Performing analytics for risk management purposes is applied in many fields, especially in financial services. We present a framework for accelerated risk analytics and show a large-scale financial sector application where this framework is used to run backtesting algorithms on risk-based securities such as options. These applications require highly computationally-intensive operations on extremely large data sets with objects numbering in the tens of billions. Intel FPGA and FinLib library for financial applications are used to offload the computation; however, another challenging problem (that we have resolved) is how to feed the data to the FPGA at the optimal speed without having to do customized coding. A combination of Apache Spark along with Levyx’s persistent dataframes are used to address this problem. These dataframes allow absorbing the computation from Spark and offloading it to Finlib in an automated way. This example can be expanded to many other areas of Risk Management such as Insurance and Cybersecurity.