Life event bank ledger

A method, comprising:

• identifying, by a computing system, a plurality of transactions associated with a plurality of users;
• anonymizing, by the computing system, the plurality of transactions;
• generating, by the computing system, a plurality of synthetic transactions configured to mimic real transactions;
• generating, by the computing system, a prediction model to identify groups of related transactions by:
• training, by the computing system, the prediction model to identify, based on the plurality of transactions and the plurality of synthetic transactions, a subset of transactions related to each other;
• testing, by the computing system, the prediction model to identify a success ratio of the training, wherein the success ratio corresponds to a threshold level of accuracy;
• and
• retraining and retesting, by the computing system, the prediction model until the prediction model groups transactions in accordance with the threshold level of accuracy;
• retrieving, by the computing system, a set of target transactions associated with a target user;
• grouping, by the computing system via the prediction model, one or more transactions of the set of target transactions into a cluster;
• and
• associating, by the computing system, a life event to the cluster of the one or more transactions.

A system, comprising:

• a processor in communication with one or more client devices associated with one or more users;
• and
• a memory having programming instructions stored thereon, which, when executed by the processor, performs operations comprising:
• anonymizing a plurality of transactions associated with a plurality of users;
• generating a prediction model to identify groups of related transactions by:
• training the prediction model to identify for a respective user, based on the anonymized plurality of transactions, a subset of the respective user's transactions that are related to each other;
• testing the prediction model to identify a success ratio of the training, wherein the success ratio corresponds to a threshold level of accuracy;
• and
• retraining and retesting the prediction model until the prediction model groups transactions in accordance with the threshold level of accuracy;
• retrieving a set of target transactions associated with a target user;
• grouping, based on the prediction model identifying relatedness to each other, one or more transactions of the set of target transactions into a cluster;
• and
• associating a life event to the cluster.