First paper in a series that will explore strategic areas where life insurers may deploy artificial intelligence (AI): in underwriting, customer experience, risk assessment, fraud detection, and operational efficiency.

What are the key risks for insurers to consider over the short to medium term?

Julia is a general-purpose open-source programming language that debuted in 2012, prioritizing ease of use and execution efficiency.

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.

Third party litigation funding (TPLF) has emerged as a powerful tool for plaintiffs seeking financial assistance to pursue legal action. 

Methods to detect anomalies can be used to find fraudulent claims in insurance, especially in products with a large frequency of payments, such as in healthcare.

This article discusses how artificial intelligence and machine learning are used to gather comprehensive claims data from structured and unstructured data fields, solving complex problems and reducing total cost of risk.

Claim analytics using artificial intelligence represents a proven method to reduce claim severity and improve the efficiency of claim-handling resources.

Big Data can now provide you with the most likely scenario of how and when you will meet your end. So, what will it mean for the future of risk management and lifestyle planning?

An overview of data science approaches, plus results of our survey on usage in the industry.

Recent technologies—particularly AI, blockchain, and the cloud—will help spur vital disruption and infuse new life into the insurance landscape.