System and method for profiling and predicting customer behavior

A system for profiling and predicting customer behavior, comprising:

• a computer server comprising instructions that when executed by one or more processors effectuate operations comprising:
• retrieving, via a system database, data of a customer from a plurality of data sources for the customer;
• processing, via a data harmonization engine, data fed by the system database, generating a harmonized single view per entity by standardizing features comprising entity's demographic, behaviors, needs, and preferences;
• feeding, via a deep learning engine, the features into a deep learning ensemble comprising convolutional neural network (CNN), deep reinforcement learning, and recurrent neural network (RNN), and training the deep learning ensemble to generate a set of customer genome markers defining customer characteristics and suggested actions for the customer and;
• providing, via a data science engine, the subset of explanatory features for every customer action using an information-theoretic predictive modeling technique based on coalition game theory;
• transmitting the customer genome markers to a prediction engine to generate a recommendation for a next best action for a target consumer, wherein the prediction engine uses deep reinforcement learning to penalize or reward itself in case a prediction underperforms or beats a previous prediction cycle;
• providing, via a web application and an application program interface (API), a user interface;
• and
• enabling, via a master controller, programmable control of the system database, the data science engine, and the deep learning engine;
• wherein the master controller is configured to create a schedule for scheduling data retrieving, transformation, validation, and training;
• and
• wherein the deep learning engine collects feedbacks as to whether the customer has taken suggested actions, wherein the feedbacks are used by the deep learning ensembles to improve predictions.