DETECTING LIFE EVENTS BY APPLYING ANOMALY DETECTION METHODS TO TRANSACTION DATA

Machine learning-based anomaly detection methods are used to identify a change in a users streaming transaction data. If a threshold level of change in the user's transaction data is detected, the user is then identified as potentially having experienced a life event. Then, after a user is identified has having potentially experienced a life event, individual user transactions are processed and analyzed to determine the specific life event the user has most likely experienced. The user is then identified as having experienced the identified specific life event. This information is then used to customize the interactions between the user and the data management system such as questions asked of the user, forms or displays provided to the user, or offers made to the user.