With over 150 hours of presentations available from ICA2018 in Berlin, we’ve had a look at some of the videos relating to Data Analytics and picked out a handful for suggested viewing.
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We’ve had a look at some of the videos relating to Data Analytics and picked out a handful for suggested viewing:
- In “Digital disruption: The impact of information management on new products and business models for society”, non-actuary Tom Jenkins takes us on a non-technical journey through the data landscape, the sheer volumes of data and the implications the future holds for data models and usage in business with the emergence of “markets of one”. (30 mins)
- In “Using risk factors in insurance analytics: data driven strategies”, Katrien Antonio takes us through a stepwise, technical example of LASSO regression in R. The presentation “unifies penalty-specific (machine learning) literature with statistical (or: actuarial) literature” and shows the construction of an R package for a “multi-type penalized proximal gradient algorithm”. A free online course, Valuation of Life Insurance Products in R, is unveiled at the end. (34 mins)
- In “The transition towards semi-autonomous vehicle insurance: the contribution of usage-based data”, Montserrat Guillen explains Usage Based Insurance (UBI) – in the case of vehicles, Pay-How-You-Drive (PHYD) insurance – and how these are interacting with advanced driver assistance systems (such as speed control); observation of the “learning effect”; and “correction” of the insurance premium using telematics information to complement traditional risk factors. (33 mins)
- In “Actuarial data science versus data protection – update against the background of the General Data Protection Regulation”, Stefan Nörtemann delivers a brief history of data directives in the EU, highlighting key changes that the GDPR brings, and the implications for targeted marketing using data available from various sources and requirements for customer consent. (27 mins)
- In “Application of machine learning in health insurance to reduce claims leakage and improve underwriting”, Satraajeet Mukherjee leads us through a health insurance-specific example with a detailed and informative analysis of machine learning models compared to GLMs. (38 mins)
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