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.
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.
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.
Over the past year we've refined this simple model to help map, evaluate and improve our clients' data science capabilities, it might work for you too.
In the final article of this technical series we demonstrate hierarchical linear regression using PyMC3 to compare vehicle NOx emissions for a range of car manufacturers.
In the second article of this technical series we demonstrate the flexible syntax of PyMC3 with regularized linear modelling of car emissions data and model evaluation.
Bayesian inference bridges the gap between white-box model introspection and black-box predictive performance. This technical series describes some methods using PyMC3, an inferential framework in Python.
To follow our post on technical user groups, here's a hat-tip to meetups & conferences throughout UK, Ireland and Europe that we enjoyed attending in 2015.
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.
Practical data science projects often include an aspect of anonymisation to carefully remove sensitive information prior to analysis; here we demonstrate several complimentary techniques and principles.