Abstract: A method for providing real-time analytics of time series data is disclosed. The method includes capturing a first set of time series data from a data flow based on a first predetermined time period; parsing the first set of time series data to identify a first key metric; capturing a second set of time series data from the data flow based on a second predetermined time period; parsing the second set of time series data to identify a second key metric, the second key metric corresponding to the first key metric; comparing, in real-time, the second key metric with the first key metric; and identifying, by using a model, an anomaly based on a result of the comparing and a predetermined threshold.

Abstract: A method for fraud detection and management may include a fraud detection computer program: receiving data from a plurality of sources, each data associated with a unique identifier; normalizing the data; modeling the normalized data with a trained machine learning data model; extracting features or attributes from the modeled data; generating one or more sets of weights for the features or attributes; identifying a subset of the features or attributes indicative of fraud based on the weights; enriching the subset of the features or attributes; detecting fraud based on the enriched subset of the features or attributes; and notifying one or more subscribing institutions of a fraud event for the detected fraud based on the validated subset of the features or attributes.