📊 Harnessing AI’s Potential in Insurance: Insights from KPMG

This in-depth KPMG report explores how AI - from traditional machine learning to generative AI - is reshaping the insurance industry, offering both transformative opportunities and significant risks. It blends industry leader perspectives, maturity assessment tools, and case studies to help insurers accelerate adoption while safeguarding trust, data quality, and compliance. A must-read for executives aiming to turn AI from pilot projects into enterprise-wide value drivers.

🔗 KPMG

🧭 Guiding Travelers with AI: Personalized, Clear, and Risk-Aware Insurance

Travel-insurance specialist Chloe Fox unpacks how AI, machine learning, and real-time data analytics are enabling highly personalized coverage while balancing emerging risks and the need for clarity in an increasingly digital age. The piece is essential reading for those curious about how smart technology is reshaping policy design and customer trust in travel insurance.

🔗 ITIJ

🤖 AI as Arbiter: How Product Recommendations in Insurance Are Under Scrutiny

This article explores how AI is poised to become the pivotal force behind insurance product recommendations - essentially “picking winners and losers” by weighing complex product terms against individual client needs. It highlights growing concerns around transparency and algorithmic fairness, and underscores regulator focus on ensuring AI-driven choices serve the consumer’s best interest rather than carriers’ competitive agendas.

🔗 Wolters Kluwer

If you really want to know how seriously a company takes data, don’t start by looking at its dashboards or data platforms. Start by looking at its job postings. The titles alone can reveal whether data is treated as a side-task for a few brave souls or as a fully fledged, strategically managed function.

Typically, the more specific and granular the data roles, the more advanced the company is in its data maturity journey. Let’s look at three stages.

In an early-stage data organization, roles are broad and generic. Titles might range from BI Analyst to Data Analyst, or even Business Analyst. Regardless of the name, these people are often simply known as “the data guy”, or the colleague who “handles the data.”

This usually means wearing many hats: collecting data, building pipelines, analyzing datasets, creating dashboards and reports, and presenting results to stakeholders. It’s a jack-of-all-trades role, but often without the processes, infrastructure, or resources to match the scope.

In a company with medium data maturity, data roles start to differentiate, but still retain some overlap. You might see job titles like Data Engineer, Data Analyst, and Data Scientist, each with a clearer focus.

A Data Engineer handles the pipelines, infrastructure, and data quality. A Data Analyst focuses more on interpreting the data and creating reports. A Data Scientist might work on predictive models or advanced analytics.

However, boundaries are still flexible: the Data Analyst may still write SQL to pull data from raw sources, and the Data Scientist may need to do their own data cleaning. The company recognizes that data requires multiple skill sets, but the organization and processes are still evolving toward specialization.

In a highly mature data organization, job titles become even more specialized, reflecting a well-structured data ecosystem. You might encounter roles like Data Governance Specialist, Machine Learning Engineer, Analytics Engineer, Data Product Manager, or ML Ops Engineer. Each role has a well-defined scope and interfaces clearly with others in the data value chain.

Here, data work is modular. The person designing the data model is not the same as the one building machine learning models, and neither is the person designing dashboards for executives. Teams follow established data governance policies, have formalized processes for data quality assurance, and use automation wherever possible. The data team operates more like a production line than a craft workshop, ensuring scalability and consistency.

The titles a company uses reveal how work is divided, how responsibilities are understood, and how seriously the organization treats data as a strategic asset. Whether you’re job hunting, consulting, or evaluating your own team, pay attention to those titles. They might tell you more than any dashboard ever could.

🛡️ Allianz Life Breach Exposes Nearly 1.4 Million

Allianz Life’s U.S. arm suffered a massive data breach via a third-party cloud CRM system, with hackers leveraging social engineering to access personal data from roughly 1.4 million individuals, including customers, financial professionals, and select employees. The insurer acted swiftly to contain the incident, involving the FBI and offering 24 months of identity theft protection to those affected.

🔗 Economic Times

⏱ Wearable Intelligence: Precision Underwriting Meets AI

WTW and Klarity unveil a cutting-edge underwriting tool that harnesses 12 years of wearable-derived health data (including activity, heart rate, and sleep patterns) to deliver individual mortality risk scores, bringing greater precision to life insurance pricing. Real-world testing demonstrates how this model can both identify low-risk individuals overlooked by traditional metrics and flag hidden risks among those deemed “preferred,” paving the way for fairer, more personalized underwriting.

🔗 WTW

🔎 NAIC Proposes Evaluation Tool for AI Systems Used by Insurers

The NAIC’s Big Data & AI Working Group has unveiled a draft AI Systems Evaluation Tool aimed at equipping insurance regulators with standardized templates and checklists to assess both financial and consumer-related risks from AI usage. This marks a substantive step beyond the existing Model Bulletin framework. The proposal’s tailored exhibits invite insurers to disclose details on AI governance, high-risk models, data sources, and usage across operations, potentially increasing oversight but also compliance demands.

🔗 Eversheds Sutherland