Blockchain controlled multi-carrier auction system for usage-based auto insurance

A computer-implemented method for managing a smart contract for one or more approved entities, the method implemented on a computer system comprising at least one processor, at least one memory device, and at least one vehicle controller associated with a vehicle the computer-implemented method comprising:

• receiving, at the computer system via the at least one vehicle controller associated with the vehicle, real-time vehicle telematics information associated with a current trip of the vehicle collected by one or more telematics sensors associated with the vehicle, wherein the real-time vehicle telematics information includes operational data related to operation of the vehicle during the current trip;
• executing, by the computer system, a machine learning model using the real-time vehicle telematics information to identify a driver for the current trip, the machine learning model trained using a plurality of historical vehicle telematics data associated with a known driver, the machine learning model configured to compare trends of at least the operational data for the current trip to trends of the plurality of historical vehicle telematics data;
• electronically comparing (i) the operational data to operational parameters associated with a smart contract for the current trip to determine whether the operation of the vehicle during the current trip is compliant with one or more original terms of the smart contract and (ii) the identified driver to a driver identifier stored in the smart contract to confirm the identified driver is one of the one or more approved entities;
• in response to a determination of potential non-compliance by the identified driver with the one more original terms while the current trip is occurring, executing computer instructions associated with the smart contract to (i) automatically generate one or more proposed modifications to the one or more original terms of the smart contract while the current trip is occurring and (ii) cause a message regarding the determined potential non-compliance with the one or more original terms and the one or more proposed modifications to be displayed on a user computer device associated with the identified driver for the current trip;
• storing, while the current trip is occurring, in a data structure of the smart contract and as an update to the smart contract, data corresponding to each of (i) a response by the identified driver to the message, the response accepting at least one of the one or more proposed responses, and (ii) the at least one accepted modification;
• causing execution of a transaction in association with a provider associated with the smart contract to implement the at least one accepted modification while the current trip is occurring;
• and
• storing, in connection with a conclusion of the current trip and in association with the machine learning model, post-trip data corresponding to the current trip, wherein the stored post-trip data, when executed on by the machine learning model, updates the plurality of historical vehicle telematics data thereby improving the accuracy of the plurality of historical vehicle telematics data for generating a subsequent smart contract for the identified driver.

At least one non-transitory computer-readable storage media having computer-executable instructions embodied thereon, wherein when executed by at least one processor of a computer system configured to manage a smart contract for one or more approved entities, the computer system further comprising at least one memory device, the computer-executable instructions cause the at least one processor to:

• receive, via at least one vehicle controller of a vehicle, real-time vehicle telematics information associated with a current trip of the vehicle collected by one or more telematics sensors associated with the vehicle, wherein the real-time vehicle telematics information includes operational data related to operation of the vehicle during the current trip;
• execute a machine learning model using the real-time vehicle telematics information to identify a driver for the current trip, the machine learning model trained using a plurality of historical vehicle telematics data associated with a known driver, the machine learning model configured to compare trends of at least the operational data for the current trip to trends of the plurality of historical vehicle telematics data;
• electronically compare (i) the operational data to operational parameters associated with a smart contract for the current trip to determine whether the operation of the vehicle during the current trip is compliant with one or more original terms of the smart contract and (ii) the identified driver to a driver identifier stored in the smart contract to confirm the identified driver is one of the one or more approved entities;
• in response to a determination of potential non-compliance by the identified driver with the one more original terms while the current trip is occurring, execute computer instructions associated with the smart contract to (i) automatically generate one or more proposed modifications to the one or more original terms of the smart contract while the current trip is occurring and (ii) cause a message regarding the determined potential non-compliance with the one or more original terms and the one or more proposed modifications to be displayed on a user computer device associated with the identified driver for the current trip;
• store, while the current trip is occurring, in a data structure of the smart contract and as an update to the smart contract, data corresponding to each of (i) a response by the identified driver to the message, the response accepting at least one of the one or more proposed responses, and (ii) the at least one accepted modification;
• cause execution of a transaction in association with a provider associated with the smart contract to implement the at least one accepted modification while the current trip is occurring;
• and
• store, in connection with a conclusion of the current trip and in association with the machine learning model, post-trip data corresponding to the current trip, wherein the stored post-trip data, when executed on by the machine learning model, updates the plurality of historical vehicle telematics data thereby improving the accuracy of the plurality of historical vehicle telematics data for generating a subsequent smart contract for the identified driver.