Updated: May 28

By Ben Pryke
New deep hedging research by Beacon Platform points at reduced reserves and higher returns on equity for life insurers’ variable annuity portfolios. Deep hedging is a new machine learning technique that enables more efficient hedging of real-world market risks than traditional risk-neutral hedging.
Variable annuities are popular tax-deferred retirement vehicles that expose customers to desirable market risks. Insurers are required to hold reserves against their variable annuity liabilities, which they calculate using Monte Carlo simulations over their portfolios. But the various guarantees commonly offered on policies mean there is typically no closed form analytical solution, forcing insurers to run nested stochastic simulations taking days or even weeks of compute time.
Deep hedging offers not only a way to avoid the nested stochastics problem but also, by training a neural network to minimize the capital reserve, hedges more optimally than risk-neutral hedging would dictate.
In this whitepaper, we demonstrate that the deep hedging approach is able to strategically under-hedge, locking in expected positive equity drift during the term of the annuity. The flexibility of using deep learning to estimate hedge notionals means that when average equity returns are low, the neural network will mimic analytical risk-neutral deltas, but as equity returns increase, deep hedging delivers a superior strategy that balances risk reduction with the long equity position of the insurer.
The ability to take advantage of and research cutting-edge techniques like deep hedging requires immediate access to flexible compute infrastructure, data science tools, and a shared development platform. A robust reserve calculation in production additionally requires access to data, analytics, and reporting tools.
Beacon’s enterprise technology platform has all these components, organized end-to-end to help actuaries, quants, and data scientists experiment at scale and deliver value to their business quickly while still satisfying enterprise controls.
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