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SYSTEMS AND METHODS FOR UNSUPERVISED DETECTION OF ANOMALOUS CUSTOMER INTERACTIONS TO SECURE AND AUTHENTICATE A CUSTOMER SESSION

Abstract:

A system includes memory devices storing instructions, and one or more processors configured to execute instructions performing method steps. The method may include self-supervised training of a bidirectional recurrent neural network (RNN) model to enable reconstruction of an input vector from a global latent vector. The training may optimize parameters for real-time anomaly detection of customer interactions. The global latent vectors may comprise encrypted representations of customer behavior. After training, the system may produce optimized vector embeddings to represent customer behavior using a trained encoder of the model. The encoder may encrypt vectors in real-time for each customer session and determine a security measurement between vectors associated with previous customer sessions and a current session. Based on the security measurement, the system may require an additional security action from a customer to authenticate a customer session. The system may retrain and optimize the model in a self-supervised manner.