FIELD OF THE DISCLOSURE

This disclosure relates to systems and methods for providing artificial intelligence (AI)-based estimates.

BACKGROUND

A typical process for insurance claims adjustment may often begin with an insured individual contacting their insurance carrier with a description of damage due to a covered occurrence. A representative of the insurance carrier takes note of the described damage and sends a contracted repair person and often a claims adjuster to estimate the cost of the damage and perform any necessary repairs. The insured individual will typically provide some evidence of the type, scope, and extent of the damage through images and video during the initial contact. Corroboration is requested from the contracted repair person or performed by the adjuster, also taking the form of images and video. This in-person corroboration often comprises identification of damaged contents and structures, measurements of damaged areas, and the computation of repair and replacements costs. Once the necessary repair work is completed, the insurance company will cover the costs to the insured.

Because of the heavy reliance on in-person visits to the insured's location to evaluate the damage, the current process to identify damages does not scale well. In certain circumstances, like catastrophic hurricanes and floods, there may be a surge of insurance claims filed in a short period of time. This overload can result in a backlog that delays the repair or replacement of many insured individuals' properties, often escalating the impact of the catastrophic event. These and other drawbacks exist.

SUMMARY

Some aspects of the present disclosure relate to a method for identifying damage in an indoor environment by analyzing a virtual model of the indoor environment. The method comprises obtaining a virtual representation of a physical scene in an indoor environment; extracting data items from the virtual representation, wherein the data items correspond to contents in the physical scene; determining a reference line in the virtual representation indicative of a vertical extent of the damage in the physical scene; and determining content damage information based on the reference line, and indicative of the contents damaged in the physical scene.

Such a method may be used for evaluating flood insurance claims utilizing a machine learning model, for example, and/or other uses. In some embodiments, one or more hardware processors may be configured by machine-readable instructions to receive description data of a location. The description data may be generated via at least one of a camera, a user interface, or an environment sensor. The one or more hardware processors may be configured to, utilizing the machine learning model, generate an inventory list comprising a plurality of items in or around the location based on the description data. The inventory list may further comprise inventory attributes associated with the plurality of items related to positions of the plurality of items in the location. The one or more hardware processors may be configured to, utilizing the machine learning model, determine, based on the description data, a flood line level for a flood previously present at the location. The one or more hardware processors may be configured to, utilizing the machine learning model, project a virtual flood plane in or around the location based on the flood line level. The one or more hardware processors may be configured to, utilizing the machine learning model, determine, based on the positions of the items in the location and the flood line level, which individual items in the inventory list were likely to sustain flood damage, wherein determining which individual items in the inventory list were likely to sustain flood damage comprises comparing the positions of the plurality of items in the location to the virtual flood plane projection.

Other aspects of the present disclosure relate to systems and methods for providing insurance underwriting estimates utilizing a machine learning model. One or more hardware processors may be configured by machine-readable instructions such that description data of a location may be received. The description data may be generated via at least one of a camera, a user interface, an environment sensor, and an external location information database. An inventory list comprising a plurality of items in or around the location may be generated utilizing the machine learning model, based on the description data. The inventory list may further comprise inventory attributes associated with the plurality of items related to a context of the plurality of items in the location. Interior or exterior condition information may be determined, utilizing the machine learning model, based on the description data, for the location. The interior or exterior condition information may describe a quality of structural components of the location or hazards in or around the location. One or more insurance underwriting estimates may be generated, utilizing the machine learning model, based on the inventory list and the interior or exterior condition information, for insuring the location.

Other aspects of the present disclosure relate to systems and methods for estimating dimensions of items in or around a location utilizing a machine learning model. One or more hardware processors may be configured by machine-readable instructions to receive description data of a location. The description data may be generated via at least one of a camera, a user interface, an environment sensor, or an external location information database. The one or more hardware processors may be configured to generate, utilizing the machine learning model, an inventory list comprising a plurality of items in or around the location based on the description data. The one or more hardware processors may be configured to receive one or more dimensions for a reference item. The reference item may be included in the plurality of items in the inventory list. The one or more hardware processors may be configured to determine, utilizing the machine learning model, based on the one or more dimensions of the reference item, dimensions and relative positions of other items in the plurality of items.

Other aspects of the present disclosure relate to systems and methods for processing insurance claims utilizing a machine learning model. One or more hardware processors may be configured by machine-readable instructions to receive description data of a location. The description data may be generated via at least one of a camera, a user interface, an environment sensor, and an external location information database. The one or more hardware processors may be configured to generate an inventory list comprising a plurality of items in or around the location, utilizing the machine learning model, based on the description data. The inventory list may further comprise inventory attributes associated with the plurality of items related to a context of the plurality of items in the location. The one or more hardware processors may be configured to determine, utilizing the machine learning model, based on the description data, interior or exterior condition information for the location. The interior or exterior condition information may describe a quality of structural components of the location or hazards in or around the location. The one or more hardware processors may be configured to receive a request for an insurance payment related to one or more items of the plurality of items in the inventory list or related to the location. The one or more hardware processors may be configured to process, utilizing the machine learning model, based on the inventory list, the interior or exterior condition information, and the request for the insurance payment, payment for one or more items of the plurality of items in the inventory list or the location.

Various other aspects, features, and advantages of the invention will be apparent through the detailed description of the invention and the drawings attached hereto. It is also to be understood that both the foregoing general description and the following detailed description are examples, and not restrictive of the scope of the invention. As used in the specification and in the claims, the singular forms of “a,” “an,” and “the” include plural referents unless the context clearly dictates otherwise. In addition, as used in the specification and the claims, the term “or” means “and/or” unless the context clearly dictates otherwise. Additionally, as used in the specification “a portion,” refers to a part of, or the entirety of (i.e., the entire portion), a given item (e.g., data) unless the context clearly dictates otherwise.

