Abstract: The present disclosure describes devices and methods monitoring a technology environment. In particular, a computing device including a processor with computer readable instructions to access a plurality of indicators (e.g., variables) that have corresponding stored historical information. The indicators are then used to calculate a summed weights table of relative risk for each time period in the past. The summed weights table is then correlated to a target variable (e.g., a variable that documents major issues, incidents, or disruptions that occurred in the technology environment in the past). The correlation coefficient between the summed weights table and the target variable is then used to implement a machine learning algorithm in order to better determine current risk levels (e.g., relative values that predict issues, incidents, or disruption).

Abstract: The present disclosure describes devices and methods monitoring a technology environment. In particular, a computing device including a processor with computer readable instructions to access a plurality of indicators (e.g., variables) that have corresponding stored historical information. The indicators are then used to calculate a summed weights table of relative risk for each time period in the past. The summed weights table is then correlated to a target variable (e.g., a variable that documents major issues, incidents, or disruptions that occurred in the technology environment in the past). The correlation coefficient between the summed weights table and the target variable is then used to implement a machine learning algorithm in order to better determine current risk levels (e.g., relative values that predict issues, incidents, or disruption).

Abstract: Provided herein are systems and methods for processing unstructured data for insurance pricing. In particular, the data processing systems and methods provided herein may be useful to price simulation for the optimization of insurance pricing. The data processing systems and methods use machine learning algorithms to automatically detect relevant rate factor tables in unstructured data sources, such as regulatory filing documents. The machine learning algorithms then extract rate factor data from the tables and place extracted rate factor data in a structured form, eliminating or reducing the need for manual structured data extraction. In some embodiments, the data processing systems and methods also include an audit interface for receiving one or more changes to the structured rate factor data. In the manner, the audit interface may be used to validate the structured rate factor data obtained via the methods described herein.

Abstract: An event sensing system includes one or more sensor assemblies, each sensor assembly including a housing, a microphone, and an audio signal processor. The audio signal processor is configured to generate one of an event classification, which is indicative of a type of event, and an event characteristic, which is indicative of a severity of an event, from the audio signal. The sensor assembly is configured to transmit event records that contain the generated event data to a cloud infrastructure component. The cloud infrastructure component is configured to calculate the probability of an event occurring at a plurality of locations in a region within a bounded time window based on the event records received from a plurality of sensor assemblies.

Abstract: An event sensing system includes one or more sensor assemblies, each sensor assembly including a housing, a microphone, and an audio signal processor. The audio signal processor is configured to generate one of an event classification, which is indicative of a type of event, and an event characteristic, which is indicative of a severity of an event, from the audio signal. The sensor assembly is configured to transmit event records that contain the generated event data to a cloud infrastructure component. The cloud infrastructure component is configured to calculate the probability of an event occurring at a plurality of locations in a region within a bounded time window based on the event records received from a plurality of sensor assemblies.

Abstract: The present disclosure describes devices and methods monitoring a technology environment. In particular, a computing device including a processor with computer readable instructions to access a plurality of indicators (e.g., variables) that have corresponding stored historical information. The indicators are then used to calculate a summed weights table of relative risk for each time period in the past. The summed weights table is then correlated to a target variable (e.g., a variable that documents major issues, incidents, or disruptions that occurred in the technology environment in the past). The correlation coefficient between the summed weights table and the target variable is then used to implement a machine learning algorithm in order to better determine current risk levels (e.g., relative values that predict issues, incidents, or disruption).

Abstract: An event sensing system includes one or more sensor assemblies, each sensor assembly including a housing, a microphone, and an audio signal processor. The audio signal processor is configured to generate one of an event classification, which is indicative of a type of event, and an event characteristic, which is indicative of a severity of an event, from the audio signal. The sensor assembly is configured to transmit event records that contain the generated event data to a cloud infrastructure component. The cloud infrastructure component is configured to calculate the probability of an event occurring at a plurality of locations in a region within a bounded time window based on the event records received from a plurality of sensor assemblies.

Abstract: The present disclosure describes devices and methods monitoring a technology environment. In particular, a computing device including a processor with computer readable instructions to access a plurality of indicators (e.g., variables) that have corresponding stored historical information. The indicators are then used to calculate a summed weights table of relative risk for each time period in the past. The summed weights table is then correlated to a target variable (e.g., a variable that documents major issues, incidents, or disruptions that occurred in the technology environment in the past). The correlation coefficient between the summed weights table and the target variable is then used to implement a machine learning algorithm in order to better determine current risk levels (e.g., relative values that predict issues, incidents, or disruption).

Abstract: Methods and systems for using vehicle-location information to determine vehicle-usage statistics and using the determined vehicle-usage statistics in an assessment of an insurance discount are described in the present disclosure. In an exemplary embodiment, vehicle-usage statistics are determined solely from received location information, without needing to gather additional information.