Patent Issued for Methods and systems for continuous risk monitoring and dynamic underwriting pricing (USPTO 11651437): Virtual i Switzerland AG

2023 JUN 01 (NewsRx) -- By a News Reporter-Staff News Editor at Insurance Daily News -- From Alexandria, Virginia, NewsRx journalists report that a patent by the inventor Geylani, Veysel Sinan (Ras Al Khaimah, United Arab Emirates), filed on July 16, 2018, was published online on May 16, 2023.

The patent’s assignee for patent number 11651437 is Virtual i Switzerland AG (Zurich, Switzerland).

News editors obtained the following quote from the background information supplied by the inventors: “This specification relates to insurance risk monitoring and dynamic underwriting pricing.

“Current insurance practices for risk monitoring/surveying involve discrete and/or manual data collection processes, mainly involving written report and pictures. These surveys generally occur once a year before a policy is renewed. Thereafter, both the insurance company and the insured party have limited monitoring capability of the risks. While the risk is a continuous stochastic process, the risk transfer and monitoring is a discrete process. This approach limits the adequate and fair risk transfer between both the insurer and the insured, and both parties can benefit in the long term from better insurance risk monitoring.”

As a supplement to the background information on this patent, NewsRx correspondents also obtained the inventor’s summary information for this patent: “The methods and systems disclosed herein related to continuous risk monitoring and dynamic underwriting system for insurers. This system incorporates an interactive platform not only for insurance companies but also for other stake holders in the insurance ecosystem-clients, loss adjusters, risk engineering, brokers, agencies, captives and third party developers. The methods and systems disclosed herein allow continuous risk monitoring of insured risks and allows the underwriting premium to be reasonably calculated for each risk.

“An insurance contract is between the insurer and insured (i.e., client). Insurance policies most of the cases are issued based on the insured declaration. Generally, insurance companies or intermediaries (brokers/agents) send internal or third party risk surveyors to the client’s premise to survey the risk. Relying on the insured party’s declaration may create a potential moral hazard and asymmetric information problem. Moral hazard refers to the lack of incentive to guard against risk when the insured is protected from its consequences. In the event of a loss, settlement of an insurance claim generally takes time because of the inherent asymmetric information and moral hazard concerns from the perspective of the insurance companies. To mitigate this moral hazard problem, insurance companies rely on uncorrelated and non-aggregation concept of big numbers, significant reinsurance, and retrocession agreements.

“Another significant issue is the pricing of each risk. There are various actuarial models to calculate the adequate or technical premium for individual risks. However, considering the limitation in available data and potential complexity of the risks, generalized linear models and other conventional models may not offer accurate pricing for the risks. The premium calculation for particular risk is divided into sub categories. First of all, the overall portfolio includes experienced or estimated insurance claim cost, loss adjustment costs, transactional costs (intermediary fees/commission) and general operational expenses. In this approach, the premium is calculated from the portfolio level to an individual risk. The adjustments on premiums are done for each specific risk. Deviation option from the technical pricing can reach up to 80% level, which can challenge the validity of the technical pricing concept for the risk.

“Since the portfolio based pricing models may not provide an accurate premium pricing for the individual risk, insurance companies need a decision making process for risk selection. To solve this issue and make the adjustments on the model generated premiums insurance, insurers may employ underwriters. However, these adjustment decisions are exposed to standard principal agent problems, and there is no significant downside (lack of legal proceedings, lack of close monitoring of the track record of individual underwrites, etc.) of the decisions for underwriters. Therefore, in the long term, the benefit of human/underwriter’s decision for insurance risks may provide only limited added value to insurance companies to find the accurate premium per risk. The technological infrastructure related limitations can also limit accurate risk pricing. Instead, the premium calculation is automatically adjusted by supply and demand, together with the market historical loss experiences in specific lines of business. This supply and demand equilibrium may not provide a particular risk premium estimate for a specific risk.

“The methods and systems disclosed herein account for the incentives insurance companies have in considering the amount of potential paid losses. The disclosed system can include independent third party developers, scientists and contractors to capture data related to the underwriting and claims. The methods and system can avoid the problem of the insured parties not wishing to purchase assets or use hardware equipment and software to capture the data for assessing individual risk.”

The claims supplied by the inventors are:

“1. A system comprising: a premise neural sensor network (PNSN) system comprising a plurality of sensors installed at an industrial facility to obtain raw PNSN data associated with the operation and/or a structural asset of the industrial facility, wherein the plurality of sensors comprise: (i) internal monitoring sensors inside the industrial facility, and (ii) external monitoring sensors outside the industrial facility, wherein the PNSN system is configured to continuously obtain the raw PNSN data from the plurality of sensors and provide the raw PNSN data for monitoring the industrial facility, and wherein the plurality of sensors are configured to generate raw PNSN data relating to one or more of odor, pressure, height, movement, displacement, and gas content at the industrial facility; a ground mobile data capturing unit (GMDCU) configured to obtain raw GMDCU data of the structural asset at the insured industrial facility; a flying mobile data capture unit (FDCU) configured to capture raw FDCU data of the structural asset at the insured industrial facility; and a dynamic risk pricing and continuous monitoring system at a location remote from the industrial facility comprising one or more processors, the one or more processors being programmed according to a dynamic pricing algorithm, the dynamic risk pricing and continuous monitoring system being configured to: continuously receive an input derived from at least one of the raw PNSN data, the raw GMDCU data, and the raw FDCU data; simulate, using the one or more processors, an incident at the industrial facility, the incident corresponding to damage to the structural asset and/or a disruption to the operation of the industrial facility; continuously update, using the one or more processors, a value for a risk premium for the industrial facility based on the simulation and the continuously received input according to the dynamic pricing algorithm, wherein the dynamic risk pricing and continuous monitoring system is further configured to generate a daily risk premium based on the continuously updated value for the risk premium for the industrial facility; output information based on the daily risk premium by outputting a graphical representation to a display device; perform automated data analytics and output a probable maximum property and business interruption loss scenarios and premises based casualty exposures; and determine that an abnormal situation exists at the industrial facility based on the continuously received input and initiate an alarm.

“2. The system of claim 1, wherein the graphical representation comprises an augmented reality or virtual reality graphical representation.

“3. The system of claim 1, wherein the dynamic risk pricing and continuous monitoring system is further configured to determine a probable maximum loss based on the simulated incident.

“4. The system of claim 1, wherein the dynamic risk pricing and continuous monitoring system further comprises a database library.

“5. The system of claim 1, wherein the dynamic risk pricing and continuous monitoring system further comprises an interface configured to interact with an external developer’s platform.

“6. The system of claim 1, wherein the plurality of sensors of the PNSN system comprise an accelerometer or a gyroscope.

“7. The system of claim 1, wherein the plurality of sensors of the PNSN system are configured to capture visual data of the structural asset.

“8. The system of claim 7, wherein the dynamic risk pricing and continuous monitoring system is further programmed to convert the visual data into a measurement of dimensions of the structural asset.”

For additional information on this patent, see: Geylani, Veysel Sinan. Methods and systems for continuous risk monitoring and dynamic underwriting pricing. U.S. Patent Number 11651437, filed July 16, 2018, and published online on May 16, 2023. Patent URL (for desktop use only): https://ppubs.uspto.gov/pubwebapp/external.html?q=(11651437)&db=USPAT&type=ids

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