Methods and systems for detecting suspicious or non-suspicious activities involving a mobile device use

A system comprising:

• one or more processors of a computing system;
• and
• at least one non-transitory computer readable medium storing instructions which, when executed by the one or more processors, cause the one or more processors to perform operations comprising:
• receiving contextual data from one or more sensors of a plurality of devices associated with one or more users;
• aggregating the contextual data to form a training dataset;
• inputting the training dataset into a machine learning model;
• determining a transfer of at least one device of the plurality of devices and one or more patterns associated with non-suspicious or suspicious activities using the machine learning model trained on the inputted training dataset, wherein the trained machine learning model is updated based on real-time contextual data from the one or more sensors;
• validating an accuracy of the machine learning model utilizing a validation dataset;
• and
• deploying the machine learning model to the at least one device for detecting the non-suspicious or suspicious activities during an electronic transaction.

A non-transitory computer readable medium, the non-transitory computer readable medium storing instructions which, when executed by one or more processors of a computing system, cause the one or more processors to perform operations comprising:

• receiving contextual data from one or more sensors of a plurality of devices associated with one or more users;
• aggregating the contextual data to form a training dataset;
• inputting the training dataset into a machine learning model;
• determining a transfer of at least one device of the plurality of devices and one or more patterns associated with non-suspicious or suspicious activities using the machine learning model trained on the inputted training dataset, wherein the trained machine learning model is updated based on real-time contextual data from the one or more sensors;
• validating an accuracy of the machine learning model utilizing a validation dataset;
• and
• deploying the machine learning model to the at least one device for detecting the non-suspicious or suspicious activities during an electronic transaction.