How your insurance score impacts what you pay—and why it exists

An insurance score is a numerical rating used by insurers to predict the likelihood that a policyholder will file a claim. It is not the same as a credit score, but it draws from similar financial data—primarily your credit history—to help insurance companies assess risk. Where a credit score is designed to measure your likelihood of repaying debt, an insurance score measures your likelihood of costing the insurer money.

This scoring tool originated in the United States in the 1990s and has been increasingly adopted in other markets with developed credit infrastructures. In Singapore, for example, where insurance pricing tends to be more community-rated and regulated, direct reliance on credit scores for insurance pricing is less prevalent. However, the principle is increasingly relevant as digital insurers, especially in the motor and home insurance sectors, look to credit data to enhance risk segmentation in their underwriting processes.

So while an “insurance score” may not appear explicitly on your financial records in Singapore or the UAE, the logic behind it—predictive profiling using credit-based metrics—is already shaping underwriting decisions in more ways than many realize.

In countries like the US, insurance scores are legally allowed in certain types of underwriting, such as for auto and homeowners insurance. In those contexts, insurers use credit-based insurance scores to decide:

• Whether to approve a policy at all
• What premium to charge
• How much risk reserve to allocate

A higher insurance score typically means the insurer views the applicant as less risky and may offer lower premiums. Conversely, a lower insurance score signals higher risk, leading to higher pricing—or outright denial.

The score itself is often based on proprietary models developed by data providers like LexisNexis or FICO, and it can include variables such as:

• Total outstanding debt
• Length of credit history
• Payment punctuality
• Number of credit inquiries
• Use of revolving credit

Importantly, the model does not consider protected characteristics like race, gender, or marital status, which are typically excluded to comply with anti-discrimination laws.

In Singapore, the Monetary Authority of Singapore (MAS) does not permit full credit-score-based pricing for health or life insurance. However, general insurers operating digitally—especially in motor and property—may use proxies such as claims history, vehicle financing method, and even job type or homeownership status to serve a similar risk-tiering function. These are not formal “insurance scores,” but they are expressions of the same underlying logic: linking financial behavior with insurance risk.

Insurance scoring is not without criticism. In the US, several states have banned or restricted its use, arguing that it penalizes the poor and amplifies systemic inequality. A person with a poor credit history due to medical debt, for instance, might end up paying more for car insurance—even if they’ve never filed a claim or had an accident.

Critics say this practice creates a feedback loop: financially vulnerable individuals are charged more for essential services, which in turn deepens financial strain. There’s also a lack of transparency. Most consumers don’t even know they have an insurance score, let alone how to improve it.

That’s where regulation differs sharply across markets. Singapore’s insurance regulatory framework focuses more on claims behavior, demographic segmenting, and health status for underwriting—but less on financial credit behavior. Still, with digital financial profiling becoming more widespread, the concept of scoring based on behavioral data is not as far away as it may seem.

Insurers in Southeast Asia are already experimenting with driver telematics, wearable-linked health premiums, and other forms of data-driven underwriting. As financial apps, credit scores, and insurance platforms become more integrated, insurance scoring—if not in name, then in practice—will likely grow.

To see how this works in practice, take the case of two individuals applying for motor insurance in a credit-scoring jurisdiction like the US or parts of the UAE:

Applicant A: Good credit history, no missed payments, low revolving debt
Applicant B: Spotty credit history, multiple late payments, high debt usage

Both have the same car, same age, same driving record, and live in the same zip code. But Applicant A receives a quote of $950 per year, while Applicant B is quoted $1,400. The difference stems from the insurance score: Applicant B’s financial behavior is viewed as predictive of higher claims risk—even if they’ve never filed one.

This logic, though controversial, is rooted in actuarial data. Studies have shown that individuals with lower credit scores are statistically more likely to file claims and cost insurers more per claim. The models aren’t perfect, but the correlations are strong enough that insurers consider them predictive.

In Singapore, similar outcomes might occur behind the scenes. A digital insurer might not disclose a formal “insurance score,” but they may quote higher rates to individuals who have financing-linked cars, rent rather than own their homes, or have recently defaulted on other financial obligations.

In the United States, most states allow the use of credit-based insurance scores for auto and home insurance underwriting, with a few exceptions like California and Massachusetts, which ban it entirely. The rationale for allowing it is simple: it helps insurers price risk more accurately, which in theory keeps premiums lower for lower-risk consumers.

In the UAE, where motor and health insurance markets are increasingly digitized, insurers may use creditworthiness proxies or financial history data—though the regulatory framework does not explicitly standardize an “insurance score” system.

In Singapore, MAS maintains more direct oversight of insurance pricing models. While most insurers are not allowed to use credit data directly for policy decisions, they may still incorporate financial proxies—such as claims ratios, occupation, or lifestyle indicators—in risk modeling, especially for general insurance. So even without a formal credit-linked insurance score, Singaporeans are not immune to being profiled for risk in ways that resemble the same methodology.

If you live in a jurisdiction where insurance scoring is used, it’s worth knowing that you can request a copy of your insurance score report from providers like LexisNexis or FICO. These reports will typically show your score, the top risk factors affecting it, and suggestions for improvement.

If you live in a place like Singapore, you won’t have an explicit insurance score—but your financial and claims behavior still matters. Insurers take note of your accident history, your ability to pay premiums on time, and in some cases, whether you’ve defaulted on related obligations like car loans.

The general rule: responsible financial behavior—on-time payments, low revolving debt, stable job history—is protective not just for your credit file but for your insurance pricing as well.

Still, there’s no need to panic. Not all insurers use behavioral scoring equally. Some emphasize claim history and lifestyle factors instead. If you're concerned, ask your insurer directly how your premium is assessed—and whether behavioral or financial data played a role. Even if your score isn’t visible, understanding the broader logic helps you advocate for fairness in pricing.

The use of insurance scores reflects a broader trend in financial services: risk is increasingly being modeled through behavioral data, often in ways consumers don’t fully understand. While these models offer efficiency and accuracy for insurers, they raise fairness and transparency concerns for policyholders—especially when financial hardship unrelated to insurance behavior ends up raising premiums.

In markets like Singapore, the absence of a formal score doesn’t mean the absence of scoring logic. Many digital insurers now use data proxies—ranging from payment frequency to vehicle ownership status—that operate similarly to risk profiles built on credit. The concern isn’t just whether insurers can model behavior—it’s whether consumers know they’re being modeled, and whether they can challenge it.

Ultimately, insurance is about pooled risk. But if data-driven profiling becomes too narrow or punitive, it risks undermining the solidarity principle that makes insurance viable in the first place. Risk-based pricing must remain accountable—not just accurate.