations. Once all the simulations have been performed, losses can be averaged for return periods across all simulations to arrive at composite event loss table. Losses for 1 in 1000 (1000 year), 2 in 1000 (500 year), 4 in 1000 (250 year), 10 in 1000 (100 year), 20 in 1000 (50 year), 40 in 1000 (25 year), and 100 in 1000 (10 year) return periods can be computed. Such losses can then be used to create composite loss exceedance curve.
[00074] FIG. 4 illustrates an exemplary curve representative of risk score and damage ratio in accordance with an embodiment of the present disclosure. As illustrated, curves for between risk score and minimum damage ratio, mean damage ratio and maximum damage ratio can be obtained with implementation of the embodiments of the present disclosure.
[00075] While the foregoing describes various embodiments of the invention, other and further embodiments of the invention may be devised without departing from the basic scope thereof. The scope of the invention is determined by the claims that follow. The invention is not limited to the described embodiments, versions or examples, which are included to enable a person having ordinary skill in the art to make and use the invention when combined with information and knowledge available to the person having ordinary skill in the art.

ADVANTAGES OF THE PRESENT DISCLOSURE
[00076] The present disclosure provides a system and method for correlating damage ratio to risk score for each hazard.
[00077] The present disclosure provides a system and method for correlating damage ratio to risk score for each hazard for determining composite loss from one or more hazards.
[00078] The present disclosure provides a system and method for correlating damage ratio to risk score that enables modeling of flood losses.
[00079] The present disclosure provides a system and method for correlating damage ratio to risk score for each hazard that aids in determination of realistic estimation of the composite loss from earthquake, flood and cyclone.

CLAIMS:
1. A method for correlating damage ratio and risk score of one or more hazards, wherein for each hazard the method comprises:
receiving, by one or more processors, a dataset comprising any or a combination of a risk score and a corresponding damage ratio pertaining to each location of one or more locations where data pertaining to the hazard is available;
estimating, by the one or more processors, a damage ratio for each of the plurality of probabilistic return periods of the hazard for the one or more locations based on obtaining of pre-determined probabilistic estimates for coverage and occupancy;
extracting, by the one or more processors, one or more unique combinations of the plurality of probabilistic return periods, the coverage and the occupancy for each of the one or more of locations;
estimating, by the one or more processors, a mean damage ratio and a standard deviation of one or more damage ratios for each of the one or more unique combinations;
estimating, by the one or more processors, a minimum damage ratio and a maximum damage ratio based on the estimation of the mean damage ratio and the standard deviation for each of the one or more unique combinations;
plotting, by the one or more processors, a plurality of curves between a risk score and any or a combination of the minimum damage ratio, the mean damage ratio and the maximum damage ratio for each of the one or more of locations;
identifying, by the one or more processor, an equation for each of the plurality of curves; and
estimating, by the one or more processors, the minimum damage ratio, the mean damage ratio, and the maximum damage ratio for the one or more locations where data pertaining to the hazard is not available based on identification of the equation for each of the plurality of curves.

2. The method of claim 1, wherein the dataset further comprises the risk score for each of the one or more locations where data pertaining to the hazard is not available.
3. The method of claim 1, wherein the one or more hazards include a flood, an earthquake, and a cyclone.

4. The method of claim 3, wherein flood based risk score is generated based on any or a combination of rainfall distribution, land use, land cover, topology, elevation, soil characteristics pertaining to each location.

5. The method of claim 1, further comprising a step of generating a loss table based on the damage ratio pertaining to the one or more of locations for the hazard.

6. The method of claim 1, further comprising a step of modeling of a composite loss from the one or more hazards so as to combine losses from the one or more hazards.

7. The method of claim 6, wherein said modeling is performed based on a randomness technique comprising the steps of:
creating a plurality of simulations, wherein each simulation represents an event with randomly generated return period for each of the one or more hazards;
estimating loss from each of the one or more hazards based on the loss table and the random return period for each of the one or more hazards;
estimating, a combined loss from the one or more hazards for each simulation; and
generating a combined loss table based on estimation of the combined loss of the one or more hazards for each simulation.

8. A system to correlate damage ratio to risk score for each hazard of the one or more hazards, said system comprising:
a non-transitory storage device having embodied therein one or more routines operable to correlate damage ratio to risk score; and
one or more processors coupled to the non-transitory storage device and operable to execute the one or more routines, wherein the one or more routines include:
a risk score receive module, which when executed by the one or more processors, receives a dataset comprising any or a combination of a risk score and a corresponding damage ratio pertaining to each location of one or more locations where data pertaining to the hazard is available;
a damage ratio estimation module, which when executed by the one or more processors, estimates a damage ratio for each of the plurality of probabilistic return periods of the hazard for the one or more locations based on obtaining of pre-determined probabilistic estimates for coverage and occupancy;
a unique combination extraction module, which when executed by the one or more processors, extracts one or more unique combinations of the plurality of probabilistic return periods, the coverage and the occupancy for each of the one or more of locations;
a statistical computation module, which when executed by the one or more processors, determines a mean damage ratio and a standard deviation of one or more damage ratios for each of the one or more unique combinations and estimates a minimum damage ratio and a maximum damage ratio based on the estimation of the mean damage ratio and the standard deviation for each of the one or more unique combinations; and
a curve generation module, which when executed by the one or more processors, plots a plurality of curves between a risk score and any or a combination of the minimum damage ratio, the mean damage ratio and the maximum damage ratio for each of the one or more of locations and identifies an equation for each of the plurality of curves, wherein
the damage ratio estimation module further estimates the minimum damage ratio, the mean damage ratio, and the maximum damage ratio for the one or more locations where data pertaining to the hazard is not available based on identification of the equation for each of the plurality of curves.

9. The system of claim 1, wherein the damage ratio estimation module generates a loss table based on the damage ratio pertaining to the one or more locations for the hazard.

10. The system of claim 9, further comprising a composite loss determination module configured to perform modeling of a composite loss from the one or more hazards so as to combine losses from the one or more hazards based on a randomness technique, enabled to:
creating a plurality of simulations, wherein each simulation represents an event with randomly generated return period for each of the one or more hazards;
estimating loss from each of the one or more hazards based on the loss table and the random return period for each of the one or more hazards;
estimating, a combined loss from the one or more hazards for each simulation; and
generating a combined loss table based on estimation of the combined loss of the one or more hazards for each simulation.