Take the 2-minute tour ×
Quantitative Finance Stack Exchange is a question and answer site for finance professionals and academics. It's 100% free, no registration required.

Actuaries (at least in Europe) are frequently severily lacking in quant finance topics. At best they are familiar with B&S model.

People going into quant finane or striving to become a quant on the other hand are often not aware that their knowledge could also be applied in an insurance context.

Classic quant-work-related topics are: options pricing, portfolio optimisation, credit risk. This topics are also ery relevant to insurers. The example below shows why. I will also add more examples later on.

Example (portfolio optimization) Consider a car insurer's portfolio. Such a portfolio consists of many individual contracts. Premium calculation is based on the equivalence principle. Premiums paid by the insured must at least cover the losses. Thus such a portfolio can generate positive or negative annual returns. Positive if premiums paid > losses, Negatative if premiums paid < losses. Also these returns change over time and are volatile. The portfolio also has a market value - even though the market is not nearly as liquid as the one for standard derivatives and the bid/ask spreads can be huge. Still, one can interpret one such portfolio as a stock with possible negative dividend.

A reinsurance company often holds fractions of such portfolios. Thus to optimize the potfolio structure they can apply portfolio-optimization theory. As far as I know there are even a couple of reinsurers out there that actually do that.

Literature: (some books and papers to showcase the interfacing of actuarial evaluation and derivatives pricing techniques)


  1. Literature suggestions on the application of option pricing, portfolio optimization etc. to insurance related topics
  2. Further examples as the one above
  3. What could quants working for banks/funds learn from actuaries ?
share|improve this question
In HFT, not much. SAS/R is of limited use (SAS is of no use since no firm will have a license). The data mining education is lacking compared to people from a machine learning or straight applied stats background. Risk tools aren't all that applicable. Machine learning and software guys are better on the simulation side which is the important thing for market risk. But a top actuary could still do the work, just most of what they learnt would not be useful. –  user2763361 Mar 29 '14 at 12:52
okey - I did forget about HFT - it also does not make much sense in an insurance context ^^ –  Probilitator Mar 29 '14 at 17:54
Be sure to check an excellent overview by Moller ( “On Valuation and Risk Management at the Interface of Insurance and Finance"). It could be slightly outdated though.. –  Jakøb H. Mar 30 '14 at 1:19
The activity of insurance companies is constrained by regulations that specify actuarial limits and actuarially defined capital reserves, not "quantish" distribution or mitigation of risk. Until this changes, the mathematical basis of the insurance is likely to remain distinct from that used in the rest of the financial industry. –  Michael Stern May 23 '14 at 13:54
@MichaelStern I am well aware of that. At the same time I disagree with you. Nowadays (at least in Europe) modelling of insurance business (above all life insurance and pension portfolios) has become very sophisticated. Modelling of early exercise options, customer surrender bahaviour, modelling of the assets etc. –  Probilitator May 23 '14 at 14:46

Your Answer


By posting your answer, you agree to the privacy policy and terms of service.

Browse other questions tagged or ask your own question.