Subscription renewal prediction with a cooperative component

A system, comprising:

• a memory coupled to a computer processor;
• a repository configured to store transactions of an initial plurality of users, and a plurality of open recurring expense sequences each having a plurality of expense sequence attributes;
• and
• a subscription renewal engine, executing on the computer processor and using the memory, configured to:
• detect, in the transactions of the initial plurality of users, the plurality of open recurring expense sequences,
• derive, using the plurality of expense sequence attributes of the plurality of open recurring expense sequences, a plurality of recurring expense groups each comprising a subset of the initial plurality of users,
• generate a first prediction that the open recurring expense sequences of a first recurring expense group of the plurality of recurring expense groups will terminate within a period of a current period,
• group, using personal attributes of the users in the first recurring expense group, the first recurring expense group into a plurality of recurring expense subgroups,
• generate, using a trained model, a plurality of scores for the plurality of recurring expense subgroups each indicating a probability that the open recurring expense sequences of the respective recurring expense subgroup are extendable beyond the current period, and
• select, using the plurality of scores for the plurality of recurring expense subgroups, a first recurring expense subgroup to attempt an extension of the open recurring expense sequences of the first recurring expense subgroup.

A method comprising:

• receiving, from a user and via a graphical user interface (GUI), transactions of the user;
• sending the transactions of the user to a subscription renewal engine performing machine learning and configured to:
• detect, in transactions of a plurality of users, a plurality of open recurring expense sequences each having a plurality of expense sequence attributes, wherein the plurality of users comprises the user,
• derive, using the plurality of expense sequence attributes of the plurality of open recurring expense sequences, a plurality of recurring expense groups each comprising a subset of the plurality of users,
• generate a prediction that the open recurring expense sequences of a first recurring expense group of the plurality of recurring expense groups will terminate within a period of a current period,
• group, using personal attributes of the users in the first recurring expense group, the first recurring expense group into a plurality of recurring expense subgroups,
• generate, using a trained model, a plurality of scores for the plurality of recurring expense subgroups each indicating a probability that the open recurring expense sequences of the respective recurring expense subgroup are extendable beyond the current period,
• select, using the plurality of scores for the plurality of recurring expense subgroups, a recurring expense subgroup to attempt extension of the open recurring expense sequences of the recurring expense subgroup, wherein the recurring expense subgroup comprises the user, and
• send, to the user and via the GUI, a predicted sequence termination message comprising the first prediction and an invitation for the user to receive a subgroup contact list comprising contact information of the users in the first recurring expense subgroup;
• receiving, from the subscription renewal engine and via the GUI, the predicted sequence termination message;
• and
• displaying, in an element within the GUI generated by a computer processor, the predicted sequence termination message.