Milliman announced that it has expanded its data science and artificial intelligence (AI) capability by taking on the data science consulting team of Ortec Finance.
The new team, in combination with Milliman’s existing data science competence, will continue to support its current customers with data science consultancy projects in the Benelux to generate value from their structured and unstructured data, and to advise them on their strategic journey to become data-driven. Ortec Finance will concentrate on the use of data science in their software solutions.
“Due to digitization, companies across a wide range of industries see their data volume explode. This, in combination with rapid progress in AI technology, leads to a substantial demand for consulting services in this field. We are at the forefront of the era of AI disruption,” says Raymond van Es, who will serve as Lead Data Science & AI of Milliman in the Benelux.
Peter Franken, Milliman Principal, sees it as an important step in strengthening Milliman’s predictive analytics and modelling capabilities in the Benelux. “Having Raymond and his team joining allows us to accelerate expanding our service offering outside the financial industry as well as our technical capabilities in the area of data science and predictive analytics.”
Data science is a term given to the broad array of activities used to gain insight and extract value from existing data sources, including techniques such as data analytics, predictive analytics, machine learning, data mining and artificial intelligence. The use of data science techniques enables the extraction of value from increasingly diverse sources of data.
The cost of storing data has been falling exponentially for decades, and many companies have started storing lots of potentially valuable internal data. Computing speeds have been increasing exponentially for decades, and data analysis software has been steadily improving, making it feasible for companies to analyse and gain insight from these larger data sets.
In this briefing note, Milliman’s Donal McGinley, Bridget MacDonnell and Eamon Comerford provide a high-level overview of how insurers can make better use of internal data to gain insight and drive competitive advantage.
Traditionally, insurers have relied heavily on data they have collected as well as industry-specific data to inform their business decisions and strategy. However, data science techniques have become more sophisticated, allowing insurers to better understand the relationship between internal and external data sources. Predictive analytics, machine learning, data mining, and artificial intelligence are helping companies extract value from both sources.
In this article, Milliman’s Cormac Gleeson and Eamon Comerford discuss how the use of external data can complement a company’s wider data science initiatives. They also explore some of the challenges posed by working with external data.
The visibility of climate change’s impact on property hazard is increasingly leading individuals and their chosen leaders to ask: how might an increase in hazard affect the desirability of living in various communities and how do we manage the socioeconomic effects? Recent stories have highlighted the concerns of “climate gentrification,” or potential migration from low-lying but relatively well-off areas to areas of higher elevation but sometimes higher poverty.
Milliman has worked with Jupiter Intelligence, a climate risk analytics provider of forward-looking and probabilistic hazard data for future conditions, to develop a framework for analysis that may spark insight for community leaders in the public and private sectors who are charged with managing climate change and planning for a resilient future. This paper by Molly Barth and John Rollins investigates insurance risk, consumer costs, and resilience incentives under the stress of a changing climate in Broward and Miami-Dade counties.
In May 2019, the European Insurance and Occupational Pensions Authority (EIOPA) published its thematic review on the benefits and risks arising from the use of Big Data Analytics (BDA) in health and motor insurance. This briefing note by Milliman’s Matthew McIlvanna, Eamonn Phelan, and Eoin O Baoighill summarises EIOPA’s report. The note explores the types of big data and predictive analytics tools currently being used in the motor and health insurance industry and some of the challenges insurers have faced. While EIOPA’s report focuses solely on motor and health insurance, the findings of its review have much broader relevance to other types of insurance.
Predictive modeling has emerged as a field that requires judgment at nearly every step. What are the best ways to implement predictive modeling into relevant areas of actuarial practice? This Society of Actuaries paper by Milliman consultants provides more perspective.