In the insurance industry, maintaining adequate cash reserves to meet claims, known as solvency risk or capital adequacy, is not just a regulatory requirement—it's a critical element of financial stability. Under the Solvency II framework, insurers face complex challenges: they must handle a multitude of variables, adapt to changing risk distributions over time, and manage extensive computational demands. This complexity often renders advanced internal model development feasible only for the largest insurers. Moreover, many insurers tend to overcompensate to meet these requirements, only realizing the extent of this overcompensation retrospectively, with figures ranging from 400% to 1000%. Historically, the "Standard Formula" set by the Prudential Regulatory Authority was meant as a temporary measure, but has become a long-standing benchmark across the industry.
As noted in the Annals of Actuarial Science, effective management of solvency risk necessitates continuous and ideally real-time monitoring. However, the application of Bayesian analysis in this field, while successful in estimating risk measures, falls short in enabling real-time updates. The use of Markov Chain Monte Carlo (MCMC) methods in Bayesian models complicates the possibility of real-time predictive adjustments, limiting their practical utility in dynamic market conditions.
Intellegri’s software revolutionizes this aspect of insurance risk management by integrating the Big Hypotheses Model, developed at the University of Liverpool, with cutting-edge computational technology. This allows for real-time data processing and updating of risk measures without the typical constraints found in traditional Bayesian models. Our platform simplifies and accelerates the risk assessment process, enabling insurers to:
By deploying Intellegri’s software, insurers can not only meet regulatory demands more efficiently but also enhance their financial planning and risk management strategies, leading to better overall performance and stability in the insurance market.