It's 2016 and most financial services companies are at least starting to implement a data science capability, here's nine questions to define the maturity of yours.
Posts in insurance
Several years into the fintech revolution, the insurance world is waking up to the disruptive possibilities of new technologies. So what's hype and what's actually useful?
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.
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.
We're recently back from R in Insurance in Amsterdam where we heard several interesting talks, delivered one of our own, and met some really great people.
We're delighted to attend and support R in Insurance this year: a leading international conference for researchers and practitioners of actuarial science and financial data analysis.
There are huge opportunities available to the life insurance industry to apply new statistical modelling techniques, recalling their original role in helping to advance data analysis.
The term 'data science' has been around now for about five years with many explanations, discussions and occasional breathless over-excitement in the technology and business press.