Quantum Risk Modeling Redefining Actuarial Science with Quantum Algorithms

Abstract

The synergy of actuarial science and quantum computing has become a change in the paradigm of how risk is being modeled, quantified and predicted. Conventional actuarial approaches are effective, but they are computationally expensive and their capability of handling high-dimensional data, complex correlations and fast-changing financial ecosystem are all subject to the inherent limitations. The exponential computational advantages offered by quantum algorithms not attainable by classical systems cannot be ignored, and make good use of the superposition, entanglement, and quantum parallelism benefits. This paper discusses how quantum computing can transform actuarial practices via superior risk modeling frameworks particularly in relation to quantum algorithms like Quantum Amplitude Estimation, and the Harrow Hasidim Lloyd (HHL) algorithm. A quantum/classical hybrid architecture is proposed to combine quantum computation with existing actuarial processes to more accurately model situations in the field of life insurance, pension fund projection, and catastrophic risk modeling. Experimental results point towards the possibility of speedups over classical methods as well as better accuracy, and they consider the limits that are posed by the present hardware and the size of the problems scale. These results indicate that not only quantum-enhanced actuarial science has the potential to offer better computational efficiency but that it also forms the opening of more robust, dynamic, and secure actuarial science in the post-quantum world.