How can life insurers address low persistency? How can data and analytics help?
We will demonstrate, with the use of a practical case study how the full cycle of actuarial analysis is evolving - from data collection and data enhancement, feature engineering, modelling, verification and ultimately application and communication.
We will provide an example application of data science applied to actuarial work including:
• Data preparation
• Model fitting
• Interpretation of results
• Performance of fitted data science model relative to one or two alternative models.
The case study provides an introduction to generalised linear modelling and its use as a predictive
modelling technique within insurance. We used open source programming software.
We will also touch on the potential to use external data, together with insurance companies data to
build an enhanced persistency model of their book of contracts.
A huge increase in data generation, data capture and data storage combined with significantly increased computing power is providing insurers with a unique opportunity to re-evaluate the value that their data can provide; and the technologies available to do that.
Methods used in non-life pricing are evolving at a fast pace and more advanced actuarial and statistical techniques are being used in pricing, competition analysis and profitability analysis.
A huge increase in data generation, data capture and data storage combined with significantly increased computing power is providing insurers with a unique opportunity to re-evaluate the value that their data can provide.
Improvements in computational power has given rise to the use of data science and artificial intelligence techniques in a wide variety of areas, including finance, driverless cars, image detection, speech recognition etc. This is directly impacting business practices within the financial services through its application within banking, insurance and asset management.
In the current economic climate, insurers need to improve their processes continually – making them more efficient and cost-effective while maintaining the agility to deal with new requirements. At the same time, technological change is providing new ways of achieving these objectives. In particular, the term ‘robotics’ appears to be used everywhere, and it is important to grasp the impact and potential use of these new technologies.
Listen to Valerie du Preez, founder and Managing Director of Actuartech, on the panel for Legerity's IFRS 17 webinar discussing, "Can IFRS17 help deliver digital transformation?".
In a world of high volume and varied datasets, data science techniques can add valuable tools to an actuary’s toolkit to provide actionable insights from data.
Sophisticated machine learning models have the potential for high predictive accuracy but their complexity may sometimes result in black box models, which, in some cases, may appear to be a trade-off between accuracy and interpretability.
Sign up to our newsletter for the latest in Actuarial technology