Tag Archives: data analytics

Milliman to expand data science and AI capability in the Benelux

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.”

The challenges of accelerating digital transformation within France’s insurance industry

The insurance industry is facing analytics challenges.  As a result of accelerating digital transformation, insurers are adapting their working methods and rethinking strategic priorities.

The current economic and health crisis has accelerated change that had already begun. Most French companies have experienced disruption, and all are imagining their “post-COVID-19” period. This entails adjusting digital strategies, prioritising specific analytics use cases and adapting work processes.

Strategies such as customer experience enhancements or process optimisation are not new but are now at the forefront of this transition. Digitalisation also affects the organisation of insurers’ daily work, including redefinition of corporate culture and management methods.

In this paper, Milliman’s Floriane Moy discusses the acceleration of digitalisation and its challenges along with the analytics strategies of insurers.

Milliman and Enova announce strategic alliance providing next generation advanced analytic solutions to life insurance industry

Milliman and Enova Decisions, a leading financial technology and analytics company, today announced a strategic alliance aimed at bringing new advanced analytics to insurers. The alliance brings together Milliman’s deep domain expertise and data resources and Enova Decisions’ real-time analytic capabilities, giving insurers an innovative platform for retaining customers, optimizing sales operations, and maximizing the value of current and future customers.

“Insurers everywhere are competing on the basis of who can best understand consumer behavior, and this competition is driven by increasingly sophisticated analytics,” says Sam Nandi, Milliman Principal and Consulting Actuary. “With Milliman’s deep subject matter expertise and Enova Decisions’ decision management platform, we give insurers the best tools to succeed in all the different dimensions of their business, whether it’s sales, distribution management, customer targeting, product development, or myriad other applications.”

“Leaders in highly regulated industries know that proper management of data for security and privacy is critical,” said Joe DeCosmo, Chief Analytics Officer for Enova Decisions. “By working with Enova Decisions, insurers can leverage our 15 year of experience making data-driven decisions in real time and access the latest decisioning technology while remaining compliant.”

The Milliman/Enova Decisions strategic alliance will provide life insurance clients with the opportunity to achieve strategic targets and business goals through the activation and operationalization of integrated,  data intelligence-driven solutions.

How to lower defense costs using analytics (infographic)

For medical malpractice insurers, market pressures continued in 2018 despite overall profitability, according to a May report by AM Best. One way to combat potential headwinds is by lowering defense costs using advanced analytic techniques. In 2017, NORCAL Group began using Milliman’s Datalytics-Defense®, which uses proprietary data-mining techniques to analyze companies’ defense cost invoices and produce actionable insights. The results from the case study shown in the infographic below demonstrate the extent NORCAL was able to reduce its defense costs, all the while maintaining its overall claims-with-payment ratio.

Exploring EIOPA’s thematic review on big data analytics in insurance

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.

How can data science enhance reserving analyses for insurance companies?

The benefits of data science are typically aligned along two complementary dimensions: streamlining of processes and deep understanding of risks. Analytics and data science have transformed actuarial pricing and are now being used within reserving. Each stage of the reserving process can be reimagined through the use of data science along with support from existing actuarial techniques and expert knowledge. In this paper, Milliman’s Rémi Bellina and Loup Ortiz examine how the use of data science tools in conjunction with individual claim and policy data can extract more value from reserving analyses.