Abstract: A machine learning system determines an estimate of elasticity of an insurance policy. The system includes one or more processors in communication with at least one memory device, the one or more processors programmed to store an insurance policy model including a plurality of characteristics for the insurance policy and historical insurance policy data including a plurality of individual insurance policies. The one or more processors are further programmed to execute the insurance policy model to calculate an estimate of elasticity of the insurance policy based upon analyzing the historical data to detect a change to a characteristic of the insurance policy. The one or more processors are further programmed to modify a characteristic based upon the calculated elasticity. The processors are further programmed to receive a user insurance application, generate an individualized insurance policy based upon the application and the modified characteristic, and transmit the individualized insurance policy.

Abstract: A method of determining loss reserves and/or providing automatic financial reporting related thereto via one or more processors includes (1) receiving a plurality of historical electronic claim documents, each respectively labeled with a claim loss amount; (2) normalizing each respective claim loss amount and training an artificial intelligence or machine learning algorithm, module, or model, such as an artificial neural network, by applying the plurality of electronic claim documents to the artificial intelligence or machine learning algorithm, module, or model. The method may include receiving a user claim and predicting a loss reserve amount by applying the user claim to the trained artificial intelligence or machine learning algorithm, module, or model, and may include unreported claims.

Abstract: Methods and systems for assessing, detecting, and responding to malfunctions involving components of autonomous vehicles and/or smart homes are described herein. Autonomous operation features and related components can be assessed using direct or indirect data regarding operation. Vehicle collision and/or smart home incident monitoring, damage detection, and responses are also described, with particular focus on the particular challenges associated with incident response for unoccupied vehicles and/or smart homes. Operating data associated with the autonomous vehicle and/or smart home may be received. Within the operating data, a divergence between sensor data from one or more sensors and control data from one or more autonomous operation features may be identified. Based on the divergence, it may be determined that an incident has occurred. Accordingly, a response to the incident may be determined. The response may be implemented by the autonomous vehicle and/or smart home.

Abstract: A method of determining loss reserves and/or providing automatic financial reporting related thereto via one or more processors includes (1) receiving a plurality of historical electronic claim documents, each respectively labeled with a claim loss amount; (2) normalizing each respective claim loss amount and training an artificial intelligence or machine learning algorithm, module, or model, such as an artificial neural network, by applying the plurality of electronic claim documents to the artificial intelligence or machine learning algorithm, module, or model. The method may include receiving a user claim and predicting a loss reserve amount by applying the user claim to the trained artificial intelligence or machine learning algorithm, module, or model, and may include unreported claims.

Abstract: Systems and methods for modifying operation of an autonomous vehicle in an emergency situation are disclosed. According to aspects, a computing device detects, based on sensor(s), an emergency event associated with the autonomous vehicle. In response to detecting the emergency event, the computing device determines location(s) of emergency vehicle(s) and determines an assistance location for the autonomous vehicle. The computing device transmits the assistance location to the emergency vehicle(s), obtains a current operation of the autonomous vehicle, and determines a vehicle operation modification for the autonomous vehicle.

Abstract: 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.

Abstract: 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.

Abstract: Methods and systems are provided for monitoring use of a vehicle having one or more autonomous (and/or semi-autonomous) operation features to determine and respond to incidents, such as collisions, thefts, or breakdowns. According to certain aspects, operating data from sensors within or near the vehicle may be used to determine when an incident has occurred and determine an appropriate response. The responses may include contacting a third party to provide assistance, such as local emergency services. In some embodiments, occurrence of the incident may be verified by automated communication with the vehicle operator.

Abstract: 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.

Abstract: Multiple insurance policies may be grouped or segmented into affinity groups, each of which corresponds to a respective risk level. A set of financial instruments corresponding to a particular affinity group of insurance policies may be created and automatically offered for auction, e.g., online. Bids received from potential investors may indicate, e.g., percentage of assumed risk, percentage of profit sharing, percentage of shared loss, service or commission fee amounts, etc. that are acceptable to the bidding party. The party or parties submitting the winning bids may be awarded investment in the financial instruments and may enter into respective financial agreements with the financial instrument provider, thereby securitizing the risk associated with the insurance policies. Profits and/or losses generated by the insurance policies of the affinity group corresponding to the financial instruments may be distributed in accordance with the terms of the financial agreement.

Abstract: Methods and systems for autonomous vehicle recharging or refueling are disclosed. Autonomous vehicles may be automatically refueled by routing the vehicles to available fueling stations when not in operation, according to methods described herein. A fuel level within a tank of an autonomous vehicle may be monitored until it reaches a refueling threshold, at which point an on-board computer may generate a predicted use profile for the vehicle. Based upon the predicted use profile, a time and location for the vehicle to refuel the vehicle may be determined. In some embodiments, the vehicle may be controlled to automatically travel to a fueling station, refill a fuel tank, and return to its starting location in order to refuel when not in use.

Abstract: Blockchain techniques for providing insurance to an affinity group may include an enforcement server that receives a plurality of bidding actions (e.g., bid or no-bid) from a plurality of bidding entities in response to an auction of an opportunity to provide insurance for members of the affinity group. The server may compile transactions corresponding to the received bidding actions into a block of transactions (e.g., a round of bidding), distribute the block of transactions to validation entities to form a consensus on the highest bid, and update copies of a distributed ledger with the block of transactions. The server may identify a winning bid and provide affinity group member data to the winning bidding entity, thereby providing consumers with lower cost insurance and/or insurance that is more reflective of actual risk (or lack thereof). The affinity group may be updated over time and re-auctioned using the blockchain techniques.

Abstract: Systems and methods for situational modification of autonomous vehicle operation are disclosed. According to aspects, a computing device may detect the occurrence of an emergency event and may determine a current operation of an autonomous vehicle that may be associated with the emergency event. The computing device may determine a modification to operation of the autonomous vehicle, where the modification may represent a violation of a roadway regulation that may enable effective handling of the emergency event. The computing device may generate a set of instructions for the autonomous vehicle to execute to cause the autonomous vehicle to undertake the operation modification.

Abstract: A computer-implemented method of generating and broadcasting telematics and/or image data is provided. Telematics and/or image data may be collected, with customer permission, in real-time by a mobile device (or a Telematics App running thereon) traveling within an originating vehicle. The telematics data may include acceleration, braking, speed, heading, and location data associated with the originating vehicle. The mobile device may generate an updated telematics data broadcast including up-to-date telematics data at least every few seconds; and then broadcast the updated telematics data broadcast at least every few seconds via wireless communication to another computing device to facilitate alerting another vehicle or driver of an abnormal traffic condition or event that the originating vehicle is experiencing.

Abstract: Methods and systems are provided for monitoring use of a vehicle having one or more autonomous (and/or semi-autonomous) operation features to determine and respond to incidents, such as collisions, thefts, or breakdowns. According to certain aspects, operating data from sensors within or near the vehicle may be used to determine when an incident has occurred and determine an appropriate response. The responses may include contacting a third party to provide assistance, such as local emergency services. In some embodiments, occurrence of the incident may be verified by automated communication with the vehicle operator.

Abstract: 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.

Abstract: The present disclosure generally relates to a computer implemented system and method for automatically generating insurance policy related data. The system and method may determine a vehicle operator and generate empirical vehicle operator identity data. The system and method may further acquire empirical vehicle operation data related to the actual vehicle operator, and correlate the empirical vehicle operator identity data and the empirical vehicle operation data to generate vehicle insurance policy related data. The system and method may further include processing one or more insurance options, including underwriting and pricing, based at least in part on the vehicle insurance policy related data.

Abstract: A computer system configured to use train telematics data to reduce risk of accidents via a mobile device traveling within a vehicle may be provided. The mobile device may be configured to (1) receive train telematics data associated with a train that includes GPS location, speed, route, heading, acceleration, and/or track data; (2) determine when, or a time period of when, the train will pass through, be passing through, or be within a predetermined distance of a railroad crossing based upon the train telematics data; (3) determine an alternate route for the vehicle to take to avoid waiting at the railroad crossing; and (4) cause display of the alternate route on a display of the mobile device or a vehicle navigation system to allow the train to pass and to avoid train-vehicle collisions. Insurance discounts may be generated based upon the risk mitigation or prevention functionality.

Abstract: Methods and systems for determining fault for an accident involving a vehicle having one or more autonomous and/or semi-autonomous operation features are provided. According to certain aspects, performance data indicative of the performance of the features may be used to determine fault for a vehicle accident, such as a collision, by allocating fault for the accident between a vehicle operator, the autonomous operation features, or a third party. The allocation of fault may be used to determine an adjustment to an insurance policy and/or adjust coverage levels for an insurance policy. The allocation of fault may further be used to adjust risk levels or profiles associated with the autonomous or semi-autonomous operation features, which may be applied to other vehicles having the same or similar features.

Abstract: A system of broadcasting information related to hazards impacting vehicle travel may include a mobile device configured to: (1) mount within a vehicle dashboard cradle so that the mobile device is positioned to take images forward of a vehicle; (2) collect telematics data, with customer permission or affirmative consent, when the vehicle is moving, including GPS location, speed, heading, lane, braking, cornering, acceleration, and/or route data of the vehicle; (3) generate an alert based upon the telematics data and the images, and/or broadcast data including the images and the telematics data; and/or (4) broadcast the alert and/or the data collected to (a) a nearby vehicle(s) and/or (b) a smart infrastructure component(s) to facilitate warning of hazards upon the nearby vehicle(s) and/or the smart infrastructure component(s) receiving and processing the alert and/or the broadcasted data. Insurance discounts may be generated based upon the risk mitigation or prevention functionality.