Abstract
Novel approaches employing an Artificial Neural Networks to enhance the infrastructure of existing Monte Carlo Risk engines are presented. An Artificial Neural Network is utilized to retrieve trade- and market data from existing Expected Exposure profiles of interest rate swaps which enables its usage as part of data control frameworks and exposure explain applications. An Artificial Neural Network is also utilized to predict Expected Exposure mimicking a Monte Carlo Risk engine showing similar accuracy at faster speeds of execution.
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