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1.发明申请公开(公告)号：US20210256615A1
公开(公告)日：2021-08-19
申请号：US16136365
申请日：2018-09-20
摘要： Techniques for implementing machine learning for insurance loss mitigation or prevention, and claims handling are disclosed. In some scenarios, the insurance loss mitigation and claims handling may be associated with a disability, worker's compensation, life or health insurance policy, and the machine-learning analytics model may be trained in accordance with data that is relevant to identifying appropriate predictions in accordance with these particular types of insurance products. For instance, the machine-learning analytics model may utilize information within a dynamic data set as training data, which may include electronically accessible information. The machine-learning analytics model may additionally be implemented to identify various predictions that are indicative of a risk of insuring an individual as well as one or more actions that, when performed, may reduce the initial calculation of risk.
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公开(公告)号：US11783422B1
公开(公告)日：2023-10-10
申请号：US16136387
申请日：2018-09-20
摘要： Techniques for implementing machine learning to improve claim handling are disclosed. In some scenarios, the machine-learning, analytics model may be trained in accordance with data that is relevant to insurance products, such as life and health insurance. A set of labeled historical claims each corresponding to a settlement amount may be analyzed to train an artificial neural network. A claim may be received from a user mobile device, and may be analyzed using the trained artificial neural network to predict a claim settlement, which may be used to generate a settlement offer. The settlement offer may be transmitted to the user's mobile device, and if a manifestation of acceptance is received from the user, then the claim may be automatically paid.
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公开(公告)号：US20230260048A1
公开(公告)日：2023-08-17
申请号：US18139131
申请日：2023-04-25
摘要： Techniques for implementing machine learning to improve claim handling are disclosed. In some scenarios, the machine-learning, analytics model may be trained in accordance with data that is relevant to insurance products, such as life and health insurance. A set of labeled historical claims each corresponding to a settlement amount may be analyzed to train an artificial neural network, A claim may be received from a user mobile device, and may be analyzed using the trained artificial neural network to predict a claim settlement, which may be used to generate a settlement offer. The settlement offer may be transmitted to the user's mobile device, and if a manifestation of acceptance is received from the user, then the claim may be automatically paid.
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公开(公告)号：US11373249B1
公开(公告)日：2022-06-28
申请号：US16136357
申请日：2018-09-20
IPC分类号： G06Q40/08 , G06N3/08 , G06V20/00 , G06V30/194
摘要： A method of determining damage to property includes inputting historical data into a machine learning model to identify an insured type, features, and/or characteristics. The method may include identifying a peril, repair and/or replacement cost of the vehicle by analyzing a digital image from a device of an insured, the digital image depicting damage to the vehicle. The method may include inputting the digital image into the trained machine learning model to identify a type, feature, and/or characteristic of the vehicle, and may include identifying a peril, repair, and/or replacement cost associated with the vehicle. A method may include receiving and/or retrieving free-form text associated with an insurance claim and/or a vehicle, identifying at least one key word composing the free-form text, and determining based on the at least one key word a cause of loss and/or peril that caused damage to the vehicle.
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公开(公告)号：US20210256616A1
公开(公告)日：2021-08-19
申请号：US16136370
申请日：2018-09-20
摘要： A method of determining an automobile-based risk level via one or more processors includes training a machine learning program, such as a neural network, to identify risk factors within electronic claim features, receiving information corresponding to one or both of (i) an automobile, such as an autonomous or semi-autonomous vehicle, and (ii) an automobile operator, analyzing the information using the trained machine learning program 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, a quotation based upon analyzing the risk indicators. The risk factors, risk indicators, and/or risk level may be used for many purposes, such as pricing, quoting, and/or underwriting of insurance policies.
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公开(公告)号：US10943464B1
公开(公告)日：2021-03-09
申请号：US16668072
申请日：2019-10-30
摘要： Machine learning systems, methods, and techniques for detecting damage and/or other conditions associated with a building, land, structure, or other real property using a real property monitoring system are disclosed. The property monitoring system is used in conjunction with machine learning techniques to determine and/or predict various conditions associated with the real property, including particular damage thereto, e.g., based upon dynamic characteristic data obtained via on-site sensors, static characteristic data, third-party input descriptive of an event impacting the building, etc. Accordingly, damage and/or loss associated with the building/real property is more quickly and/or accurately ascertained so that suitable mitigation techniques may be applied. In some scenarios, previously undetectable or uncharacterized damage and/or other conditions may be discovered and mitigated.
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公开(公告)号：US20220284517A1
公开(公告)日：2022-09-08
申请号：US17752702
申请日：2022-05-24
IPC分类号： G06Q40/08 , G06N3/08 , G06V30/194 , G06V20/00
摘要： A method of determining damage to property includes inputting historical data into a machine learning model to identify an insured type, features, and/or characteristics. The method may include identifying a peril, repair and/or replacement cost of the vehicle by analyzing a digital image from a device of an insured, the digital image depicting damage to the vehicle, The method may include inputting the digital image into the trained machine learning model to identify a type, feature, and/or characteristic of the vehicle, and may include identifying a peril, repair, and/or replacement cost associated with the vehicle. A method may include receiving and/or retrieving free-form text associated with an insurance claim and/or a vehicle, identifying at least one key word composing the free-form text, and determining based on the at least one key word a cause of loss and/or peril that caused damage to the vehicle.
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公开(公告)号：US20210398227A1
公开(公告)日：2021-12-23
申请号：US17466722
申请日：2021-09-03
摘要： Machine learning techniques for determining a risk level of a target building or other type of real property include receiving data indicative of various historical characteristics of and/or associated with real property, and/or receiving data included in historical, electronic claims pertaining to buildings/real properties, and utilizing the received data to train a machine learning or other model that identifies or discovers risk factors associated with buildings/real properties. The machine learning or other model may be applied to characteristic data associated with the target building/real property to generate risk factors and/or risk indicators of the target building/real property. The techniques may include analyzing the generated risk factors and/or risk indicators to determine a risk level of the target building/real property. The risk factors, risk indicators, and/or risk level may be used for many purposes, such as pricing, quoting, underwriting, or re-underwriting of insurance policies.
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公开(公告)号：US10699498B1
公开(公告)日：2020-06-30
申请号：US16145958
申请日：2018-09-28
摘要： A method for attributing vehicle telematics data to individuals may include receiving vehicle telematics data collected by a data collection device during a plurality of trips. Subsets of the vehicle telematics data may correspond to different trips, and may be used to generate respective metric sets. Each metric set may include metrics indicative of different driving behaviors and/or different features of a driving environment. The method may also include retrieving, from a policy database, policy information pertaining to an insurance policy associated with the data collection device, and determining, based upon the policy information, a number of disclosed drivers associated with the insurance policy. A statistical analysis that includes executing a clustering algorithm may be performed on the metric sets, and, based upon the results, at least some of the metrics and/or at least some of the subsets of vehicle telematics data may be assigned to the disclosed drivers.
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公开(公告)号：US10115246B1
公开(公告)日：2018-10-30
申请号：US15010381
申请日：2016-01-29
摘要： A method for attributing vehicle telematics data to individuals may include receiving vehicle telematics data collected by a data collection device during a plurality of trips. Subsets of the vehicle telematics data may correspond to different trips, and may be used to generate respective metric sets. Each metric set may include one or more metrics each indicative of a different driving behavior or a different feature of a driving environment. The method may also include retrieving, from a policy database, policy information pertaining to an insurance policy associated with the data collection device, and determining, based upon the policy information, a number of disclosed drivers associated with the insurance policy. A statistical analysis may be performed on the metric sets, and, based upon the results, at least some of the metrics and/or at least some of the subsets of vehicle telematics data may be assigned to the disclosed drivers.
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