TECHNICAL FIELD

The present invention generally relates to systems and methods for generating consumer mobility profiles, in particular, for training and/or creating mobility profiles with respect to users.

BACKGROUND

Consumers have been reluctant, historically, to share large amounts of data with companies, even for the purpose of receiving enhanced service. The type of data users have been willing to share has varied over time, and generally, users' hesitance to share personal information and location/movement data has remained constant. The historical lack of quality user data has blunted the effectiveness of campaigns intended to appeal to prospective customers looking for a more connected and personalized banking and insurance experience. However, new research has shown that younger users are appreciably more willing to share all types of data with companies, particularly in exchange for improved and/or personalized service.

User data collected may create a better understanding of each consumer, including by allowing multiple users to be distinguished in a way that may not be possible with traditional data collection practices, where only limited information may be known about each respective user. Newfound user willingness, combined with the application of data modeling algorithms across and among many categories of user data, may provide companies with unprecedented insights into their users' behaviors, preferences, and the risks attendant to those customers. By having a deeper understanding of user behavior, it may be possible for companies to offer rewards to customers who act to decrease risks. Therefore, there is a need for methods and systems for generating user mobility profiles, to provide a more personalized experience to users and to allow service providers to apply findings from the data for the benefit of the user by dynamically learning a user's preferences and opportunities to reduce risk, along with individual user information to help the user navigate life more effectively, efficiently, and intuitively.

BRIEF SUMMARY

In one aspect, a computer-implemented method of generating a user mobility profile includes receiving, in a remote computing device, mobility data associated with the user; storing, in an electronic database, the mobility data associated with the user; generating a machine learning mapping; analyzing, using the generated machine learning mapping, a portion of the mobility data to produce an output corresponding to the portion of the mobility data; generating, based on the output, information corresponding to the user; transmitting the generated information to a display device of the user; and displaying, in the display device, the generated information.

In another aspect, a computing system includes one or more processors; and one or more memories storing instructions that, when executed by the one or more processors, cause the computing system to receive, in a remote computing device, mobility data associated with the user. The one or more memories may store further instructions that, when executed by the one or more processors, cause the computing system to store, in an electronic database, the mobility data associated with the user; generate a machine learning mapping, analyze, using the generated machine learning mapping, a portion of the mobility data to produce an output corresponding to the portion of the mobility data; generate, based on the output, information corresponding to the user; transmit the generated information to a display device of the user; and display, in the display device, the generated information.

In yet another aspect, a non-transitory computer readable medium containing program instructions that when executed, cause a computer to receive, in a remote computing device, mobility data associated with the user. The non-transitory computer readable medium may include further instructions that when executed, cause a computer to store, in an electronic database, the mobility data associated with the user; generate a machine learning mapping, analyze, using the generated machine learning mapping, a portion of the mobility data to produce an output corresponding to the portion of the mobility data; generate, based on the output, information corresponding to the user; transmit the generated information to a display device of the user; and display, in the display device, the generated information.

