Abstract: A method for utilizing a graph path cache to facilitate real-time data consumption by a plurality of machine learning models is disclosed. The method includes receiving an input from a source, the input relating to a request to characterize a data element; retrieving a data attribute that corresponds to the data element from a data management system; determining, in real-time using the graph path cache, a graph attribute that corresponds to the data element by performing deep link analysis on a graph database; executing, in real-time, a model by using the data attribute and the graph attribute, the model corresponding to the request in the input; and transmitting, in real-time, a result of the executed model to the source in response to the input.

Abstract: A method for utilizing a graph path cache to facilitate real-time data consumption by a plurality of machine learning models is disclosed. The method includes receiving an input from a source, the input relating to a request to characterize a data element; retrieving a data attribute that corresponds to the data element from a data management system; determining, in real-time using the graph path cache, a graph attribute that corresponds to the data element by performing deep link analysis on a graph database; executing, in real-time, a model by using the data attribute and the graph attribute, the model corresponding to the request in the input; and transmitting, in real-time, a result of the executed model to the source in response to the input.

Abstract: According to an embodiment of the present invention, an automated computer implemented system determines counterparty credit risk calculations. The system comprises a memory component; and a calculation engine programmed to: generate a computation graph of calculation components; build mappings of how each calculation component is connected; and perform path loop calculations where data is shuffled at each stage based on the mappings.