DekaBank improves modelling accuracy and risk management with NAG® DCO/C++

DekaBank wanted better risk management, more accurate pricing and to support the bank’s expanding derivatives business, all without increasing computing costs. That’s when they turned to automatic differentiation (AD), and in particular adjoint automatic differentiation (AAD). After comparing three tools, DekaBank chose NAG’s AD solution, NAG® DCO/C++.

Complete the form below to download the case study and learn how DekaBank achieved a 95% reduction in compute requirements using AAD to reduces costs and improves risk management.

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