## R in Insurance Conference returns to London for 2016

Once again we're delighted to attend and support this year's R in Insurance; a leading international conference for practitioners of actuarial science and financial data science.

Our use of the R statistical environment for data science

Once again we're delighted to attend and support this year's R in Insurance; a leading international conference for practitioners of actuarial science and financial data science.

In the final post of this technical series on derivatives trading we discuss using options as insurance, why the Black-Scholes model is wrong and trader psychology.

In the third post in this series on volatility and options trading we investigate the effects and trading implications of input behaviours on the option price.

Continuing our series on options and options trading, we focus on the behavioural patterns associated with options prices and how non-linear behaviour is an important consideration.

In this series we cover options: a deceptively complex trading instrument that provide an entirely different type of insurance - against directional moves in financial markets.

We finish our series on Bayesian networks by discussing conditional probability, more complex models, missing data and other real-world issues in their application to insurance modelling.

We continue our series on Bayesian networks by discussing their suitability for fraud detection in complex processes: for example assessing medical non-disclosure in life insurance applications.

Bayesian networks are useful tools for probabilistically computing the interdependencies and outcomes of real-world systems given limited information. Here we describe their use in fraud detection.

Technology moves incredibly quickly these days and practical learnings only slowly formalised into academic courses; fortunately there's many semi-professional and purely social ways to keep up.

Real data is often unavailable for creating demos, learning and especially for publishing. Here we describe methods to generate realistic artificial data which has fewer constraints.

In the final article of this technical series we generate outputs to estimate the value of mortality swaps and discuss the concept of pricing tail risk.

In the second article of this technical series we put the pieces together to begin estimating the value of a mortality swap, an esoteric insurance derivative.

This series discusses a novel approach to modelling mortality swaps: an esoteric derivative insuring against the risk of life-contingent assets like annuities and life insurance policies.

In the final article of this series we investigate the behaviours of different distribution distance metrics to let us automatically determine the scale of the change.

In the second article of this series we continue our technical discussion on using Bayesian analysis to probabilistically estimate the effect of a business process change.

In this series of articles we'll discuss the use of probabilistic modelling to help efficiently evaluate changes to business processes and the success of marketing campaigns.