For timely compliance with Europe’s new AI-Act, insurers should start assessing the risk level of their AI, implement measures, and monitor performance.

A look at Python in Excel focusing on specific challenges faced by financial, actuarial and data science professionals within the insurance industry.

This briefing note discusses the scope of FIDA, its potential impact on the insurance industry and the strategic options available to insurers to compete in this new environment.

Actuarial models are the backbone of any life insurance company.

This article examines the challenges faced by claims professionals and explores the transformative potential of artificial intelligence (AI) in enhancing departmental performance.

Insurance product filings are publicly available documents submitted to state departments of insurance that describe new insurance products or revisions to existing insurance products for regulated types of insurance, like homeowners insurance.

In the last decade we have seen great advancements in the field of natural language processing (NLP).

In this paper, we will describe several use cases in healthcare for generative AI and explore the potential implications of this growing technology for healthcare payers.

Julia is a general-purpose open-source programming language that debuted in 2012....

Milliman data science leaders discuss the opportunities and challenges of using machine learning in insurance, particularly within healthcare.

In this introduction to large language models (LLMs) for insurance professionals, we discuss how these components of artificial intelligence are trained to produce accurate results.

Artificial intelligence has been the buzz term of 2023....

Wearable technologies are becoming increasingly important in our daily lives as well as in medicine, potentially leading to medical breakthroughs....

This paper investigates how the choice of financial data can impact the calibration and the simulation of credit spread (credit default) scenarios within an economic scenario generator, as well as the insurance liability valuation metrics.

Designing and building a custom life insurance projection and asset and liability management (ALM) model in-house is a challenging endeavour that many insurance companies are considering.

Platforms used for life insurance models have been evolving for a while.

This article summarises the results of a research study on accelerating projections of life insurance portfolios by compressing the data of underlying policies.

How to use explainability to fight fraud

Valuing an insurance balance sheet is a complex exercise that requires the use of stochastic economic scenarios.

Being able to evaluate a machine learning (ML) model is essential part of the toolbox for hospital administrators, providers, and insurance administrators.

There is an increasing complexity of risk neutral valuation models in the insurance industry, along with a growing regulatory attention on them.

Compared with traditional techniques, state-of-the-art machine learning algorithms can substantially improve the forecasts of mortality rates.

Compared with traditional mortality models, machine learning algorithms can significantly improve the forecasts of future mortality rates.

Big data, combined with the increased usage of machine learning algorithms, allows the life insurance industry to model the surrounding world much more effectively than in the past.

The emergence of data analytics and machine learning is providing insurers and reinsurers with new insights into how they drive and monitor their business.

After many failed attempts, Michigan legislators finally passed a historic piece of legislation in 2019, Senate Bill No. 1, which will bring sweeping changes to auto insurance in the state starting July 1, 2020.

With the abundance of data and analytical resources, how important is it for insurance carriers to develop and maintain those resources in-house? Sheri Scott answers this and other questions in this Q&A.

Milliman consultants Nancy Watkins, Matt Chamberlain, Peggy Brinkmann, and Sheri Scott discuss how predictive analytics can uncover value in new and expanding data sets, helping improve pricing, underwriting, and profitability.

How Milliman used analytics to develop a predictive app for a transportation provider.

Learn how predictive analytics differ from other approaches to data—and how it can help your business.

This article examines two case studies demonstrating the use of predictive analytics tools in assumption-setting for LTC claims.