KNOW MORE ABOUT SOLVENCY CAPITAL REQUIREMENTS & REMITRIX

WHAT ARE SOLVENCY CAPITAL REQUIREMENTS?

A company’s Solvency Capital Requirement (SCR) is the amount of its own funds it is legally required to keep on hand so that it can be confident that even in the most extreme scenario it can cover all losses.

WHO IS SUBJECT TO SCR?

All members of the EU are subject to Solvency II regulations. Solvency II regulations require member states to hold onto enough funds to be 99.5% confident they can weather the most extreme loss scenario. Many other countries have developed fully or partially equivalent laws – including the US, Canada, Switzerland, Australia, Brazil, Bermuda, and Mexico. SCR regulations also exist in Japan, Taiwan, South Korea, China, Hong Kong, Vietnam, Thailand, Indonesia, Malaysia, and Singapore.

HOW DO INSURERS KNOW THEIR SCR?

They don’t. Not exactly, anyway. Calculating the SCR is a very difficult and time-consuming process. It involves complicated calculations to estimate the range of potential risk in the underwriting of life, non-life, and health insurance as well as many other factors. Insurance companies can used models created by regulators, which are for obvious reasons quite conservative and force them to freeze up more of their own free funds than might be necessary, or they can calculate their SCR themselves based on their own internal models. This is what the major insurance houses do today, pouring time and money into the project. Their actuaries must analyze the vast amounts of data and do their best to estimate how much of their own funds they need to keep on hand in order to be compliant. Once again, you can see how they would err on the side of caution, assuming more risk rather than less so the company is compliant even if their risk prediction is slightly off.

HOW CAN REMITRIX HELP?

RemitRix uses the power of Machine Learning (ML) to help insurers optimize their SCR. ML can be a wonderful tool for actuaries, allowing them to determine SCR far better than with traditional modeling. Not only can the computer analyze big data much faster than human beings can, it can also predict risk with much more accuracy. More accurate predictions of risk means insurers can do a better job determining how much of their funds to keep on hand to stay compliant. That way they don’t overshoot and let their own free funds sit around frozen when they could be used for something else, or (less often) undershoot and end up in trouble for not having enough funds to manage their risk. To learn more about why ML can process more data more efficiently and predict more accurately than human beings using traditional modeling, check out our bit on ML!

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