Welcome to this week’s issue of Risk, Bytes and Beyond - your bi-weekly roundup of the latest insights at the intersection of data analytics, artificial intelligence and insurance. Delivered to your inbox every second Friday! In each edition, you’ll find:
Curated content from experts in data and insurance
An insight brief on a changing topic
The latest headlines on data and AI in insurance
Thanks for reading - let’s dive in!
💡 Noteworthy Content
This World Economic Forum article explores how embedded insurance, which describes integrating coverage directly into everyday purchases like flight bookings or device checkouts, and data-driven personalization could help to reshape lost trust in the insurance industry.
In this excellent article, the author argues that modern core systems in insurance can offer far more than just procedural processing. They have the potential to become the strategic backbone driving business transformation. This insightful piece challenges the industry norm of treating core platforms as just back‑office infrastructure and highlights why forward‑thinking carriers should rethink their IT foundations to gain competitive advantage.
AI transformation in insurance isn’t just about tech - it’s about people. This article offers actionable strategies for leaders who face internal resistance to AI adoption, highlighting why empathy and clear communication are as important as algorithms. A useful read for anyone driving change in traditional industries.
💭 Insight Brief
Why Insurance Struggles with Data Quality - and How to Fix It
We’ve all heard this a thousand times: data is the new oil.
I never liked this metaphor much, because it makes people think that data is a raw commodity. Something ready-made and lying around, just waiting to be extracted and used. But that’s misleading.
Data isn’t valuable just because it exists. It only becomes valuable when it's accurate, reliable, and well-structured. And that requires effort, intention, and alignment across an entire organization.
Let’s face it: in the insurance industry, most people know that data is important (after all, insurance would not be possible without it), but they still don’t act like it is.
Why? Because they’re not incentivized to care.
Let’s take claims data as an example.
Claims handlers are typically evaluated using KPIs like:
How many claims they processed in a given timeframe
How long it took them to resolve a claim on average
How satisfied customers were with the experience
All reasonable metrics. But here’s the problem:
These KPIs rarely reflect the quality of the data that claims handlers produce in the process. No one asks:
Did they accurately and consistently describe the claim?
Did they choose the correct option in structured data fields like drop-downs?
Did they document the claim in a way that supports downstream uses, like pricing, fraud detection, or product development?
The truth is: even though claims data is foundational to nearly everything insurers do - underwriting, reserving, analytics, reporting - it’s often treated as a byproduct, not a product.
The role of systems and design
Of course, technology plays a big role. Claims systems shape how data is entered and managed. Well-designed systems can help enforce quality by:
Implementing automatic validation for fields like postal codes, dates, or reserve amounts.
Offering real-time text suggestions or grammar correction to improve free-text inputs.
Reducing ambiguity with intelligent defaults and user guidance.
But no matter how good the system is, it can’t fix misaligned incentives.
What needs to change?
If we want to improve data quality at scale, we need to embed it into the culture. That starts with leadership.
Data quality isn’t just a technical issue, it’s an organizational issue. Everyone, from claims handlers to underwriters to product managers, should understand that they are data creators, and what they do matters.
To drive this shift, companies should:
Align incentives so that data quality is recognized and rewarded.
Include data quality metrics in performance reviews.
Invest in training to build awareness and skills around data stewardship.
Foster cross-functional collaboration between business, IT, and data teams.
If data really is the new oil, then we need to treat it like a high-value resource that requires care, precision, and responsibility - not just extraction.
📰 Industry Headlines
NBC News reports on how patients are using AI-powered appeal tools to successfully challenge insurance denials for treatments. These intelligent systems generate tailored appeal letters, backed by clinical evidence, helping overturn decisions often within a day. This highlights how AI can help to streamline a notoriously complex and time-consuming process.
🔗 NBC News
This article profiles the startup AIUC (Artificial Intelligence Underwriting Company), which has just emerged from stealth with a $15 million seed funding round, aimed at bridging enterprise trust gaps around AI agents. It highlights an innovative new risk‑management model addressing real enterprise anxiety over AI agent failures - a timely and strategic solution at the intersection of insurtech, governance, and AI deployment.
🔗 Fortune
A new report from Fairer Finance shows that UK insurers are using up to 400 data points - including email domain, birth location, and even the time of day an application is submitted - to calculate car and home insurance premiums, often penalizing low-income households with no clear connection to actual risk. With 72% of consumers calling these practices unfair, the study warns of a growing “poverty premium” that disproportionately affects vulnerable groups, while urging regulators to mandate transparency or limit pricing factors.
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