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With its unparalleled ability to turn data from raw material into a valuable resource, AI is more than an opportunity to innovate. Nevertheless, this doesn’t make it the answer to every problem that involves data. Although understanding and avoiding these misconceptions is essential, there is another equally critical consideration when it comes to realising AI's impact. Namely, the question of how to turn models that are theoretically performant pre-deployment into operationalised systems in...

In a previous article, we discussed how the recently launched Aioi R&D Lab - Oxford was tackling the problem of ageing populations by using insurance telematics data and AI to detect the risk factors indicating cognitive decline in elderly drivers. As ageing population dynamics around the world continue to shift, projects like this can create shared value in society by enabling older safe drivers to maintain their freedom with access to affordable insurance products. This article explores how...

We have previously spoken about how AI is more than just an innovation opportunity, despite contrary prevailing wisdom. It can already solve real and complex problems in various challenging domains. But before work starts on implementation, it’s vital to establish the nature of these problems, especially with regard to the data involved, and to consider whether AI is truly the right solution.

Much of the rhetoric surrounding AI and Machine Learning (ML) suggests a universal solution to every...

Fraud is one of the most pervasive threats to the insurance industry, causing damage to both businesses and customers. Every insurer takes steps to try and negate this threat, but how effective are these measures, and could AI hold the key to truly effective fraud detection?

The history of insurance fraud

Insurance is ubiquitous in modern society and accounts for over 7% of the world’s economic activity. Although the exact origins are difficult to pinpoint, we know insurance dates back to at...

In the previous two blogs in the series, we addressed what makes machine learning models trustworthy and how machine learning models fail. This blog is focused on how machines learn and one particular approach called meta-learning, which enables machine learning models to improve over time such that they can generalise to perform related but distinct tasks. We will look at how humans learn compared to machine learning, what meta-learning is, and how meta-learning can be done responsibly.

How do...

The integration of Artificial Intelligence (AI) and machine learning has already brought transformative opportunities across the rapidly evolving landscape of the insurance industry. As data generation has surged and competition intensified, insurers have recognised AI’s game-changing potential as a tool for analysing expansive volumes of data with unprecedented speed and accuracy. With data generation occurring at such high velocity, insurers turned to AI as a series of experiments to make...

AI is everywhere. It has rapidly become an integral part of how we organise our lives, go about our jobs, and get from place to place. And yet, in our discussions with customers, we often encounter a wide range of misconceptions about what AI actually is. In this article, we aim to shed some light on what this technology is and how it works, but also on where AI began, who the pioneers were that paved the way, and where all of this is heading. We spoke to Mind Foundry’s co-founder Stephen...

At the beginning of 2023, we announced the launch of the Aioi R&D Lab - Oxford, a joint venture between Mind Foundry and our partners Aioi Nissay Dowa Insurance (ANDI) and Aioi Nissay Dowa Europe (AND-E). One of the projects the Lab is currently working on is to solve a problem affecting countries with ageing populations, like Japan and the United Kingdom. 

We interviewed Davide Zilli, VP of Machine Learning at Mind Foundry, Koji Takahashi, Telematics Project Manager at Aioi R&D Lab - Oxford...