EP3097523A1 - Model customization by parameter adjustment - Google Patents

Model customization by parameter adjustment

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Publication number
EP3097523A1
EP3097523A1 EP15740480.7A EP15740480A EP3097523A1 EP 3097523 A1 EP3097523 A1 EP 3097523A1 EP 15740480 A EP15740480 A EP 15740480A EP 3097523 A1 EP3097523 A1 EP 3097523A1
Authority
EP
European Patent Office
Prior art keywords
loss
computing device
data
parameter
parameters
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Withdrawn
Application number
EP15740480.7A
Other languages
German (de)
French (fr)
Other versions
EP3097523A4 (en
Inventor
Steven A. JAKUBOWSKI
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Impact Forecasting LLC
Original Assignee
Impact Forecasting LLC
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Impact Forecasting LLC filed Critical Impact Forecasting LLC
Publication of EP3097523A1 publication Critical patent/EP3097523A1/en
Publication of EP3097523A4 publication Critical patent/EP3097523A4/en
Withdrawn legal-status Critical Current

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Classifications

    • H—ELECTRICITY
    • H01—ELECTRIC ELEMENTS
    • H01L—SEMICONDUCTOR DEVICES NOT COVERED BY CLASS H10
    • H01L21/00—Processes or apparatus adapted for the manufacture or treatment of semiconductor or solid state devices or of parts thereof
    • H01L21/70—Manufacture or treatment of devices consisting of a plurality of solid state components formed in or on a common substrate or of parts thereof; Manufacture of integrated circuit devices or of parts thereof
    • H01L21/77—Manufacture or treatment of devices consisting of a plurality of solid state components or integrated circuits formed in, or on, a common substrate
    • H01L21/78—Manufacture or treatment of devices consisting of a plurality of solid state components or integrated circuits formed in, or on, a common substrate with subsequent division of the substrate into plural individual devices
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q40/00—Finance; Insurance; Tax strategies; Processing of corporate or income taxes
    • G06Q40/08—Insurance
    • H—ELECTRICITY
    • H01—ELECTRIC ELEMENTS
    • H01L—SEMICONDUCTOR DEVICES NOT COVERED BY CLASS H10
    • H01L21/00—Processes or apparatus adapted for the manufacture or treatment of semiconductor or solid state devices or of parts thereof
    • H01L21/02—Manufacture or treatment of semiconductor devices or of parts thereof
    • H01L21/04—Manufacture or treatment of semiconductor devices or of parts thereof the devices having at least one potential-jump barrier or surface barrier, e.g. PN junction, depletion layer or carrier concentration layer
    • H01L21/50—Assembly of semiconductor devices using processes or apparatus not provided for in a single one of the subgroups H01L21/06 - H01L21/326, e.g. sealing of a cap to a base of a container
    • H01L21/56—Encapsulations, e.g. encapsulation layers, coatings
    • H01L21/561—Batch processing
    • H—ELECTRICITY
    • H01—ELECTRIC ELEMENTS
    • H01L—SEMICONDUCTOR DEVICES NOT COVERED BY CLASS H10
    • H01L23/00—Details of semiconductor or other solid state devices
    • H01L23/28—Encapsulations, e.g. encapsulating layers, coatings, e.g. for protection
    • H01L23/31—Encapsulations, e.g. encapsulating layers, coatings, e.g. for protection characterised by the arrangement or shape
    • H01L23/3107—Encapsulations, e.g. encapsulating layers, coatings, e.g. for protection characterised by the arrangement or shape the device being completely enclosed
    • H01L23/3114—Encapsulations, e.g. encapsulating layers, coatings, e.g. for protection characterised by the arrangement or shape the device being completely enclosed the device being a chip scale package, e.g. CSP
    • H—ELECTRICITY
    • H01—ELECTRIC ELEMENTS
    • H01L—SEMICONDUCTOR DEVICES NOT COVERED BY CLASS H10
    • H01L2224/00—Indexing scheme for arrangements for connecting or disconnecting semiconductor or solid-state bodies and methods related thereto as covered by H01L24/00
    • H01L2224/01—Means for bonding being attached to, or being formed on, the surface to be connected, e.g. chip-to-package, die-attach, "first-level" interconnects; Manufacturing methods related thereto
    • H01L2224/10—Bump connectors; Manufacturing methods related thereto
    • H01L2224/11—Manufacturing methods
    • H—ELECTRICITY
    • H01—ELECTRIC ELEMENTS
    • H01L—SEMICONDUCTOR DEVICES NOT COVERED BY CLASS H10
    • H01L23/00—Details of semiconductor or other solid state devices
    • H01L23/562—Protection against mechanical damage
    • H—ELECTRICITY
    • H01—ELECTRIC ELEMENTS
    • H01L—SEMICONDUCTOR DEVICES NOT COVERED BY CLASS H10
    • H01L2924/00—Indexing scheme for arrangements or methods for connecting or disconnecting semiconductor or solid-state bodies as covered by H01L24/00
    • H01L2924/0001—Technical content checked by a classifier
    • H01L2924/0002—Not covered by any one of groups H01L24/00, H01L24/00 and H01L2224/00

Definitions

  • the present technology relates to systems and methods for adjusting loss parameters that are used in calculating losses when modeling the occurrence of catastrophic events.
  • the systems and methods of the present technology relate to improvements that can be implemented within catastrophe modeling platforms, which provide analytic computing device simulation of natural or man-made catastrophes (such as hurricane, earthquakes, severe thunderstorms, wildfire, and terrorism perils).
  • catastrophe modeling platforms which provide analytic computing device simulation of natural or man-made catastrophes (such as hurricane, earthquakes, severe thunderstorms, wildfire, and terrorism perils).
  • a catastrophe modeling platform is a client-server based computing device simulation platform produced by Impact Forecasting, LLC and that is provided as the ELEMENTS suite.
  • the ELEMENTS catastrophe modeling suite is a set of compiled computing device application code used in the analysis and processing of insurance or other portfolio data.
  • the ELEMENTS suite includes data code designed for use on Microsoft Software operating systems (primarily on the client server environment).
  • the ELEMENTS suite provides a platform that contains multiple analysis modules for handling and processing property, casualty and life portfolios to determine expected financial losses due to natural and manmade catastrophes.
  • Some of the major modules include: the data import facility, the determination of hazard (for various perils), the determination of ground up loss based on the vulnerability (calculation of normalized loss ratios for various hazard intensities), and application of insurance structures (the financial aspects of insurance - such as limits, deductibles, reinsurance, etc.)
  • the data import facility processes client exposure data (such as property values including the replacement value of structures, monetary value of personal property or time based coverages that include additional living expenses, loss of rents, business interruption, and various insurance terms for deductibles, limits, layer positions, and reinsurance).
  • client exposure data such as property values including the replacement value of structures, monetary value of personal property or time based coverages that include additional living expenses, loss of rents, business interruption, and various insurance terms for deductibles, limits, layer positions, and reinsurance).
  • the determination of hazard is a complex suite of analytical algorithms and supporting peril data that perform mathematical modeling of various aspects of the hazard examined.
  • the purpose of this hazard module is to produce the likely frequency and severity of hazard outcomes for various natural perils. For example, in the case of tropical cyclones for the Atlantic basin (called hurricanes in this region), the hazard module produces a suite of expected future outcomes that describe the wind speed for various locations for a collection of simulated hurricanes.
  • the vulnerability module incorporates the frequency and severity data from the hazard with various types of construction, occupancy and other classifications to produce expected financial loss data (normalized loss ratios which are combined with exposure data to determine the overall loss).
  • the vulnerability module contains a large number of supporting tables, data, or mathematical relationships which correlate expected losses with various hazard intensity values. Depending upon the number of construction classification types (such as year built, region, building materials used in construction, occupancy, building code classifications, etc.), the number of components in the vulnerability tables or supporting mathematical relationships can be quite large (hundreds or thousands of records within supporting tables).
  • the vulnerability module provides the methods and procedures that identify which individual damage functions apply to each construction and occupancy classification (which may involve tables that identify construction type, occupancy, roof material, age of construction, geophysical regions).
  • the processing of insurance terms within the financial module incorporates the initial loss estimate at the property or insurance site (called ground up losses) and sequentially applies any and all appropriate insurance data.
  • Insurance terms can include but are not limited to: various forms of deductibles, which can be applied at the coverage, site, or policy level; insurance limits, which can be applied at the coverage, site, or policy level; reinsurance terms such as treaty or facultative insurance terms; and layer applications, such as pro rata or excess of loss insurance terms.
  • the outcome of the overall analytical process is a collection of expected losses, which describe the frequency and severity of future outcomes based on the client or exposure data provided.
  • Figure 1 illustrates one example of a computing device system of the present technology.
  • Figure 2 illustrates one example of a parameter adjustment menu screen of the present technology.
  • Model customization in accordance with the present technology can provide support to model users to provide (1) calibration based on model differences between actual known claims results and modeled loss results, (2) modification of client expectations (adjustments based on in-house user knowledge), and/or (3) sensitivity such as uncertainty around large hurricanes or earthquakes in regions that have little history for a basis.
  • Model customization in accordance with the present technology allows for modification of model parameters in two major areas: (1) loss severity and (2) event frequency adjustment.
  • the purpose of parameter adjustment in each area can, for example, be to improve the predictive value of the vulnerability module, and/or provide a facility for "what-if sensitivity analysis on the uncertainty within the model.
  • Model customization in accordance with the present technology allows individual users of a catastrophe model to modify parameters relating to the frequency and severity of loss.
  • the modification of loss severity occurs at the "ground up" level, before insurance terms like limits and deductibles.
  • Modification within the core vulnerability model at the ground up level can provide a consistent approach for better predictive value. For example, customizing the loss behavior of residential wood frame (at the ground up level) by 20% allows modification of insurance terms (like reduced deductibles) without further adjustment for consistent application.
  • the parameters that can be adjusted can include:
  • Construction classification including for example, wood-frame, masonry, steel frame, reinforced concrete, or mobile home.
  • Line of business including for example, residential, commercial, industrial, or agricultural
  • Region - including for example, North, Northeast, Northwest, West, East, Midwest, Southeast, Southwest, etc. Regions can be on a national scale, but can also be on a local scale, such as by state, or geographic region within a state.
  • loss severity adjustments can be applied uniformly across a user's portfolio (for all portfolio locations), or can be applied to a subset of portfolio locations based on construction and/or regional classification.
  • the parameters that can be adjusted can include:
  • Region - including for example, North, Northeast, Northwest, West, East, Midwest, Southeast, Southwest, etc. Regions can be on a national scale, but can also be on a local scale, such as by state, or geographic region within a state.
  • Event magnitude including for example, tropical storm category, earthquake magnitude, tornado category, etc.
  • loss frequency adjustments can be applied uniformly across all events, or can be applied to a subset of selected events based on region or event magnitude.
  • Each parameter in the areas of loss severity and loss frequency can be adjusted by an adjustment factor.
  • the adjustment factor can be a scalar multiplier, where the nominal value is set to 1.0, representing no change.
  • the adjustment factor for parameters that are not being adjusted can be set to a value of 1.0
  • the adjustment factor for parameters that are being adjusted to have an increase in ground up losses can be set to a value of greater than 1.0
  • the adjustment factor for parameters that are being adjusted to have a decrease in ground up losses can be set to a value of less than 1.0.
  • the scalars may range from zero, or nearly zero, to two.
  • the catastrophe model can be run using the adjusted parameters.
  • the catastrophe model applies all appropriate insurance terms - deductibles, limits, layers, reinsurance. Due to the non-linear nature of the algorithms within the catastrophe model, the scalar adjustment factors applied to any of the frequency or severity loss parameters may not result in a linear adjustment to the losses calculated based on the adjusted parameters. For example, a 10% change in ground up losses may produce 20% change in Probable Maximum Loss metrics.
  • FIGURE 1 Elements of an exemplary computing device system are illustrated in FIGURE 1, in which the catastrophe model and parameter adjustment functionality are provided to a user by a computing device 100.
  • Computing device 100 can be connected to a local area network (LAN) 102 and/or a wide area network (WAN) 104.
  • Computing device 100 can include a central processor 110, that controls the overall operation of the computing device, and a system bus 112, that connects central processor 1 10 to the components described herein.
  • System bus 112 may be implemented with any one of a variety of conventional bus architectures.
  • Computing device 100 can include a variety of interface units and drives for reading and writing data or files.
  • computing device 100 can include a local memory interface 114, and a removable memory interface 116, respectively coupling a hard disk drive 1 18, and a removable memory drive 120, to system bus 1 12.
  • removable memory drives include magnetic disk drives, and optical disk drives, that receive removable memory elements 122.
  • Hard disks in general, include one or more read/write heads that convert bits to magnetic pulses when writing to a computing device-readable medium; and convert magnetic pulses to bits when reading data from the computing device readable medium.
  • a single hard disk drive 118 and a single removable memory drive 120 are shown for illustration purposes only and with the understanding that computing device 100 may include several of such drives.
  • Computing device 100 may include drives for interfacing with other types of computing device readable media such as magneto-optical drives.
  • system memories such as system memory 120, generally read and write data electronically and do not include read/write heads.
  • System memory 120 may be implemented with a conventional system memory having a read only memory section that stores a basic input/output system (BIOS) and a random access memory (RAM) that stores other data and files.
  • BIOS basic input/output system
  • RAM random access memory
  • FIGURE 1 shows a universal serial bus (USB) interface 122 coupling a keyboard 124 and a pointing device 126 to system bus 112.
  • Pointing device 132 may be implemented with a hardwired or wireless mouse, track ball, pen device, or similar device.
  • Computing device 100 may include additional interfaces for connecting peripheral devices to system bus 112.
  • FIGURE 1 shows a IEEE 1394 interface 128 that may be used to couple additional devices to computing device 100.
  • Peripheral devices may include game pads scanners, printers, and other input and output devices and may be coupled to system bus 112 through parallel ports, game-ports, PCI boards, or any other interface used to couple peripheral devices to a computing device.
  • Computing device 100 also includes a video adapter 130 coupling a display device 132 to system bus 112.
  • Display device 132 may include a cathode ray tube (CRT), liquid crystal display (LCD), field emission display (FED), plasma display or any other device that produces an image that is viewable by the user.
  • a touchscreen interface 134 may be included to couple a touchscreen (not shown) to system buss 112.
  • a touchscreen may overlay at least part of the display region of display device 132 and may be implemented with a convention touchscreen technology, such as capacitive or resistive touchscreen technology.
  • FIGURE 1 One skilled in the art will appreciate that the device connections shown in FIGURE 1 are for illustration purposes only and that several of the peripheral devices could be coupled to system bus 112 via alternative interfaces.
  • a video camera could be connected to IEEE 1394 interface 128 and pointing device 126 could be connected to another interface.
  • Computing device 100 may include a network interface 136 that couples system bus 1 12 to LAN 102.
  • LAN 102 may have one or more of the well-known LAN topologies and may use a variety of different protocols, such as Ethernet.
  • Computing device 100 may communicate with other computing devices and devices connected to LAN 102, such as computing device 138 and printer 140.
  • Computing devices and other devices may be connected to LAN 102 via twisted pair wires, coaxial cable, fiber optics or other media.
  • electromagnetic waves such as radio frequency waves, may be used to connect one or more computing devices or devices to LAN 102.
  • a wide area network 104 such as the Internet, can also be accessed by computing device 100.
  • FIGURE 1 shows network interface 136 connected to LAN 102.
  • LAN 102 may be used to connect to WAN 104.
  • FIGURE 1 shows a router 142 that may connect LAN 102 to WAN 104, in a conventional manner.
  • Server 144, mobile terminal 146 and a computing device 148 are shown connected to or in electronic communication with WAN 104. Numerous additional servers, computing devices, handheld devices, personal digital assistants, telephones and other devices can also be connected to WAN 104.
  • a mobile network card 150 may be used to connect to LAN 102 and/or WAN 104; and a mobile network card may be configured to connect to LAN 102 and/or WAN 104 via a mobile telephone network, in a conventional manner.
  • computing device 100 and server 144 can be controlled by computing device-executable instructions stored on a non-transient computing device- readable medium.
  • computing device 100 may include computing device-executable instructions stored on a memory for transmitting information to server 144, receiving information from server 144, and displaying the received information on display device 132.
  • server 144 can include (stored on a memory computing device) executable instructions for receiving requests from computing device 100, and processing data and transmitting data going to computing device 100.
  • server 144 transmits hypertext markup language (HTML) and extensible markup language (XML) formatted data, to computing device 100.
  • HTML hypertext markup language
  • XML extensible markup language
  • network should be broadly interpreted to include not only systems in which remote storage devices are coupled together via one or more communication paths, but also stand-alone devices that may be coupled, from time to time, to such systems that have storage capability. Consequently, the term “network” includes not only a “physical network” 102 and 104, but also a “content network,” which is comprised of the data- attributable to a single entity— which resides across all physical networks.
  • Methods of the present technology can include using the computing device 100 to provide a catastrophe loss model including loss model data, which can include both exposure data and event loss data.
  • Methods can include the computing device providing a plurality of loss parameters, with each loss parameter having a variable adjustment factor set to a default value.
  • the plurality of loss parameters can be provided, by a parameter adjustment menu screen.
  • Loss parameters can include severity parameters and frequency parameters.
  • Figure 2 illustrates one example of a parameter adjustment menu screen 200 that can be provided to a user by computing device 100 to allow a user to adjust loss severity parameters 202 and frequency parameters 204.
  • the numerical value 206 for each adjustment factor presented for each parameter is set to a default value of 1.0. Because the numerical value for each adjustment factor in this example is a scalar multiplier, the default value of 1.0 indicates no change.
  • the user can modify the value of at least one adjustment factor for at least one adjustment parameter to create a modified adjustment parameter.
  • a user can input modified values for one or more of the adjustment factors, as desired.
  • the computing device 100 can receive the modified value, or values, input by a user, and can apply the modified adjustment parameter to the loss model data to generate adjusted loss model data.
  • the modified adjustment factor can be applied to the exposure data.
  • the loss parameter for which a modified adjustment factor is received is a frequency factor
  • the modified adjustment factor can be applied to the event loss data.
  • each modified adjustment factor for a given severity factor can be applied to the exposure data, and each modified adjustment factor for a given frequency factor can be applied to the event loss data.
  • the computing device 100 can compute at least one of gross or net losses based on the adjusted loss model data.
  • FIGURE 3 shows properties in coastal states ranging from Texas to Maine that are part of the fictional portfolio.
  • the portfolio includes structures and contents coverages.
  • the structures in the portfolio include a mix of masonry, wood frame, and unknown construction.
  • the age of the structures varies.
  • the insurance includes a 5% coverage deductible and includes 50% facultative placement.
  • the first and second tables provided below in TABLE A show values (amounts listed in dollars) respectively before and after application of a construction type factor adjustment in the amount of 1.20x.
  • the notional results provided below relate to various hurricanes and show the total losses based on application of the ground up adjustment factor.
  • the net treaty column reflects the value of any other form of insurance such as reinsurance.
  • FIGURE 4 provides a map of Florida to depict the various regions of varying wind gusts as measured in meters/second and the related hurricane wind peril that were experienced during Hurricane Jeanne. The wind speed allows for simulation of losses. This map shows the importance of accurately assessing risk based on information on specific locations.
  • FIGURES 5-6 show two graphs that depicting net losses respectively for a moderate hazard and for a small hazard, which is just above the deductible. Together, these two graphs show that while absolute differences are larger for high-hazard regions, proportional changes to net losses are relatively higher in small-hazard regions, where resulting losses are only marginally above the deductible hurdle. The graphs also show that marginal losses occurring on the outer boundary of the hazard region may affect a large area of additional net losses.
  • FIGURE 7 provides a map of Florida to depict the track of Hurricane Jeanne and the associated average losses in various regions of Florida.
  • the data related to the map allows for a comparison of original losses vs. adjusted losses after parameter adjustment.
  • ground up adjustment to construction was set to a uniform 1.2x for all regions and types. This example indicated that gross losses were about 1.325x on average. Note that loss effects are not uniform spatially across the wind hazard region. In particular, along the central track, relative changes were lower; and along the peripheral regions where the wind speeds were lower, the relative changes were higher.
  • the screen shot shows the use of an adjustment factor based on the construction type.
  • the adjustment factor can be further adjusted based on the location, using data for the Impact Forecasting regions.
  • the table below, TABLE B provides an example of the combined calculation based on construction type including masonry (MAS), wood frame (WD) and unknown (UNK); and location including Florida (FL), Southeastern states (SE), and Gulf states (Gulf).
  • the data in the table below provides a stochastic analysis that shows before and after adjustments, with breakouts for construction type and regions.
  • FIGURE 2 illustrates one example of a parameter adjustment menu screen 200 that can be provided to a user by computing device 100 to allow a user to adjust frequency parameters 204.
  • the frequency-related parameter relates to the storm category, for instance a Category 3, 4 or 5, for a particular region, such as Florida, the Gulf states, the Southeast states, or the Northeast states.
  • a fictional portfolio of properties is assessed, properties in Western states including California, Oregon, and Washington that are part of the fictional portfolio.
  • the portfolio includes structures and contents coverages.
  • the structures in the portfolio include a mix of masonry, wood frame, and unknown construction.
  • the age of the structures varies.
  • the insurance includes a 5% coverage deductible and includes 50% facultative placement.
  • the first and second tables provided below show values respectively before and after application of a construction type factor adjustment in the amount of 1.20x.
  • the notional results provided below relate to various earthquakes and show the total losses based on application of the ground up adjustment factor.
  • the net treaty column reflects the value of any other form of insurance, such as reinsurance.
  • FIGURE 9 provides a map of Southern California to depict the various regions of earthquake shake peril and peak ground acceleration. Specifically, the map depicts the USGS Great Southern California ShakeOut scenario, based on a magnitude 7.8 earthquake along the San Andres fault. This map shows the importance of parameter adjustment factors accurately assessing risk based on information on specific locations.
  • FIGURE 10 provides a map of Southern California to depict the path of USGS Great Southern California ShakeOut scenario along the San Andres fault.
  • the data related to the map allows for a comparison of original losses vs. adjusted losses after parameter adjustment.
  • ground up parameter adjustment factors to construction was set to a uniform 1.2x for all regions and types. This example indicated that gross losses were about 1.374x, on average. Note that loss effects are not uniform spatially across the earthquake hazard region. In particular, along the near-fault regions, relative changes were lower and along the far-field regions, (lower PGA) the relative changes were higher.
  • the screen shot shows the use of an adjustment factor based on the construction type.
  • the peril field can be moved from Hurricane in Example 1 to Earthquake to yield the screen shot shown in FIG. 11.
  • the parameter adjustment factor can then be further adjusted based on the location using data for Impact Forecasting regions.
  • the table below provides a stochastic analysis with a combined calculation based on construction type including reinforced concrete (RC), wood frame (WD) and unknown (UNK); and location including California (CA) and Pacific Northwest (PNW).
  • FIGURE 11 illustrates one example of a parameter adjustment menu screen 200 shown in FIGURE 2, that can be provided to a user by computing device 100, to allow a user to adjust the frequency parameters.
  • the parameter adjustment factor relates to the occurrence rate.
  • the adjustment factor also includes the earthquake magnitude for a particular region including California (CA) and Pacific Northwest (PNW).
  • AAL 52,700,000 15,700,000 15,100,000 AAL 1.042 1.047 1.049
  • ground up losses can be multiplied by a given scalar (either up or down) and losses can be propagated through the account structure in the loss model. Losses that are net of insurance applications (deductibles, limits, reinsurance, etc.) may have a more pronounced effect than the ground up factor. Net of insurance effects are larger on relatively smaller loss regions (far- field/outer boundary conditions).
  • Examples 1 and 2 show that parameter adjustment to both frequency and severity, can be made on any given analysis.
  • the resulting analysis provides a unique functionality over and above changes made to the Event Loss table modification.

Abstract

Systems and methods of the present technology allow users of a catastrophe model to modify frequency and severity loss parameters and analyze how the modifications affect the losses calculated by the model.

Description

MODEL CUSTOMIZATION BY PARAMETER ADJUSTMENT
FIELD OF THE INVENTION
[0001] The present technology relates to systems and methods for adjusting loss parameters that are used in calculating losses when modeling the occurrence of catastrophic events.
DESCRIPTION OF RELATED ART
[0002] The systems and methods of the present technology relate to improvements that can be implemented within catastrophe modeling platforms, which provide analytic computing device simulation of natural or man-made catastrophes (such as hurricane, earthquakes, severe thunderstorms, wildfire, and terrorism perils). One example of a catastrophe modeling platform is a client-server based computing device simulation platform produced by Impact Forecasting, LLC and that is provided as the ELEMENTS suite. The ELEMENTS catastrophe modeling suite is a set of compiled computing device application code used in the analysis and processing of insurance or other portfolio data. The ELEMENTS suite includes data code designed for use on Microsoft Software operating systems (primarily on the client server environment).
[0003] The ELEMENTS suite provides a platform that contains multiple analysis modules for handling and processing property, casualty and life portfolios to determine expected financial losses due to natural and manmade catastrophes. Some of the major modules include: the data import facility, the determination of hazard (for various perils), the determination of ground up loss based on the vulnerability (calculation of normalized loss ratios for various hazard intensities), and application of insurance structures (the financial aspects of insurance - such as limits, deductibles, reinsurance, etc.)
[0004] The data import facility processes client exposure data (such as property values including the replacement value of structures, monetary value of personal property or time based coverages that include additional living expenses, loss of rents, business interruption, and various insurance terms for deductibles, limits, layer positions, and reinsurance).
[0005] The determination of hazard is a complex suite of analytical algorithms and supporting peril data that perform mathematical modeling of various aspects of the hazard examined. The purpose of this hazard module is to produce the likely frequency and severity of hazard outcomes for various natural perils. For example, in the case of tropical cyclones for the Atlantic basin (called hurricanes in this region), the hazard module produces a suite of expected future outcomes that describe the wind speed for various locations for a collection of simulated hurricanes.
[0006] The vulnerability module incorporates the frequency and severity data from the hazard with various types of construction, occupancy and other classifications to produce expected financial loss data (normalized loss ratios which are combined with exposure data to determine the overall loss). The vulnerability module contains a large number of supporting tables, data, or mathematical relationships which correlate expected losses with various hazard intensity values. Depending upon the number of construction classification types (such as year built, region, building materials used in construction, occupancy, building code classifications, etc.), the number of components in the vulnerability tables or supporting mathematical relationships can be quite large (hundreds or thousands of records within supporting tables). The vulnerability module provides the methods and procedures that identify which individual damage functions apply to each construction and occupancy classification (which may involve tables that identify construction type, occupancy, roof material, age of construction, geophysical regions).
[0007] The processing of insurance terms within the financial module incorporates the initial loss estimate at the property or insurance site (called ground up losses) and sequentially applies any and all appropriate insurance data. Insurance terms can include but are not limited to: various forms of deductibles, which can be applied at the coverage, site, or policy level; insurance limits, which can be applied at the coverage, site, or policy level; reinsurance terms such as treaty or facultative insurance terms; and layer applications, such as pro rata or excess of loss insurance terms. [0008] The outcome of the overall analytical process is a collection of expected losses, which describe the frequency and severity of future outcomes based on the client or exposure data provided.
BRIEF DESCRIPTION OF THE DRAWINGS
[0009] Specific examples have been chosen for purposes of illustration and description, and are shown in the accompanying drawings, forming a part of the specification.
[0010] Figure 1 illustrates one example of a computing device system of the present technology.
[0011] Figure 2 illustrates one example of a parameter adjustment menu screen of the present technology.
DETAILED DESCRIPTION
[0012] Model customization in accordance with the present technology can provide support to model users to provide (1) calibration based on model differences between actual known claims results and modeled loss results, (2) modification of client expectations (adjustments based on in-house user knowledge), and/or (3) sensitivity such as uncertainty around large hurricanes or earthquakes in regions that have little history for a basis.
[0013] Model customization in accordance with the present technology allows for modification of model parameters in two major areas: (1) loss severity and (2) event frequency adjustment. The purpose of parameter adjustment in each area can, for example, be to improve the predictive value of the vulnerability module, and/or provide a facility for "what-if sensitivity analysis on the uncertainty within the model.
[0014] Model customization in accordance with the present technology allows individual users of a catastrophe model to modify parameters relating to the frequency and severity of loss. The modification of loss severity occurs at the "ground up" level, before insurance terms like limits and deductibles. Modification within the core vulnerability model at the ground up level can provide a consistent approach for better predictive value. For example, customizing the loss behavior of residential wood frame (at the ground up level) by 20% allows modification of insurance terms (like reduced deductibles) without further adjustment for consistent application.
[0015] In the area of loss severity, the parameters that can be adjusted can include:
• All portfolio records
• Construction classification - including for example, wood-frame, masonry, steel frame, reinforced concrete, or mobile home.
• Line of business - including for example, residential, commercial, industrial, or agricultural
• Building age - including for example a year band such as "< 1995," "1995 - 2001," " post 2001," etc.
• Region - including for example, North, Northeast, Northwest, West, East, Midwest, Southeast, Southwest, etc. Regions can be on a national scale, but can also be on a local scale, such as by state, or geographic region within a state.
• Building size - including for example, a square footage range such as "<1500 sq ft," "1500 - 3500 sq ft," or " >=3500 sq ft"
[0016] Using these loss severity parameters, loss severity adjustments can be applied uniformly across a user's portfolio (for all portfolio locations), or can be applied to a subset of portfolio locations based on construction and/or regional classification.
[0017] In the area of loss frequency, the parameters that can be adjusted can include:
• All event frequencies
• Region - including for example, North, Northeast, Northwest, West, East, Midwest, Southeast, Southwest, etc. Regions can be on a national scale, but can also be on a local scale, such as by state, or geographic region within a state.
• Event magnitude - including for example, tropical storm category, earthquake magnitude, tornado category, etc. [0018] Using these loss frequency parameters, loss frequency adjustments can be applied uniformly across all events, or can be applied to a subset of selected events based on region or event magnitude.
[0019] Each parameter in the areas of loss severity and loss frequency can be adjusted by an adjustment factor. The adjustment factor can be a scalar multiplier, where the nominal value is set to 1.0, representing no change. In such an example, the adjustment factor for parameters that are not being adjusted can be set to a value of 1.0, the adjustment factor for parameters that are being adjusted to have an increase in ground up losses can be set to a value of greater than 1.0, and the adjustment factor for parameters that are being adjusted to have a decrease in ground up losses can be set to a value of less than 1.0. For example, the scalars may range from zero, or nearly zero, to two.
[0020] After adjustment factors are applied to the parameters as desired by the user, and adjusted parameters are generated, the catastrophe model can be run using the adjusted parameters. The catastrophe model applies all appropriate insurance terms - deductibles, limits, layers, reinsurance. Due to the non-linear nature of the algorithms within the catastrophe model, the scalar adjustment factors applied to any of the frequency or severity loss parameters may not result in a linear adjustment to the losses calculated based on the adjusted parameters. For example, a 10% change in ground up losses may produce 20% change in Probable Maximum Loss metrics.
[0021] Various examples of the present technology may be implemented with computing device devices, computing device networks and systems that exchange and present information. Elements of an exemplary computing device system are illustrated in FIGURE 1, in which the catastrophe model and parameter adjustment functionality are provided to a user by a computing device 100. Computing device 100 can be connected to a local area network (LAN) 102 and/or a wide area network (WAN) 104. Computing device 100 can include a central processor 110, that controls the overall operation of the computing device, and a system bus 112, that connects central processor 1 10 to the components described herein. System bus 112 may be implemented with any one of a variety of conventional bus architectures. [0022] Computing device 100 can include a variety of interface units and drives for reading and writing data or files. In particular, computing device 100 can include a local memory interface 114, and a removable memory interface 116, respectively coupling a hard disk drive 1 18, and a removable memory drive 120, to system bus 1 12. Examples of removable memory drives include magnetic disk drives, and optical disk drives, that receive removable memory elements 122. Hard disks, in general, include one or more read/write heads that convert bits to magnetic pulses when writing to a computing device-readable medium; and convert magnetic pulses to bits when reading data from the computing device readable medium. A single hard disk drive 118 and a single removable memory drive 120 are shown for illustration purposes only and with the understanding that computing device 100 may include several of such drives. Computing device 100 may include drives for interfacing with other types of computing device readable media such as magneto-optical drives.
[0023] Unlike hard disks, system memories, such as system memory 120, generally read and write data electronically and do not include read/write heads. System memory 120 may be implemented with a conventional system memory having a read only memory section that stores a basic input/output system (BIOS) and a random access memory (RAM) that stores other data and files.
[0024] A user can interact with computing device 100 with a variety of input devices, and through graphical user interfaces provided to the user by the computing device 100, such as though a browser application. For example, FIGURE 1 shows a universal serial bus (USB) interface 122 coupling a keyboard 124 and a pointing device 126 to system bus 112. Pointing device 132 may be implemented with a hardwired or wireless mouse, track ball, pen device, or similar device.
[0025] Computing device 100 may include additional interfaces for connecting peripheral devices to system bus 112. FIGURE 1 shows a IEEE 1394 interface 128 that may be used to couple additional devices to computing device 100. Peripheral devices may include game pads scanners, printers, and other input and output devices and may be coupled to system bus 112 through parallel ports, game-ports, PCI boards, or any other interface used to couple peripheral devices to a computing device. [0026] Computing device 100 also includes a video adapter 130 coupling a display device 132 to system bus 112. Display device 132 may include a cathode ray tube (CRT), liquid crystal display (LCD), field emission display (FED), plasma display or any other device that produces an image that is viewable by the user. A touchscreen interface 134 may be included to couple a touchscreen (not shown) to system buss 112. A touchscreen may overlay at least part of the display region of display device 132 and may be implemented with a convention touchscreen technology, such as capacitive or resistive touchscreen technology.
[0027] One skilled in the art will appreciate that the device connections shown in FIGURE 1 are for illustration purposes only and that several of the peripheral devices could be coupled to system bus 112 via alternative interfaces. For example, a video camera could be connected to IEEE 1394 interface 128 and pointing device 126 could be connected to another interface.
[0028] Computing device 100 may include a network interface 136 that couples system bus 1 12 to LAN 102. LAN 102 may have one or more of the well-known LAN topologies and may use a variety of different protocols, such as Ethernet. Computing device 100 may communicate with other computing devices and devices connected to LAN 102, such as computing device 138 and printer 140. Computing devices and other devices may be connected to LAN 102 via twisted pair wires, coaxial cable, fiber optics or other media. Alternatively, electromagnetic waves, such as radio frequency waves, may be used to connect one or more computing devices or devices to LAN 102.
[0029] A wide area network 104, such as the Internet, can also be accessed by computing device 100. FIGURE 1 shows network interface 136 connected to LAN 102. LAN 102 may be used to connect to WAN 104. FIGURE 1 shows a router 142 that may connect LAN 102 to WAN 104, in a conventional manner. Server 144, mobile terminal 146 and a computing device 148 are shown connected to or in electronic communication with WAN 104. Numerous additional servers, computing devices, handheld devices, personal digital assistants, telephones and other devices can also be connected to WAN 104. [0030] In some examples, a mobile network card 150 may be used to connect to LAN 102 and/or WAN 104; and a mobile network card may be configured to connect to LAN 102 and/or WAN 104 via a mobile telephone network, in a conventional manner.
[0031] The operation of computing device 100 and server 144 can be controlled by computing device-executable instructions stored on a non-transient computing device- readable medium. For example, computing device 100 may include computing device-executable instructions stored on a memory for transmitting information to server 144, receiving information from server 144, and displaying the received information on display device 132. Furthermore, server 144 can include (stored on a memory computing device) executable instructions for receiving requests from computing device 100, and processing data and transmitting data going to computing device 100. In some embodiments, server 144 transmits hypertext markup language (HTML) and extensible markup language (XML) formatted data, to computing device 100.
[0032] As noted above, the term "network" as used herein and depicted in the drawings should be broadly interpreted to include not only systems in which remote storage devices are coupled together via one or more communication paths, but also stand-alone devices that may be coupled, from time to time, to such systems that have storage capability. Consequently, the term "network" includes not only a "physical network" 102 and 104, but also a "content network," which is comprised of the data- attributable to a single entity— which resides across all physical networks.
[0033] Methods of the present technology can include using the computing device 100 to provide a catastrophe loss model including loss model data, which can include both exposure data and event loss data. Methods can include the computing device providing a plurality of loss parameters, with each loss parameter having a variable adjustment factor set to a default value. The plurality of loss parameters can be provided, by a parameter adjustment menu screen. Loss parameters can include severity parameters and frequency parameters.
[0034] Figure 2 illustrates one example of a parameter adjustment menu screen 200 that can be provided to a user by computing device 100 to allow a user to adjust loss severity parameters 202 and frequency parameters 204. As illustrated, the numerical value 206 for each adjustment factor presented for each parameter is set to a default value of 1.0. Because the numerical value for each adjustment factor in this example is a scalar multiplier, the default value of 1.0 indicates no change. In order to modify the output of the catastrophe model, the user can modify the value of at least one adjustment factor for at least one adjustment parameter to create a modified adjustment parameter. A user can input modified values for one or more of the adjustment factors, as desired.
[0035] The computing device 100 can receive the modified value, or values, input by a user, and can apply the modified adjustment parameter to the loss model data to generate adjusted loss model data. When the loss parameter for which a modified adjustment factor is received is a severity factor, the modified adjustment factor can be applied to the exposure data. Similarly, when the loss parameter for which a modified adjustment factor is received is a frequency factor, the modified adjustment factor can be applied to the event loss data. When multiple modified adjustment parameters are received, each modified adjustment factor for a given severity factor can be applied to the exposure data, and each modified adjustment factor for a given frequency factor can be applied to the event loss data.
[0036] Once any modified adjustment parameters have been applied to the loss model data, the computing device 100 can compute at least one of gross or net losses based on the adjusted loss model data.
EXAMPLES OF THE PRESENT TECHNOLOGY
[0037] Non-limiting examples compatible with certain embodiments described herein are now provided. The examples are given by way of illustration, and are not intended to limit the disclosure herein.
[0038] Example 1 - Hurricane Peril Notional Results
[0039] In this example, a fictional portfolio of properties is assessed. FIGURE 3 shows properties in coastal states ranging from Texas to Maine that are part of the fictional portfolio. The portfolio includes structures and contents coverages. The structures in the portfolio include a mix of masonry, wood frame, and unknown construction. The age of the structures varies. The insurance includes a 5% coverage deductible and includes 50% facultative placement.
[0040] The first and second tables provided below in TABLE A show values (amounts listed in dollars) respectively before and after application of a construction type factor adjustment in the amount of 1.20x. The notional results provided below relate to various hurricanes and show the total losses based on application of the ground up adjustment factor. The net treaty column reflects the value of any other form of insurance such as reinsurance.
TABLE A
Event Name Ground Up Gross Net Treaty
CHARLEY 2004 29,448,000 18,217,000 17,195,000
JEANNE 2004 33,483,000 15,994,000 15,951,000
IVAN 2004 22,050,000 13,658,000 13,358,000
KATRI A 2005 53,467,000 31,400,000 30,971,000
RITA 20O5 22,858,000 14,438,000 14,159,000
WILMA 2005 65,831,000 36,796,000 36,640,000
GUSTAV 2008 8,703,000 4,895,000 4,838,000
IKE 2008 41,793,000 23,096,000 22,878,000
HUGO 1989 47,715,000 23,409,000 23,261,000
ANDREW 1992 263,931,000 210,534,000 188,225,000
Event Name Ground Up Gross Net Treaty Ground Up Gross Net Treaty
CHARLEY 2004 35,338,000 23,348,000 21,513,000 1.200 1.282 1.251
JEANNE 2004 40,179,000 21,196,000 21,026,000 1.200 1.325 1.318
IVAN 2004 26,460,000 17,520,000 16,764,000 1.200 1.283 1.255
DENNIS 2005 5,752,000 3,453,000 3,393,000 1.200 1.306 1.294
KATRINA 2005 64,161,000 40,517,000 39,286,000 1.200 1.290 1.268
RITA 2005 27,430,000 18,490,000 17,760,000 1.200 1.281 1.254
WILMA 2005 78,997,000 48,070,000 47,482,000 1.200 1.306 1.296
GUSTAV 2008 10,443,000 6,346,000 6,180,000 1.200 1.296 1.277
IKE 2008 50,152,000 30,049,000 29,391,000 1.200 1.301 1.285
HUGO 1989 57,258,000 30,860,000 30,397,000 1.200 1.318 1.307
ANDREW 1992 316,717,000 261,543,000 221,301,000 1.200 1.242 1.176
[0041] FIGURE 4 provides a map of Florida to depict the various regions of varying wind gusts as measured in meters/second and the related hurricane wind peril that were experienced during Hurricane Jeanne. The wind speed allows for simulation of losses. This map shows the importance of accurately assessing risk based on information on specific locations. [0042] FIGURES 5-6 show two graphs that depicting net losses respectively for a moderate hazard and for a small hazard, which is just above the deductible. Together, these two graphs show that while absolute differences are larger for high-hazard regions, proportional changes to net losses are relatively higher in small-hazard regions, where resulting losses are only marginally above the deductible hurdle. The graphs also show that marginal losses occurring on the outer boundary of the hazard region may affect a large area of additional net losses.
[0043] FIGURE 7 provides a map of Florida to depict the track of Hurricane Jeanne and the associated average losses in various regions of Florida. The data related to the map allows for a comparison of original losses vs. adjusted losses after parameter adjustment. In this example, ground up adjustment to construction was set to a uniform 1.2x for all regions and types. This example indicated that gross losses were about 1.325x on average. Note that loss effects are not uniform spatially across the wind hazard region. In particular, along the central track, relative changes were lower; and along the peripheral regions where the wind speeds were lower, the relative changes were higher.
[0044] In FIGURE 2, the screen shot shows the use of an adjustment factor based on the construction type. The adjustment factor can be further adjusted based on the location, using data for the Impact Forecasting regions. The table below, TABLE B, provides an example of the combined calculation based on construction type including masonry (MAS), wood frame (WD) and unknown (UNK); and location including Florida (FL), Southeastern states (SE), and Gulf states (Gulf). The data in the table below provides a stochastic analysis that shows before and after adjustments, with breakouts for construction type and regions.
TABLE B
Const Region Factor Adj Ground Up Gross Net Treaty
MAS FL 1.00 3,356,000 2,079,000 1,999,000
UNK FL 1.00 2,998,000 1,804,000 1,758,000
WD FL 1.00 12,250,000 7,880,000 7,461,000
MAS SE 1.00 524,000 299,000 293,000
UNK SE 1.00 546,000 352,000 340,000
WD SE 1.00 1,910,000 1,129,000 1,095,000
MAS NE 1.00 541,000 279,000 275,000
UNK NE 1.00 568,000 341,000 334,000
WD NE 1.00 2,217,000 1,239,000 1,212,000
MAS Gulf 1.00 1,701,000 956,000 932,000
UNK Gulf 1.00 1,714,000 1,059,000 1,023,000
WD Gulf 1.00 6,201,000 3,649,000 3,537,000
Const Region Factor Adj Ground Up Gross Net Treaty Ground Up Gross Net Treaty
MAS FL 0.95 3,188,000 1,936,000 1,873,000 0.950 0.931 0.937
UNK FL 1.10 3,298,000 2,059,000 1,983,000 1.100 1.141 1.128
WD FL 1.05 12,862,000 8,416,000 7,904,000 1.050 1.068 1.059
MAS SE 1.00 524,000 299,000 293,000 1.000 1.000 1.000
UNK SE 1.00 546,000 352,000 340,000 l.OOO 1.000 1.000
WD SE 1.00 1,910,000 1,129,000 1,095,000 1.000 1.000 1.000
MAS NE 1.00 541,000 279,000 275,000 1.000 1.000 1.000
UNK NE 1.00 568,000 341,000 334,000 1.000 1.000 1.000
WD NE 1.00 2,217,000 1,239,000 1,212,000 1.000 1.000 1.000
MAS Gulf 1.00 1,701,000 956,000 932,000 1.000 1.000 1.000
UNK Gulf 1.15 1,971,000 1,279,000 1,211,000 1.150 1.208 1.184
WD Gulf 1.10 6,822,000 4,170,000 4,004,000 1.100 1.143 1.132
[0045] The tables below, under the heading of TABLE C, provides a stochastic analysis of the portfolio and blends the construction types and regions together then shows notional results before and after applying the ground up adjustment factor. In addition, when assessing the storm severity to calculate the factor, the frequency is also included in the factor.
TABLE C
Return Ground Up Gross Net Treaty
1000 741,973,000 676,383,000 546,789,000
500 550,493,000 452,149,000 407,751,000
250 469,266,000 373,921,000 349,535,000
100 325,066,000 243,488,000 230,832,000
50 227,083,000 158,190,000 151,695,000
20 117,848,000 77,006,000 74,052,000
10 67,560,000 41,515,000 40,343,000
AAL 34,526,000 21,066,000 20,258,000
STDDEV 70,949,000 52,976,000 48,543,000
Return Ground Up Gross Net Treaty Return Ground Up Gross Net Treaty
1000 815,498,000 709,785,000 561,482,000 1000 1.099 1.049 1.027
500 573,880,000 473,529,000 420,156,000 500 1.042 1.047 1.030
250 487,913,000 391,114,000 361,615,000 250 1.040 1.046 1,035
100 348,063,000 262,769,000 244,131,000 100 1.071 1.079 1.058
50 236,130,000 165,791,000 157,690,000 50 1.040 1.048 1.040
20 124,357,000 81,864,000 77,868,000 20 1.055 1.063 1.052
10 70,554,000 43,934,000 42,561,000 10 1.044 1.058 1.055
AAL 36,147,000 22,455,000 21,456,000 AAL 1.047 1.066 1.059
STDDEV 73,923,000 55,761,000 50,560,000 STDDEV 1.042 1.053 1.042
[0046] As discussed, FIGURE 2 illustrates one example of a parameter adjustment menu screen 200 that can be provided to a user by computing device 100 to allow a user to adjust frequency parameters 204. In this example, the frequency-related parameter relates to the storm category, for instance a Category 3, 4 or 5, for a particular region, such as Florida, the Gulf states, the Southeast states, or the Northeast states.
[0047] The tables below, under the heading of TABLE D, show the notional results before and after application of the ground up parameter adjustment factor.
TABLE D
Return Ground Up Gross Net Treaty
1000 741,973,000 676,383,000 546,789,000
500 550,493,000 452,149,000 407,751,000
250 469,266,000 373,921,000 349,535,000
100 325,066,000 243,488,000 230,832,000
50 227,083,000 158,190,000 151,695,000
20 117,848,000 77,006,000 74,052,000
10 67,560,000 41,515,000 40,343,000
AAL 34,526,000 21,066,000 20,258,000
STDDEV 70,949,000 52,976,000 48,543,000
Return Ground Up Gross Net Treaty Return Ground U Gross Net Treaty
1000 813,380,000 712,547,000 590,633,000 1000 1.096 1.053 1.080
500 553,576,000 467,166,000 411,102,000 500 1.006 1.033 1.008
250 478,524,000 380,063,000 350,887,000 250 1.020 1.016 1.004
100 356,641,000 256,458,000 248,517,000 100 1.097 1.053 1.077
50 237,078,000 166,671,000 159,537,000 50 1.044 1.054 1.052
20 126,262,000 81,780,000 78,922,000 20 1.071 1.062 1.066
10 71,284,000 43,439,000 42,271,000 10 1.055 1.046 1.048
AAL 36,123,000 22,161,000 21,295,000 AAL 1.046 1.052 1.051
STDDEV 73,662,000 55,134,000 50,486,000 STDDEV 1.038 1.041 1.040
[0048] Example 2 - Earthquake Peril Notional Results
[0049] In Figure 8, a fictional portfolio of properties is assessed, properties in Western states including California, Oregon, and Washington that are part of the fictional portfolio. The portfolio includes structures and contents coverages. The structures in the portfolio include a mix of masonry, wood frame, and unknown construction. The age of the structures varies. The insurance includes a 5% coverage deductible and includes 50% facultative placement.
[0050] The first and second tables provided below, under the heading of TABLE E, show values respectively before and after application of a construction type factor adjustment in the amount of 1.20x. The notional results provided below relate to various earthquakes and show the total losses based on application of the ground up adjustment factor. The net treaty column reflects the value of any other form of insurance, such as reinsurance. TABLE E
[0051] FIGURE 9 provides a map of Southern California to depict the various regions of earthquake shake peril and peak ground acceleration. Specifically, the map depicts the USGS Great Southern California ShakeOut scenario, based on a magnitude 7.8 earthquake along the San Andres fault. This map shows the importance of parameter adjustment factors accurately assessing risk based on information on specific locations.
[0052] FIGURE 10 provides a map of Southern California to depict the path of USGS Great Southern California ShakeOut scenario along the San Andres fault. The data related to the map allows for a comparison of original losses vs. adjusted losses after parameter adjustment. In this example, ground up parameter adjustment factors to construction was set to a uniform 1.2x for all regions and types. This example indicated that gross losses were about 1.374x, on average. Note that loss effects are not uniform spatially across the earthquake hazard region. In particular, along the near-fault regions, relative changes were lower and along the far-field regions, (lower PGA) the relative changes were higher.
[0053] In FIGURE 2, the screen shot shows the use of an adjustment factor based on the construction type. The peril field can be moved from Hurricane in Example 1 to Earthquake to yield the screen shot shown in FIG. 11. The parameter adjustment factor can then be further adjusted based on the location using data for Impact Forecasting regions. The table below provides a stochastic analysis with a combined calculation based on construction type including reinforced concrete (RC), wood frame (WD) and unknown (UNK); and location including California (CA) and Pacific Northwest (PNW). The data in the tables below, under the heading of TABLE F, shows before and after adjustments with breakouts for construction type and regions.
TABLE F
Region Const Factor Adj Ground Up Gross Net Treaty
CA RC 1.00 6,537,000 1,840,000 1,751,000
CA UNK 1.00 5,848,000 1,303,000 1,260,000
CA WD 1.00 24,712,000 5,260,000 5,096,000
PNW RC 1.00 3,637,000 2,085,000 1,989,000
PNW UNK 1.00 2,503,000 1,135,000 1,099,000
PNW WD 1.00 9,533,000 4,000,000 3,890,000
Region Const Factor Adj Ground Up Gross Net Treaty Ground Up Gross Net Treaty
CA RC 1.05 6,864,000 2,021,000 1,914,000 1.050 1.098 1.093
CA UNK 1.20 7,017,000 1,836,000 1,733,000 1.200 1.409 1.375
CA WD 0.85 21,005,000 3,790,000 3,737,000 0.850 0.721 0.733
PNW RC 1.10 4,001,000 2,385,000 2,249,000 1.100 1.144 1.131
PNW UNK 1.25 3,128,000 1,596,000 1,514,000 1.250 1.406 1.378
PNW WD 0.90 8,580,000 3,367,000 3,302,000 0.900 0.842 0.849
[0054] The tables below, under the heading of TABLE G, takes the portfolio and blends the construction types and regions together then shows notional results before and after applying the ground up adjustment factor.
TABLE G
Return Ground Up Gross Net Treaty
1000 800,600,000 394,900,000 373,900,000
500 686,000,000 340,300,000 331,200,000
250 543,100,000 264,500,000 254,300,000
100 425,600,000 185,700,000 180,100,000
50 336,400,000 127,100,000 123,700,000
20 221,400,000 77,700,000 74,700,000
10 138,500,000 42,400,000 41,000,000
AAL 52,800,000 15,600,000 15,100,000
STDDEV 102,700,000 41,400,000 39,800,000
Return Ground Up Gross Net Treaty Return Ground Up Gross Net Treaty
1000 779,600,000 368,800,000 350,000,000 1000 0.974 0.934 0.936
500 655,300,000 321,200,000 306,200,000 500 0.955 0.944 0.925
250 513,600,000 250,600,000 237,500,000 250 0.946 0.947 0.934
100 405,200,000 179,300,000 171,900,000 100 0.952 0.966 0.954
50 319,800,000 123,100,000 118,800,000 50 0.951 0.969 0.960
20 212,800,000 75,600,000 73,200,000 20 0.961 0.973 0.980
10 133,700,000 41,300,000 39,900,000 10 0.965 0.974 0.973
AAL 50,600,000 15,000,000 14,400,000 AAL 0.958 0.962 0.954
STDDEV 98,600,000 40,100,000 38,400,000 STDDEV 0.960 0.969 0.965
[0055] As discussed above, FIGURE 11 illustrates one example of a parameter adjustment menu screen 200 shown in FIGURE 2, that can be provided to a user by computing device 100, to allow a user to adjust the frequency parameters. In this example, the parameter adjustment factor relates to the occurrence rate. The adjustment factor also includes the earthquake magnitude for a particular region including California (CA) and Pacific Northwest (PNW).
[0056] The tables below, under the heading of TABLE H, shows the notional results before and after application of the ground up adjustment factor for a magnitude 7.50 earthquake. TABLE H
Return Ground Up Gross Net Treaty
1000 779,600,000 368,800,000 350,000,000
500 655,300,000 321,200,000 306,200,000
250 513,600,000 250,600,000 237,500,000
100 405,200,000 179,300,000 171,900,000
50 319,800,000 123,100,000 118,800,000
20 212,800,000 75,600,000 73,200,000
10 133,700,000 41,300,000 39,900,000
AAL 50,600,000 15,000,000 14,400,000
STDDEV 98,600,000 40,100,000 38,400,000
Return Ground Up Gross Net Treaty Return Ground Up Gross Net Treaty
1000 781,100,000 388,200,000 365,100,000 1000 1.002 1.053 1.043
500 683,300,000 335,200,000 320,400,000 500 1.043 1.044 1.046
250 544,600,000 268,100,000 255,400,000 250 1.060 1.070 1.075
100 432,300,000 186,500,000 180,100,000 100 1.067 1.040 1.048
50 334,700,000 126,900,000 123,000,000 50 1.047 1.031 1.035
20 220,500,000 78,300,000 75,600,000 20 1.036 1.036 1.033
10 142,500,000 42,200,000 40,800,000 10 1.066 1.022 1.023
AAL 52,700,000 15,700,000 15,100,000 AAL 1.042 1.047 1.049
STDDEV 102,300,000 41,700,000 39,900,000 STDDEV 1.038 1.040 1.039
[0057] As shown above in Examples 1 and 2, the use of a parameter adjustment for frequency changes allows the user to adjust the annual event frequencies by region and event strength (either hurricane category or earthquake magnitude). Selected events that match the criteria can be updated. The resulting Probable Maximum Losses can be recalculated using new updated event records, which also applies to breakouts, such as state or county loss results. Frequency changes would have no effect on historical or forecast scenarios.
[0058] As shown above in Examples 1 and 2, the use of a parameter adjustment for severity changes also allows the user to adjust the ground up loss behavior by a given factor. Adjustment factors can be either based on model-miss results or user judgment for sensitivity analysis. Additionally, ground up losses can be multiplied by a given scalar (either up or down) and losses can be propagated through the account structure in the loss model. Losses that are net of insurance applications (deductibles, limits, reinsurance, etc.) may have a more pronounced effect than the ground up factor. Net of insurance effects are larger on relatively smaller loss regions (far- field/outer boundary conditions).
[0059] Finally, Examples 1 and 2 show that parameter adjustment to both frequency and severity, can be made on any given analysis. The resulting analysis provides a unique functionality over and above changes made to the Event Loss table modification.
[0060] From the foregoing, it will be appreciated that although specific examples have been described herein for purposes of illustration, various modifications may be made without deviating from the spirit or scope of this disclosure. It is therefore intended that the foregoing detailed description be regarded as illustrative rather than limiting, and that it be understood that it is the following claims, including all equivalents, that are intended to particularly point out and distinctly claim the claimed subject matter.

Claims

CLAIMS What is claimed is:
1. A method of adjusting losses calculated by a catastrophe loss model by adjusting loss parameters, comprising steps of: providing by a computing device a catastrophe loss model including loss model data; providing by the computing device a plurality of loss parameters, each loss parameter having a variable adjustment factor set to a default value; receiving at the computing device a modified value input by a user for at least one adjustment factor to create a modified adjustment parameter; applying the modified adjustment parameter at the computing device to the loss model data to generate adjusted loss model data; and computing at the computing device at least one of gross or net losses based on the adjusted loss model data.
2. The method of claim 1, wherein the loss parameters comprise severity parameters and frequency parameters.
3. The method of claim 1, wherein the loss model data includes exposure data and event loss data.
4. The method of claim 1, wherein: when the loss parameter for which a modified adjustment factor is received is a severity factor, the modified adjustment factor is applied to initial loss estimate data; and when the loss parameter for which a modified adjustment factor is received is a frequency factor, the modified adjustment factor is applied to event loss data.
5. The method of claim 1 , wherein the value for each adjustment factor is a scalar multiplier.
6. The method of claim 2, wherein the severity parameters include at least one parameter selected form the group consisting of all portfolio records, construction classification, line of business, building age, region, and building size.
7. The method of claim 2, wherein the frequency parameters include at least one parameter selected form the group consisting of all even frequencies, region, and event magnitude.
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