Abstract: Systems and methods are provided for receiving a request for lookalike data, the request for lookalike data comprising seed data and generating sample data from the seed data and from user data for a plurality of users, to use in a lookalike model training. The systems and methods further provide for capturing a snapshot of social graph data for a plurality of users and computing social graph features based on the seed data and the user data for the plurality of users, training a lookalike model based on the sample data and the computed social graph features to generate a trained lookalike model, generating a lookalike score for each user of the plurality of users in the user data using the trained lookalike model, and generating a list comprising a unique identifier for each user of the plurality of users and an associated lookalike score for each unique identifier.

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: Systems and methods are provided for receiving a request for lookalike data, the request for lookalike data comprising seed data and generating sample data from the seed data and from user data for a plurality of users, to use in a lookalike model training. The systems and methods further provide for capturing a snapshot of social graph data for a plurality of users and computing social graph features based on the seed data and the user data for the plurality of users, training a lookalike model based on the sample data and the computed social graph features to generate a trained lookalike model, generating a lookalike score for each user of the plurality of users in the user data using the trained lookalike model, and generating a list comprising a unique identifier for each user of the plurality of users and an associated lookalike score for each unique identifier.

Abstract: Systems and methods are provided for receiving a request for lookalike data, the request for lookalike data comprising seed data and generating sample data from the seed data and from user data for a plurality of users, to use in a lookalike model training. The systems and methods further provide for capturing a snapshot of social graph data for a plurality of users and computing social graph features based on the seed data and the user data for the plurality of users, training a lookalike model based on the sample data and the computed social graph features to generate a trained lookalike model, generating a lookalike score for each user of the plurality of users in the user data using the trained lookalike model, and generating a list comprising a unique identifier for each user of the plurality of users and an associated lookalike score for each unique identifier.

Abstract: Systems and methods are provided for detecting target object(s) within image(s) based on selective template matching. More specifically, the systems and methods relate to template generation, selection and matching based on the identification of regions of interest within image(s). Training images showing target object(s) can be obtained and regions of interest that are deemed more likely to contain part(s) of the target object can be identified based on the training images. Subsequent to the identification of regions of interest, templates for target object detection can be generated based thereon. Templates can be applied on testing images. Based on the test application of templates, a subgroup of templates can be selected to serve as a basis for target object detection in subsequent images.