Credit risk stands for the risk of losses caused by unwanted events, such as the default of an obligor. The managing of portfolio credit risks is crucial for financial institutions. The multi-factor Merton model is one of the most widely used tools that modelling the credit risks for financial institutions. Typically, the implementation of the multi-factor Merton model involves Monte Carlo simulations which are time-consuming. This would significantly restrict its usability in daily credit risk measurement. In this report, we propose an FPGA architecture for credit-risk measurements in the multi-factor Merton models. The presented architecture uses a variety of optimization techniques such as kernel vectorization and loop unrolling, to optimize the performance of the FPGA implementation. The evaluation results show that compare to a basic C++ implementation running on a single-core Intel i5-4210 CPU, our proposed FPGA implementation can achieve an acceleration of up to 22 times, with a precision loss of less than 10−8.