Abstract: An on-demand insurance (ODI) server for generating dynamic user offerings may be provided. The ODI server may include at least one processor in communication with a memory device. The at least one processor may be programmed to (i) receive, from a user computing device associated with a user, an insurance policy request for a trip from a start location to an end location, (ii) determine at least one transportation mode available for the trip, (iii) access contextual data associated with the trip, (iv) assess a risk associated with the at least one transportation mode, (v) calculate a risk score associated with the at least one transportation mode based upon at least the contextual data, (vi) generate an insurance offering associated with the at least one transportation mode, and/or (vii) transmit the insurance offering in real time to the user computing device for purchase by the user.

Abstract: Systems and methods are described for performing analysis of parametric events. The method may include: (1) receiving weather data from a weather oracle network; (2) calculating, using a first trained machine learning algorithm, a likelihood of a trigger activation for a parametric event (or a trigger event) for a user based at least upon the weather data; (3) calculating, using a second trained machine learning algorithm, an estimated loss for the user based at least upon the likelihood of the trigger activation; (4) determining an initial coverage for the user; and (5) determining whether to offer the user updated coverage for the parametric event based at least upon a comparison of the initial coverage and the estimated loss for the user.

Abstract: Techniques for using connected vehicles as secondary data sources to confirm weather data and other triggering crop-damaging events associated with agriculture and agribusinesses, and mitigation techniques associated therewith are provided, including (1) determining indication of a weather event associated with a location of interest; (2) determining possible mitigation techniques for mitigating damage caused by the weather event; (3) receiving indications of location data associated with vehicles/passengers; (4) comparing the location data to a location of interest in order to identify vehicles/passengers within a proximity of the location of interest; (5) receiving sensor data captured by sensors associated with the identified vehicles; and (6) determining, based upon the sensor data captured by the sensors associated with the identified vehicles over a period of time, whether any of the possible mitigation techniques have been performed at the location of interest over the period of time.

Abstract: Techniques for using connected vehicles as secondary data sources to confirm weather data and other triggering events are provided, including (1) determining indication of a weather event associated with a location of interest; (2) receiving indications of location data captured by location sensors associated with vehicles (such as vehicle-mounted sensors and/or mobile devices, virtual headsets, or wearables of passengers); (3) comparing the location data captured by the location sensors to a location of interest in order to identify one or more vehicles within a proximity of the location of interest; (4) receiving environmental sensor data captured by environmental sensors associated with one or more of the identified vehicles; and (5) determining, based upon the environmental sensor data captured by the environmental sensors associated with the one or more of the identified vehicles, an indication of an accuracy of the indication of the weather event associated with the location of interest.

Abstract: Systems and methods are described for facilitating a zero-trust index mutual aid on a distributed ledger. The method may include: (1) receiving weather data at the distributed ledger; (2) detecting, based at least upon the weather data, that a trigger for a parametric event is met for a first subset of a plurality of users; (3) retrieving, responsive to the detecting, payment from a second subset of the plurality of users in accordance with a smart contract stored on the distributed ledger; (4) allocating the payment from the second subset of the plurality of users into respective allocated payments in accordance with the smart contract; and (5) causing the first subset of the plurality of users to receive the respective allocated payments in accordance with the smart contract.

Abstract: An adaptive mapping (AM) computing device having at least one processor in communication with at least one memory device is provided. The AM computing device may be configured to retrieve a plurality of tasks associated with a user, and retrieve geographic mapping data. The AM computing device may also generate a route model based upon the retrieved plurality of tasks and the retrieved geographic mapping data. The AM computing device may execute the route model to determine an optimal route, and transmit, to the user, an optimized travel plan based upon the optimal route.

Abstract: The following relates generally to determining effectiveness of an update to a vehicle feature. In some embodiments, information indicating an update to a vehicle feature, and accident record information may be received. A first dataset from before the update was implemented in the vehicle, and a second dataset from after the update was implemented in the vehicle may then be constructed. An effectiveness score may then be calculated based upon the first and second datasets.

Abstract: An adaptive mapping (AM) computing device having at least one processor in communication with at least one memory device is provided. The AM computing device may be configured to retrieve a plurality of tasks associated with a user, and retrieve geographic mapping data. The AM computing device may also generate a route model based upon the retrieved plurality of tasks and the retrieved geographic mapping data. The AM computing device may execute the route model to determine an optimal route, and transmit, to the user, an optimized travel plan based upon the optimal route.

Abstract: An on-demand insurance (ODI) server for generating dynamic user offerings may be provided. The ODI server may include at least one processor in communication with a memory device. The at least one processor may be programmed to (i) receive, from a user computing device associated with a user, an insurance policy request for a trip from a start location to an end location, (ii) determine at least one transportation mode available for the trip, (iii) access contextual data associated with the trip, (iv) assess a risk associated with the at least one transportation mode, (v) calculate a risk score associated with the at least one transportation mode based upon at least the contextual data, (vi) generate an insurance offering associated with the at least one transportation mode, and/or (vii) transmit the insurance offering in real time to the user computing device for purchase by the user.

Abstract: Systems and methods for determining the effectiveness of vehicle safety features are provided. Vehicle data is obtained for vehicles having various smart safety features, and a list of translated vehicle build records is generated from the obtained data applying OEM-agnostic terminology for the smart safety features. A machine learning algorithm may be trained to generate an effectiveness score associated with one or more smart safety features, at least by analyzing a plurality of translated vehicle build records, vehicle telematics data, and vehicle accident records associated with each of the plurality of vehicles.

Abstract: Systems and methods for determining a reparability of a vehicle are provided. In some embodiments, vehicle data is obtained, and a list of variables is generated from the obtained data. A machine learning algorithm may then be trained to generate a reparability metric by: (i) generating correlation metrics between the variables and costs to repair the vehicle, (ii) removing variables with correlation metrics below a threshold, and (iii) training the machine learning algorithm based upon unremoved variables.

Abstract: Systems and methods for building a vehicle data repository (VDR) are provided. In some embodiments, the VDR is constructed by adding standardized build sheets to it. The standardized build sheets may be constructed by selecting data from various data sources (e.g., original equipment manufacturer (OEM) databases, National Highway Traffic Safety Administration (NHTSA) databases, Highway Loss Data Institute (HLDI) databases, and/or Insurance Institute for Highway Safety (IIHS) databases). Furthermore, a common ontology may be created and applied to the various data sources to aide in the selection of data between the data sources to construct the standardized build sheets.

Abstract: An on-demand insurance (ODI) server for generating dynamic user offerings may be provided. The ODI server may include at least one processor in communication with a memory device. The at least one processor may be programmed to (i) receive, from a user computing device associated with a user, an insurance policy request for a trip from a start location to an end location, (ii) determine at least one transportation mode available for the trip, (iii) access contextual data associated with the trip, (iv) assess a risk associated with the at least one transportation mode, (v) calculate a risk score associated with the at least one transportation mode based upon at least the contextual data, (vi) generate an insurance offering associated with the at least one transportation mode, and/or (vii) transmit the insurance offering in real time to the user computing device for purchase by the user.