FIELD OF INVENTION

This disclosure generally relates to detecting damage, loss, and/or other conditions associated with an automobile and human passengers, operators, and/or pedestrians to determine risk levels for insurance to better and/or more efficiently match price to risk.

BACKGROUND

As computer and computer networking technology has become less expensive and more widespread, more and more devices have started to incorporate digital “smart” functionalities. For example, controls and sensors capable of interfacing with a network may now be incorporated into devices such as vehicles and/or traffic control systems. Furthermore, it is possible for one or more vehicle and/or central controllers to interface with the smart devices or sensors.

However, conventional systems may not be able to automatically detect and characterize various conditions or damage associated with a vehicle or building. Additionally, conventional systems may not be able to detect or sufficiently identify and describe damage that is hidden from human view, and that typically has to be characterized by explicit human physical exploration, extent and range of electrical malfunctions, etc. Conventional systems further may not be able to formulate precise characterizations of loss without including unconscious biases, and may not be able to equally weight all historical data in determining loss mitigation factors.

BRIEF SUMMARY

The present disclosure generally relates to systems and methods for detecting damage, loss, and/or other conditions associated with a vehicle using a computer system and/or a building, land, structure, or other real property using a property monitoring system. Embodiments of exemplary systems and computer-implemented methods are summarized below. The methods and systems summarized below may include additional, less, or alternate components, functionality, and/or actions, including those discussed elsewhere herein.

In one aspect, the present embodiments may relate to determining an automobile-based risk level via one or more processors, training a neural network to identify risk factors that are predictive electronic claim features, receiving information corresponding to (i) an automobile, and/or (ii) an automobile operator, analyzing the information using the trained neural network to generate one or more risk indicators, determining, by analyzing the risk indicators, a risk level corresponding to the automobile, and/or displaying, to a user, an insurance quotation based upon analyzing the risk indicators. The automobile may be a smart, autonomous, or semi-autonomous vehicle, and have sensors, software, and electronic components that direct autonomous or semi-autonomous vehicle features or technologies—each of which may have a various levels of risk, or lack thereof, that may be analyzed and determined by the present embodiments. Systems and methods may automatically generate risk models for various types of vehicle insurance types and loss types, such as by the application of artificial intelligence and machine learning methods as disclosed herein, to provide more granular risk models, leading to more accurate commercial offerings, and more appropriate matching premium price to actual risk.

In another aspect, a computer-implemented method of determining an automobile-based risk level via one or more processors may include training, via one or more processors, a neural network to identify risk factors that are predictive of electronic vehicle claim records. The neural network may include a plurality of layers, and an input layer from among the plurality of layers may include a plurality of input parameters—with each corresponding to a different claim attribute. The method may include, via one or more processors, receiving information corresponding to (i) an automobile, and/or (ii) an automobile operator; and analyzing the information using the trained neural network. Analyzing the information may include generating, within the plurality of layers, one or more risk indicators corresponding to the information. The method may also include determining a risk level corresponding to the vehicle. The method may include additional, less, or alternate actions, including those discussed elsewhere herein.

In another aspect, a computing system may include one or more processors, and one or more memories storing instructions. When the instructions are executed by the one or more processors, they may cause the computing system to provide a first application to a user of a client computing device. The first application, when executing on the client computing device, may cause the client computing device to obtain a set of information from an input device of the client computing device, and transmit, via a communication network interface of the client computing device, the set of information to a remote computing system. The instructions may cause the computing system to receive, at the remote computing system, the set of information and process, at the remote computing system, the set of information. The instructions may cause the computing system to identify, by the remote computing system, one or more risk indications, at least in part, by applying the set of information to a trained neural network and generate, by the remote computing system analyzing the one or more risk indications, a quotation, such as quote for auto insurance. The instructions may cause the computing system to (i) display the quotation to the user, and (ii) provide the quotation as input to a second application. The system may include additional, less, or alternate functionality, including that discussed elsewhere herein.

Advantages will become more apparent to those skilled in the art from the following description of the preferred embodiments which have been shown and described by way of illustration. As will be realized, the present embodiments may be capable of other and different embodiments, and their details are capable of modification in various respects. Accordingly, the drawings and description are to be regarded as illustrative in nature and not as restrictive.

