Analytics and modeling

By Patricia L. Renzi  |  15 May 2018

The ABCs of insurance technology—specifically artificial intelligence, blockchain, and the cloud—must be front and center in every insurance company’s strategic initiatives.

By Joe Long, Dan McCurley  |  02 May 2018

This article describes a cloud use case where the authors were able to cut a three-month machine learning exploration project down to just under four days using a mixture of open source tools and the Microsoft Azure cloud.

By Peggy Brinkmann, Philip S. Borba  |  20 September 2017

Advancing technology and new data sets have allowed predictive analytics to evolve in sophistication.

By Patricia L. Renzi  |  07 September 2017

Pat Renzi, Milliman’s global leader in the technology area, discusses the transformation of financial reporting systems.

By Dermot Corry  |  07 September 2017

Dermot Corry discusses possible future trends for the Irish market based on his global observations.

By Allen J. Schmitz, Andrew H. Dalton, Daniel A. Nitz  |  25 August 2017

Companies have experienced significant benefits from their first principles models. This article summarizes key topics from a series of articles about first principles modeling for long-term care insurance.

By Sheri Lee Scott  |  22 August 2017

Using data collected by Zendrive, Milliman recently studied the impact of distracted driving and other driving behaviors on collision frequency.

By Wade Matterson  |  07 August 2017

Data is just one component of delivering a personalised retirement experience.

18 July 2017

Milliman assisted a transportation provider in finding a new way to predict the company’s traffic and revenue using open source technology.

By Dr. Lucy Allan, Corey Grigg, Neil Cantle  |  30 June 2017

This report describes how to develop insights into drivers of economic capital within an internal model framework and how to use these insights to make risk management decisions.

By Neil Cantle  |  21 June 2017

This report uses advanced machine learning algorithms, such as deep neural networks, to analyse social media conversations about Brexit.

By Pierre-Edouard Arrouy, Paul Bonnefoy, Alexandre Boumezoued  |  12 June 2017

As it is now crucial to get fast calibration procedures for a certain model, Milliman developed an advanced parameter inference strategy allowing to set such interest rate models in a significantly faster way compared to the classical methods available today.

By Michael Paczolt, William Torres  |  30 May 2017

Predictive analytics vs. historical data summaries: Understanding the difference.

By Jonathan B. Glowacki, Martin Reichhoff  |  17 May 2017

Machine-learning techniques have the potential to help improve business processes, better manage risk, and advance research, but these techniques are not without their own potential risks.

By Jonathan B. Glowacki, Makho Mashoba  |  03 May 2017

Predictive analytics can be applied to many techniques and tools to increase efficiencies within the mortgage industry.

By Richard C. Frese, Tony F. Bloemer  |  02 May 2017

Actuarial expertise is important when working with an actuary but is not the only criteria that should be considered.

20 March 2017

A well-designed usage-based insurance program, aligned with customer needs, will produce positive return by both increasing revenue and lowering costs.

09 December 2016

The combined power of cloud computing and advanced data analytics is being harnessed to help insurers hone their modelling skills to better manage risk, cut costs and improve efficiencies.

By Ping Xiao, Mark R. Shapland  |  22 August 2016

This paper reviews the Hayne MLE modeling framework using a standard notation and covers a number of practical data issues and addresses the diagnostic testing of the model assumptions.

By Eric P. Krafcheck  |  30 June 2016

What types of predictive modeling support should be included in a filing to minimize the objections and amount of time needed to receive an approval from a regulator?