We all want to be fair in the work we do, right? And it’s especially important in the insurance industry, where fairness means giving everyone an equal chance when it comes to coverage, pricing, and claims. But sometimes, even with the best intentions, something called bias can creep in. It can quietly shape decisions in ways we might not even notice, leading to outcomes that aren’t equal—often influenced by things like race or socioeconomic background.

Let’s take a closer look at this important topic and what you, as an insurance professional, can do about it.

The Unseen Influence: Understanding Bias in Insurance

Think about it—bias, in the context of insurance, isn’t about intentionally being unfair. Instead, it refers to those often unconscious prejudices or tendencies you might have towards certain individuals or groups. This can lead to them being treated differently in the insurance process, even if you don’t realise it’s happening.

Why should this be on your radar as an insurer? Well, for starters, it’s an ethical imperative. You do want to build an industry based on trust and equity, don’t you? Legally, bias can lead to serious problems and regulatory scrutiny. And from a business perspective, overlooking potential partiality can mean you’re not serving all your consumers fairly, potentially missing out on opportunities and damaging your reputation.

So, let’s peel back the layers and understand the different forms bias in insurance can take.

The Landscape of Bias: Explicit, Implicit, and Beyond

Bias isn’t a one-size-fits-all concept. We often talk about the two main types:

• Explicit: This is the kind of bias we’re consciously aware of—those overt attitudes and beliefs that might lead us to treat someone differently. While less common in professional settings today, it’s still important to acknowledge.
• Implicit: This is where things get a bit trickier. Implicit biases are the unconscious attitudes and stereotypes that affect our understanding, actions, and decisions without us even realising it. These are often learned over time and can be quite subtle.

Beyond these, there are also a number of various cognitive biases that can influence our judgment. Several examples include:

• Confirmation bias: Sometimes we focus too much on information that supports what we already believe — and in doing so, we might miss out on other important details or perspectives..
• Availability heuristic: We tend to rely on what comes to mind quickly, even if it’s not the most accurate or typical example. Just because something is easy to remember doesn’t mean it tells the whole story.
• Anchoring bias: The first piece of information we get can really stick—and it often shapes how we see everything that comes after, even if it shouldn’t.
• Affinity bias: We might naturally favour people who are similar to us, but if we’re not careful, it can result in unfair treatment.

And finally, it’s worth thinking about systemic bias—the kind that’s built into the very systems, policies, and day-to-day processes of a company or even the whole industry. In insurance, this often shows up through the data and algorithms insurers rely on. If that data reflects existing inequalities, the technology can unintentionally keep those patterns going—or even make them worse.

Where Can Bias Sneak into Your Insurance Practices?

The reality is, prejudice and partiality can potentially touch almost every aspect of the insurance lifecycle:

• Underwriting: When insurers are assessing risk, are they relying on factors that might unfairly disadvantage certain groups? Are there proxies for protected characteristics creeping into their rating? Could there be subjectivity in how they evaluate certain risks? Insurance companies must be vigilant in identifying and addressing biases in their underwriting processes to ensure fairness around the treatment of all policyholders.
• Actuarial modelling and pricing: The models you rely on are based on historical data—and if that data reflects old patterns of inequality, your pricing decisions might unknowingly carry those patterns forward.That’s why it’s so important to take a close look at your algorithms and check for any discriminatory outcomes, even if they weren’t intended.
• Claims adjusting: It’s important to pause and ask: are you unintentionally judging some claimants as more credible than others? Are your settlement offers consistent, no matter a person’s background? Sometimes, unconscious partiality can influence how we evaluate or investigate claims—even when we don’t mean for it to.
• Product development and marketing: When you’re creating new products, are they really built with all kinds of people in mind—or might some groups be getting left out without you realising it? And when it comes to marketing, it’s worth asking: are we challenging stereotypes, or accidentally reinforcing them?
• Customer service: Are all your consumers getting the same respect and care, no matter who they are or where they come from? And is your communication clear, inclusive, and easy for everyone to understand?

The Real-World Impact: Why Addressing Bias in Insurance Matters

The consequences of insurance bias are far-reaching:

• Unequal access: People from certain demographic groups might pay higher premiums and be denied coverage altogether, which means they miss out on the financial protection others have.
• Financial pain: This can be a big economic burden and vulnerability for the individuals and communities affected.
• Loss of trust: When policyholders feel they’re being treated unfairly, they lose trust in the whole insurance industry.
• Legal and regulatory headaches: Bias can lead to discrimination lawsuits and increased scrutiny from regulatory bodies.
• Missed opportunities: By not serving all consumer segments equally, insurers might be missing out on significant business opportunities.
• Reputational damage: In today’s world, unfair discrimination can spread fast and damage your brand.

Identifying and Measuring Bias in Insurance

The first step in tackling bias is recognising where it might exist. Here are some ways insurers can try to identify and measure it:

• Dive into the data: Analyse your underwriting, pricing, and claims data to look for any significant disparities in outcomes across different groups. Set up key performance indicators to track potential prejudice over time.
• Conduct bias audits: Bring in internal or external experts to review your policies, processes, and even the algorithms you use. There are standardised frameworks designed to help with this.
• Listen to feedback: Actively seek feedback from your clients and stakeholders about their experiences. Pay close attention to customer complaints for any patterns that might suggest unfair bias.
• Run scenarios: Use hypothetical situations to test how different decisions might be made and whether there are any potential biases influencing those choices.
• Implement rigorous testing protocols: Make sure your algorithms and predictive models go through careful testing—so you can catch and fix any hidden bias before it affects your clients.

Taking Action: Strategies for Reducing Bias in Insurance

Addressing bias requires a proactive and ongoing effort. Here are some strategies insurers can implement:

• Boost awareness through training: Educate your employees about the different types of bias and their potential impact. Implement unconscious bias training programs to help individuals become more aware of their own tendencies. Foster a workplace culture that values inclusivity and equitable treatment.
• Review and revamp your models: Critically examine your underwriting and pricing models. Are there rating factors that might be acting as proxies for protected characteristics? Can you explore alternative, less discriminatory ways to assess risk? If you’re using algorithms, you need to ensure they are transparent and explainable.
• Standardise your operations: Implement clear, consistent guidelines for underwriting, claims adjusting, and customer service. When everyone’s on the same page, there’s less room for personal judgment to quietly influence decisions.
• Support diversity and inclusion: When your team includes people from different backgrounds, you get a broader range of perspectives—and that can make all the difference in spotting and reducing potential biases.
• Seek external guidance: Don’t be afraid to reach out to experts in bias detection and mitigation. Collaborate with consumer advocacy groups and regulatory bodies to gain valuable insights.
• Monitor and evaluate regularly: Continuously track your key metrics and be prepared to make adjustments to your processes as needed.
• Develop and adhere to ethical principles: Make sure your use of AI and data stays impartial and transparent in your insurance practices.

The Role of Tech and Ethics: A Balancing Act

Technology—especially artificial intelligence (AI) and machine learning—offers both opportunities and challenges around bias. These tools can spot patterns in huge volumes of data that people might overlook. But if the data they’re trained on includes biases from the real world, the algorithms can end up reinforcing those same unfair patterns—or even making them worse.

That’s why it’s so important to think about ethics when building and using AI in insurance. Ensuring fairness, transparency, and accountability should be at the core of any automated system. And just as important: keeping humans involved in the loop to catch and correct any issues before they cause harm.

The Ongoing Commitment to Fairness

Even when it’s unintentional, bias can seriously affect how fair and inclusive your insurance system is. That’s why it’s so important to stay informed about the rules and regulations designed to prevent unfair treatment—and to make sure your practices reflect that.

As the industry professionals, you all share the responsibility to spot these issues and do something about them. Building an unbiased, more equitable industry doesn’t happen overnight—it’s a continuous effort. It means staying curious, being open to learning, and making sure every consumer has a fair chance and fair outcomes.

It’s a challenge, yes—but also an opportunity. Let’s keep pushing for an insurance industry we’re all proud to be part of.

Ready to build a more equitable future for insurance? Discover how our tailored software solutions can help you identify and mitigate bias in your operations. Get in touch!