Abstract: Platform behavior may be adapted via machine-learning-based entity lifecycle monitoring. A web crawler agent collects data comprising an entity identifier token. A machine learning model is trained to determine, based at least in part on the entity identifier token, whether a corresponding entity is associated with the computing platform (e.g., whether the corresponding entity is a platform subscriber entity for the computing platform). Based on the output of the machine learning model applied to the entity identifier token (in some embodiments, in combination with other relevant data parsed from the collected data), an indication of an entity lifecycle status and a confidence value therefor are determined. Based on the entity lifecycle status and the confidence value, a listener is bound to the platform subscriber entity. The listener monitors activity of the platform subscriber entity with respect to the platform and identifies platform action to take in response.

Abstract: Systems and methods of the present disclosure include computer processing resources to enable automated detection of recurring data records for predicting recurring and future data records including receiving a data record history comprising historical data records having a quantity attribute, a date attribute, and an entity attribute. A record pattern machine learning model engine is utilized to identify a recurring data record pattern including a cadence of recurrence for a set of recurring data records of a particular entity based on historical data records. The most recent historical data record of the recurring data records is determined and a recurrence prediction service is utilized to predict of a future data record based on the cadence and the most recent historical data record. A future data record recommendation is generated for a future action that matches the quantity attribute and of the entity attribute of the recurring data records.