Abstract: A method and corresponding system to determine an optimized credit limit assignment using reinforcement learning techniques in order to maximize a reward function for a given bank. A reinforcement learning module is configured to use a set of user profiles and an associated set of risk profiles to determine an initial credit limit assignment. Based on this initial credit limit assignment, an updated set of user profiles and an associated set of updated risk profiles are generated. The reinforcement learning module can use these updated sets of user profiles and associated risk profile as inputs to determine an optimized credit limit assignment that maximizes the reward function for the given bank.

Abstract: A system (100) includes one or more processors (113), one or more memory devices (114) operable with the one or more processors, and a communication device (1105) in communication with at least one terminal device (102,103,104,105) having a user interface (112). The one or more processors causing display of a visual simulation (123) in the user interface of one or more loss graphical objects (501) interacting with one or both of an entity type graphical object (601) or one or more loss mitigator graphical objects (602,603,604) as a function of a confidence level defined by a loss probability and a loss magnitude.

Abstract: Systems and methods for distributed business process management are disclosed. In one embodiment, in an information processing apparatus comprising at least one computer processor, a method for configuration-driven distributed orchestration using different software components to execute a complex business process may include: (1) receiving a request for a runtime flow from a flow management adapter; (2) reading a flow configuration from the request; (3) creating an instance of the runtime flow; (4) initiating a service call to each component in the runtime flow; (5) creating a runtime instance in a database along with a state of each dependency in the runtime flow; and in response to external dependencies being met: (6) building and sending message to the components using a message builder; (7) initiating flow actions via an event-driven scheduler; and (8) making a service call to at least one of the components using the message builders.

Abstract: In an illustrative embodiment, systems and methods for monitoring insurance claims include identifying, based on predetermined monitoring frequency, insurance claims identified for vulnerability detection processing. Vulnerability detection features may be extracted from data files of the claims, which provide an indication of claim handling deficiencies that can cause claim leakage. A trained vulnerability detection data model can be used to detect claim handling vulnerabilities within the extracted vulnerability detection features where each of the vulnerabilities may include a likelihood of the vulnerability resulting in claim leakage. The vulnerability detection data model may be trained with a data set customized to a respective insurance provider for each claim. Vulnerability scores indicating an overall likelihood of claim leakage can be calculated for the claims based on the detected claim handling vulnerabilities.

Abstract: A method includes receiving, from a computing device of a customer, a request that specifies allocation of an insurance premium to a plurality of locations of the customer. The method includes generating, in response to the request and by an invoice distribution server, one or more rules to implement the request. The method also includes receiving, by the invoice distribution system, an amount of the insurance premium. The method also includes executing the one or more rules to allocate the insurance premium to the plurality of locations of the customer in accordance with the request. The method further includes generating a plurality of invoices indicative of the allocation of the insurance premium.

Abstract: A suite of fluidless predictive machine learning models includes a fluidless mortality module, smoking propensity model, and prescription fills model. The fluidless machine learning models are trained against a corpus of historical underwriting applications of a sponsoring enterprise, including clinical data of historical applicants. A data appended procedure supplements historical applications data with public records and credit risks. Various features of this data are engineered for improved predictive characteristics. Fluidless models are trained by application of a random forest ensemble including survival, regression and classification models. The trained models produce high-resolution, individual mortality scores. A fluidless underwriting protocol runs these predictive models to assess mortality risk and other risk attributes of a fluidless application that excludes clinical data to determine whether to present an accelerated underwriting offer.

Abstract: Embodiments of the present invention utilize mobile devices to provide information on a user's behaviors during transportation. For example, a mobile device carried by a user can be used to detect and analyze driving behavior. The mobile device can further be used to provide alerts based on driving behavior and to encourage and track modification of driving behavior.

Abstract: A multi-region risk relationship data store may contain electronic records representing a plurality of multi-region risk relationships and, for each multi-region risk relationship, an electronic record identifier and a set of multi-region attribute values including a plurality of hierarchical local risk relationship attribute values. A back-end application computer server may receive an indication of a selected risk relationship and display at least some of the associated multi-region attribute values. The server may receive adjustments to some of the multi-region or local risk-relationship attribute values, and displayed information may be automatically and dynamically configured based on the received adjustments and a standalone risk calculation unit. At least some local risk relationship attribute values may be replicated for multiple regions. When adjustments have been completed, the server may automatically assemble a multi-region risk relationship document.

Abstract: Consumer safety of repaired vehicles can be improved by providing systems and methods for generating vehicle repair estimate reports based on predictive estimating and transmitting these reports to clients. A master server serviced by a vehicle manufacturer generates a vehicle repair estimate report based on predictive estimating and transmits this report to a client via a web-based graphical user interface. Also, a master server serviced by a vehicle manufacturer generates a vehicle repair estimate report based on predictive estimating and transmits this report to a client via a vehicle repair claims management user interface. The vehicle repair estimate report based on predictive estimating may comprise repair packages and repair information relating to recommended replacement parts and quantities thereof, the part numbers for the suggested replacement parts, the price estimate for the suggested replacement parts, manufacturer-recommended repair instructions, and other relevant information.

Abstract: A computer-implemented method of providing specialized media on a website is disclosed. The method includes producing campaign media which is delivered to a user through a third-party provider. After the user has received the campaign media, and upon indication of a request from the user to access a website controlled by a content provider, specialized media on a website controlled by the content provider may be provided. Providing the specialized media may be done based on the campaign media previously delivered to the user through the third-party provider.

Abstract: An electronic device receives operational data indicative of an operational characteristic of a vehicle from a sensor of the electronic device, a sensor of the vehicle, and/or images/videos captured by a camera. The electronic device determines that the vehicle has had a potential incident and a likelihood that the potential incident has actually occurred based on analysis of the operational data. The electronic device also receives risk management data associated with the vehicle from a database, and determines a severity level for the potential incident based on the operational data and the risk management data. The electronic device then sends a notification indicative of the potential incident based on the likelihood that the potential incident has actually occurred and the severity level for the potential incident to a third-party remote system (e.g., of a towing service, an emergency service, or both) to request assistance.

Abstract: Disclosed is a computing architecture and method for operating and managing a health care plan from a mobile device or web browser. The present invention provides an architecture that facilitates customer access to affordable, personalized health care, along with the efficient processing of claims.

Abstract: Embodiments are disclosed for automatically processing a claim provided by a user. Responsive to receiving a notice of loss associated with a claim of a user, a set of customer identity validation data are collected. The set of customer identity validation data may be determined to meet a pre-defined identity validation criteria. Responsive to determining that the set of customer identity validation data meets the pre-defined identity validation criteria, current claim evaluation data for the claim may be accessed. A set of predictive impact assessment scores associated with the current claim evaluation data may be determined using a predictive model. The set of current claim evaluation data may be determined to meet pre-defined claim data criteria by comparing the predictive impact assessment scores with a set of impact assessment thresholds. Responsive to determining that the set of current claim evaluation data meets the pre-defined claim data criteria, a reactive action to the claim may be determined.

Abstract: Vehicle inspection systems and methods are disclosed. A system disclosed includes a drone vehicle configured to fly in a vicinity of the damaged vehicle at an accident scene. The drone includes at least one camera for taking imagery of the damaged vehicle at an accident scene in support of a damage assessment and a frame coupling the drone vehicle and the at least one camera. A computing device is in communication with the at least one camera to receive the imagery from the at least one camera for purposes of damage assessment.

Abstract: Example methods, apparatus and articles of manufacture to process insurance claims using cloud computing are disclosed herein. An example system to process an insurance claim using cloud computing includes an assistance resource of a cloud computing platform configured to interact electronically with at least one of an insured person, a representative of the insured person, or a representative of an insured entity to obtain information related to an insurance claim, an artificial intelligence resource of the cloud computing platform configured to process the information to identify an action to be taken for the insurance claim, and a backend system configured to perform the action, the backend processing module also useable by a person to manually process the insurance claim.

Abstract: The subject matter disclosed herein provides methods for executing a script to retrieve chargeback records. The method may execute a script for retrieving one or more chargeback records for one or more merchants from one or more web portals of one or more financial institutions. The one or more chargeback records may represent a return of funds to one or more purchasers by the one or more financial institutions. The script may have commands. These commands may navigate a web browser to a web portal associated with at least one financial institution. These commands may also provide authentication information for the at least one merchant into the web portal, access the one or more chargeback records for the at least one merchant, and download the one or more chargeback records. Related apparatus, systems, techniques, and articles are also described.

Abstract: Methods and systems for monitoring use and determining risks associated with operation of a vehicle having one or more autonomous operation features are provided. According to certain aspects, operating data may be recorded during operation of the vehicle. This may include information regarding the vehicle, the vehicle environment, use of the autonomous operation features, and/or control decisions made by the features. The control decisions may include actions the feature would have taken to control the vehicle, but which were not taken because a vehicle operator was controlling the relevant aspect of vehicle operation at the time. The operating data may be recorded in a log, which may then be used to determine risk levels associated with vehicle operation based upon risk levels associated with the autonomous operation features. The risk levels may further be used to adjust an insurance policy associated with the vehicle.

Abstract: Systems and methods relating to improving the experience of gig-economy workers are disclosed, with particular reference to gig-economy work involving vehicle use. Such systems and methods include automatically monitoring and evaluating activities such as driving during performance of gigs, as well as providing recommendations based thereupon. Gig-related activities may be automatically detected based upon data provided by a mobile device associated with the gig-economy worker, thereby automatically generating a record of such activities for the worker. Telematics data indicative of movement of a vehicle may be collected and analyzed to detect gig-economy activities. During or after performance of gig-economy work, data automatically collected may be used to generate and present recommendations or education points to the gig-economy worker.

Abstract: Embodiments may monitor operation of a machine and provide feedback to maintain use within certain parameters. Mobile personal communication device sensors are each configured to monitor at least one machine parameter (speed, acceleration, location, etc.), generate a signal encapsulating the monitored machine parameter, and transmit the generated sensor signals to a control unit of the communication device. The control unit may receive the generated sensor signals, store the received signals, and selectively combine the received signals. The communication device also includes a transmitter coupled to the control unit capable of transmitting the combined signal. A transceiver remote from the machine may determine a current machine condition and compare that condition to received conditions from other machines. Feedback may then be provided to adjust operation of the machine based on the comparison. For example, an operator interface may provide audio and/or visual feedback to the operator of a vehicle.

Abstract: A system may receive a first resource allocation request (associated with a first company) from a first entity. The system may determine that the first request is not associated with a risk relationship and arrange for it to be processed via a claim servicing entity. Information about the first request may then be routed to a request handler device for processing and a communication with a first graphical indication may be generated. The system may also receive a second resource allocation request (associated with the first company) from a second entity and determine that it is to be processed via a risk. The system may then route information about the second request to a request handler device for processing and generate a communication with a second graphical indication. Note that the request handler device may be the same device that received the information about the first request.

Abstract: An employee net promoter score (eNPS) adjustment system and method adjusts an employee net promoter score (eNPS) using a predictive model. The predictive model identifies key eNPS driver variables and adjusts a programmatically generated quantification of employee engagement and/or sentiment toward an employer based upon received data representing the key eNPS driver variables.

Abstract: A system may include processor(s), a memory in communication with the processor(s), and storing instructions, that when executed by the processor(s), are configured to cause the system to prevent unnecessary payments. The system may receive a signal indicative of an event in which a user device associated with a user enters a geofenced area associated with a merchant. The system may identify a merchant category code (MCC) associated with the merchant, and determine whether benefit(s) related to the MCC are associated with account(s) of the user. The system may receive an attempted purchase associated with the merchant, and may determine whether the attempted purchase relates to the benefit(s). The system may automatically block the attempted purchase related to the benefit(s) and associated with the user, and may transmit a message to the user via the user device, the message providing the benefit(s).

Abstract: Concepts and technologies directed to an integrated telecommunications roadside unit for supporting vehicle-to-everything (“V2X”) communications are disclosed herein. Embodiments of an integrated telecommunications roadside unit can include a processor and a memory that store computer-executable instructions that configure a processor to perform operations. The operations can include intercepting a vehicle-to-vehicle communication that is provided from a first vehicle to a second vehicle. The vehicle-to-vehicle communication can be transmitted using a direct transmission mode. The operations can further include encapsulating the vehicle-to-vehicle communication in a user plane package. The operations can also include routing the user plane package to a destination network access node, where the destination network access node is outside of a direct communication range corresponding to the first vehicle and the second vehicle.

Abstract: Methods, computer-readable media, software, and apparatuses provide a system for adjusting a deductible based on a current driving situation. Information regarding the current driving situation related to a vehicle may be obtained from various sources. The information may be used to evaluate a degree of safety of the current driving situation. If the driving situation is deemed to be safe, the deductible may be lowered. The amount the deductible may be lowered may be determined based on insurance information regarding past accidents or past insurance claims. A notification of the adjustment may be output to the driver. Also, the adjustment may be stored with a time associated with the adjustment so that the appropriate deductible is used in case of an accident. Multiple adjustments to the deductible may be made during a single trip as the driving situation changes.

Abstract: Location data associated with an on-demand electric vehicle and an indication that an on-demand electric vehicle has fallen are received. It is determined that a rental associated with the on-demand electric vehicle has ended. Information indicating that the on-demand electric vehicle is in a fallen position is provided via a user interface.

Abstract: Methods and systems for homeowner-directed risk mitigation for damage to a property associated with insurance-related events are provided. A smart home controller may analyze data received from a plurality of smart devices disposed on, or proximate to, a property as well as data received from an insurance provider. If it is determined that an actual or potential risk of property damage exists, the smart home controller may transmit an alert to a homeowner detailing the risk. The homeowner may respond to the alert by transmitting an instruction to mitigate or prevent damage associated with the risk back to the smart home controller. Subsequently, the smart home controller may transmit information about the actual or potential risks and any homeowner-directed mitigative actions to an insurance provider. The insurance provider may interpret the transmitted data and perform insurance activities, such as providing discounts and adjusting an insurance policy associated with the property.

Abstract: Method and system for determining a status of an application previously installed on a mobile device of a user are disclosed. For example, the method includes determining that an application has been installed on a mobile device of a user, determining whether the sensor data is received from the application at least once during a predetermined time interval, determining that the application does not work properly on the mobile device of the user in response to the sensor data being not received at least once during the predetermined time interval, and transmitting a notification to the mobile device indicating that the application does not work properly.

Abstract: A system that includes a calculation unit configured to receive an input derived from data measured from a sensor located in a first location, the calculation unit is configured to determine a value for a risk premium. The system includes a storage unit to store the input; and an output unit configured to receive information based on the value determined by the calculation unit and outputs a graphical representation to a display device. The system is at a second location different from the first location.

Abstract: An artificial intelligence-based multi-channel system and method to evaluate a buyer or consumer's intent to purchase benefits products. The system and method determine the buyer or consumer's needs, identifies and presents relevant benefits products based on eligibility to the buyer or consumer. The system and method can transfer the buyer or consumer to a producer to facilitate an enrollment process for the benefits products.

Abstract: Methods, computer-readable media, systems and apparatuses for determining and implementing usage-based insurance policies are presented. A first cost per day and second cost per day associated with the usage-based insurance policy may be determined and a cost for a first trip may be determined based on the cost per day and cost per mile. Driving data received during the first trip may be used to modify the cost per day and cost per mile to determine a second cost per day and/or cost per mile that may be used to determine a cost of subsequent driving trips.

Abstract: Systems, methods, and computer-executable instructions for identifying a candidate include receiving unscripted communication, the unscripted communication comprising communication from a first speaker. Properties of the unscripted communication are extracted. A psychographic classifier classifies the first speaker into a psychographic category based on the extracted properties. An aggregate psychographic category of a team is determined based on psychographic categories of each of the team members of the team. A weakness in the aggregate psychographic category of the team is determined. A new team member that has a psychographic category that addresses the weakness in the aggregate psychographic category of the team is identified. A recommendation that the first speaker become a team member of the team is provided.

Abstract: Systems and methods for entity risk management are disclosed. A system for entity risk management may include a memory storing instructions and at least one processor configured to execute instructions to perform operations including: establishing a connection between the system and a data source, the data source being remote from the system and associated with a first entity; receiving first document data from the data source; normalizing the first document data; classifying the normalized document data; extracting model input data from the classified document data; applying a machine learning model trained to predict risk levels using second document data to the extracted model input data to predict a risk level associated with the first entity; generating analysis data based on the predicted risk level; and based on the analysis data, transmitting an alert to a management device communicably connected to the system.

Abstract: A method, computer system, and a computer program product for active machine learning is provided. The present invention may include annotating a plurality of data entries. The present invention may also include building a first dataset based on the annotated plurality of data entries. The present invention may then include receiving user feedback based on the built first dataset. The present invention may further include assigning a plurality of weights to a plurality of data entry subsets. The present invention may also include generating a second weighted dataset based on the received user feedback.

Abstract: Aspects of the disclosure relate to processing asset transfers. A computing platform may receive an asset transfer request and extract information from it. The computing platform may modify a distributed ledger to include a new block corresponding to the asset transfer request. The computing platform may identify a consensus method by identifying, for each of a plurality of consensus methods, a subset of blocks from the distributed ledger that, when used to execute the corresponding consensus method, result in a lowest energy consumption value in comparison to remaining subsets of blocks. The computing platform may execute each consensus method using the corresponding identified subset of blocks, which may result in establishing consensus to process the asset transfer request. Based on establishing the consensus, the computing platform may direct an event processing platform to process the asset transfer request, which may cause the event processing platform to process the request accordingly.

Abstract: The present disclosure introduces systems and methods to evaluate application data. A computer system to evaluate application data is described. In one embodiment, a data module may be used to receive application data and validate the application data against at least one secondary data source. A predictive modeling module may be used to model the application data by applying predictive analytics. Further, a confidence level module may be used to calculate a confidence level factor and at least one aggregate degree of confidence level to evaluate an application. Other embodiments are also described.

Abstract: Method for managing charging service comprising independent Installers, independent utilities, independent electric charging customers, CO2 levels, and independent energy markets, the method comprising: electronically calculating cost of service on a periodic basis based on a plurality of dynamically changing influencing parameters; electronically determining a capital cost of installation; obtaining customer's zip code to determine regional variations in installation cost; estimating customer's location from customer's IP address to determine regional variations in installation cost; Soliciting information about conditions at an installation location which may affect installation cost by soliciting customer input in response to an online questionnaire; soliciting an address of the installation location, then obtaining data comprising age of a building, size of the building and number of units from publicly available data bases, to determine installation cost.

Abstract: Aspects of the disclosure relate to enhanced processing systems for providing dynamic driving metric outputs using improved machine learning methods. A computing platform may receive sensor data from vehicle sensors. The computing platform may generate a pattern deviation output corresponding to an output of a sensor data analysis model, an actual outcome associated with a lowest TTC value, and driving actions that occurred over a prediction horizon corresponding to the pattern deviation output. The computing platform may cluster the pattern deviation outputs to maximize a ratio of inter-cluster variance to intra-cluster variance. The computing platform may train a long short term memory (LSTM) for each cluster, and may verify consistency of the pattern deviation outputs in the respective clusters.

Abstract: A method performed by a computing system includes receiving from a first detection system, a plurality of identity tokens, the plurality of identity tokens including different types of identity tokens, the first detection system having a plurality of different types of detection instruments. The method further includes using information from the first detection system, creating correlation information that correlates different types of identity tokens. The method further includes receiving from a second detection system, a number of identity tokens, the second detection system having a plurality of different types of detection instruments that is fewer than the first detection system. The method further includes using the number of identity tokens received from the first and second detection systems, identifying a vehicle passing through the second detection system.

Abstract: Systems, computer-implemented methods, and computer program products to facilitate contextual comparison of semantics in conditions of different policies are provided. According to an embodiment, a system can comprise a processor that executes computer executable components stored in memory. The computer executable components can comprise a comparison component that contextually compares semantics of conditions in policy data of different policies based on a feature of at least one entity. The computer executable components further comprise a contextualization component that employs a model to provide a contextual explanation of how a first condition in first policy data of a first policy is semantically different from a second condition in second policy data of a second policy based on the feature of the at least one entity.

Abstract: A damage assessment module operating on a computer system automatically evaluates a property, estimating damage to the property by analyzing a point cloud of a property. The damage assessment module identifies individual point clusters or segments from the point cloud and detects potentially damaged areas of the property surface by identifying outlier points in the point clusters. The damage assessment module may be used to determine the financial cost of the damage and/or determine whether the property should be replaced or repaired. In addition to eliminating the need for an estimator to visit the property in person, the damage assessment module improves the consistency and accuracy associated with estimating damage to a property.

Abstract: A system that varies the terms and conditions of a subsidized loan includes a blockchain service circuit structured to interpret a plurality of access control features corresponding to a plurality of parties associated with a loan; a data collection circuit structured to interpret entity information corresponding to a plurality of entities related to a lending transaction corresponding to the loan; a smart contract circuit structured to specify loan terms and conditions relating to the loan; and a loan management circuit structured to interpret loan related events in response to the entity information, the plurality of access control features, and the loan terms and conditions, wherein the loan related events are associated with the loan; and implement loan related activities in response to the entity information, the plurality of access control features, and the loan terms and conditions, wherein the loan related activities are associated with the loan.

Abstract: A computer-implemented method for expediting processing of a payment for a patient's healthcare service with a healthcare provider includes displaying an out-of-pocket cost for a patient for a healthcare service at a display interface of a computer device prior to the healthcare service being rendered. After patient selection of the healthcare service and payment of any out-of-pocket costs, a health token is issued to the patient as cryptocurrency for payment of the healthcare service. The health token is received from the healthcare provider after the healthcare provider renders the healthcare service, receives the health token from the patient for payment of the healthcare service, and embeds data pertaining to the healthcare service into the health token. Based on the data embedded in the health token, the healthcare service is approved or disapproved. If the healthcare service is approved, a payment for the healthcare service is issued to the healthcare provider.

Abstract: A system and method of smart contract and distributed ledger platform with blockchain authenticity verification includes a blockchain service circuit structured to interface with a distributed ledger; a data collection circuit structured to receive data related to a set of items of collateral that provide security for a loan: a smart contract circuit structured to create a smart lending contract for the loan and assign at least a portion of the set of items of collateral to the loan, thereby creating an assigned set of items of collateral; wherein the blockchain service circuit is further structured to record the assigned set of items of collateral to a loan-entry in the distributed ledger, and wherein each of the blockchain service circuit, the data collection circuit, and the smart contract circuit further comprise a corresponding application programming interface (API) component structured to facilitate communication among the circuits of the system.

Abstract: An information processing apparatus (2000) detects that a customer (30) takes up a product from a display area (20), using a captured image (12). The information processing apparatus (2000) generates information (customer information) indicating the acquisition count by the customer (30) in a manner that is associated with an identifier of the customer (30). The information processing apparatus 2000 sets the acquisition count, which is indicated by the customer information on the customer (30), in a case where the customer (30) makes payment for the products, to zero.

Abstract: Systems and methods include processors for receiving training data for a user activity; receiving bias criteria; determining a set of model parameters for a machine learning model including: (1) applying the machine learning model to the training data; (2) generating model prediction errors; (3) generating a data selection vector to identify non-outlier target variables based on the model prediction errors; (4) utilizing the data selection vector to generate a non-outlier data set; (5) determining updated model parameters based on the non-outlier data set; and (6) repeating steps (1)-(5) until a censoring performance termination criterion is satisfied; training classifier model parameters for an outlier classifier machine learning model; applying the outlier classifier machine learning model to activity-related data to determine non-outlier activity-related data; and applying the machine learning model to the non-outlier activity-related data to predict future activity-related attributes for the user activity

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: A personalized insurance (“PI”) computing device for determining an optimal insurance product for a driver operating a vehicle for a transportation network company (“TNC”) during a period of increased demand includes at least one processor in communication with at least one memory. The processor is configured to: (i) receive, from a TNC, data indicating increased demand for transportation services, (ii) retrieve driver data that includes the driver history, (iii) generate an optimal pricing model for the driver based upon the increased demand and the driver data, (iv) execute the model to determine an optimal insurance product having characteristics reflecting at least one risk factor associated with the increased demand for transportation services and a risk profile determined from analyzing the driver data, and (v) transmit an offer to the driver to provide transportation services at an increased earnings rate and with the determined optimal insurance product.

Abstract: It is described at least a method for performing vehicle operation analysis of a driver, comprising retrieving data related to the operation of the vehicle via a mobile device, wherein the mobile device is placed inside the vehicle and the retrieved data is data that is at least provided by data providing units included in the mobile device; adding a driver-specific identifier to the retrieved data which identifies the driver of the vehicle irrespective of the vehicle that is used; performing analysis of the retrieved data in the mobile device, and/or sending the retrieved data to a remote computer and performing analysis of said data in the remote computer, wherein the analysis includes extracting driver-specific vehicle operation schemes and/or calculating a driver-specific safety factor.

Abstract: Systems and methods may analyze property telematics data, with homeowner permission or affirmative consent, to update risk-based coverage of a property, such as a house, during a short-term rental. The systems include a renter analytics computing device, a plurality of property telematics devices at the property, including at least one sensor, and a property telematics controller. The renter analytics computing device is configured to retrieve a pre-rental record including parameters of a short-term rental of the property, and retrieve property telematics data, from the property telematics devices, associated with the short-term rental. The renter analytics computing device may develop a risk level profile associated with the short-term rental and determine an adjusted coverage rate to cover the property during the short-term rental. As a result, a homeowner of the property may be protected against damages incurred to the property during the short-term rental.

Abstract: A computer system for creating driving tournaments for users including a processor in communication with a memory device is provided. The processor may be programmed to: i) receive user data associated with a plurality of users, ii) generate, based at least in part upon the received user data, a driving profile including driving characteristics of each user, iii) match users based at least in part upon the driving profiles of the users, wherein a match occurs when driving characteristics of the driving profiles are substantially similar, iv) create a driving tournament for the matched one or more users, v) receive current vehicle telematics data and current mobile device data associated with the matched one or more users, and vi) determine, after the predetermined time period has ended, a winner of the driving tournament.