INSIGHTS | BENEFITS AND ACCOUNTABILITY
Canadian insurers are using increasingly sophisticated data, analytics and artificial intelligence to identify potentially fraudulent healthcare claims. But as the industry gets better at finding suspicious patterns, questions remain about transparency, false positives and what happens to legitimate claimants caught in the process.
For most Canadians with workplace health benefits, submitting a healthcare claim is routine.
A prescription is purchased. A physiotherapy appointment is completed. A dental procedure is performed. A claim is submitted through an app or benefits portal, and reimbursement follows.
What happens between pressing “submit” and receiving the money is less visible.
Behind that simple transaction is a sophisticated claims ecosystem designed to answer one question: Should this claim be paid?
Canadian insurers are increasingly using data analytics, automated systems and artificial intelligence to identify unusual billing patterns, potential fraud and claims requiring additional review.
The objective is straightforward: protect the integrity of benefits plans and help control premiums and benefit costs.
But another question follows: What happens when a legitimate claimant looks suspicious to the system?
The scale of Canada’s benefits system
Private health insurance plays a significant role in Canadian healthcare. According to the Canadian Life and Health Insurance Association (CLHIA), member companies provide supplementary health insurance to nearly 30 million Canadians.
In 2023, insurers paid approximately $36.6 billion in supplementary health claims.
The vast majority of claims are legitimate. But benefits fraud remains a concern for insurers, employers and plan sponsors.
Fraud can involve fabricated or inflated claims, services that were never provided, inappropriate billing, provider-claimant collaboration or reimbursement for services that are not covered. There is also waste or abuse, where activity may be questionable without necessarily constituting deliberate fraud.
For an individual insurer, detecting patterns can be difficult. A provider’s unusual activity may appear ordinary when viewed in isolation. Broader analysis can reveal connections across millions of claims.
AI enters the claims system
In 2022, the CLHIA announced an industry initiative to pool de-identified claims data and use advanced artificial intelligence to identify potential benefits fraud.
The initiative was designed to analyze patterns across millions of records and identify connections that might not be visible within an individual insurer’s data.
In May 2025, the CLHIA announced that additional providers and data were being added to the program. Its 2024 industry facts reported that more than 55 million claims had been analyzed using advanced AI to identify links to potential fraud.
For insurers, this represents a powerful fraud-fighting tool.
For Canadians, it raises a different question: What happens when the system identifies you—or your healthcare provider—as unusual?
A flag isn’t a finding of fraud
This distinction is critical.
An analytical system does not necessarily determine that someone committed fraud. It may simply identify a claim, provider or pattern that warrants further investigation.
That could involve unusual billing behaviour, claim frequency, relationships between providers and claimants, geographic patterns or other characteristics.
A flag is therefore not necessarily an accusation.
But the consequences can still be significant. A claim may be delayed, additional documentation requested, a provider contacted or a claim referred to a special investigations unit. In more serious circumstances, an insurer may deny a claim or take action against a provider.
For a legitimate claimant, the experience can be confusing. They may have done nothing wrong; they may simply have been caught in a pattern that looked unusual statistically.
The false-positive problem
Every fraud-detection system faces the same challenge: How do you identify more fraud without incorrectly flagging legitimate activity?
If a system is too conservative, sophisticated fraud can go undetected. If it is too aggressive, legitimate claims can be caught in the net.
Healthcare makes that challenge particularly important.
People have different medical needs. Some patients require frequent treatment. Some providers specialize in complex conditions. Families can also experience periods when their legitimate healthcare utilization increases dramatically.
A statistically unusual pattern may therefore have a perfectly reasonable medical explanation.
That makes the human review process critical.
The question isn’t simply whether technology can identify unusual activity. It is what happens after the flag is raised.
Does a human review the claim? What information does the reviewer receive? Can the reviewer override the system? Is the claimant told why additional information is required? How long can the review take? What happens if the initial concern proves unfounded?
The answers may vary by insurer, claim type and reason for review.
The privacy question
As insurers connect more information, another issue becomes increasingly important: How much information should be used to assess an individual claim?
Healthcare claims contain highly sensitive personal information. Even when data is de-identified, combining large datasets can reveal patterns that would not be visible in individual records.
Canadian privacy regulators are increasingly examining the implications of artificial intelligence and automated decision-making. A 2026 joint investigation by federal and provincial privacy regulators emphasized the importance of consent, reasonable expectations, accuracy and safeguards around sensitive personal information.
Those principles matter in insurance.
A claimant may reasonably expect information provided to an insurer to be used to adjudicate a claim. Whether that expectation extends to analysis against millions of other claims is a more complicated question.
Insurers can argue that fraud prevention is a legitimate purpose. The challenge is balancing that purpose with appropriate privacy protections.
Protecting the system and the claimant
There is a strong economic rationale for fraud detection.
The CLHIA has framed its industry initiative as a way to protect the affordability and accessibility of group benefits. Fraud can ultimately affect employers, employees, insurers and plan sustainability.
But increasingly powerful detection tools create a corresponding responsibility.
If technology is being used to protect the benefits system, there must also be processes to protect people incorrectly identified by that technology.
A data model can identify a pattern. It cannot necessarily explain why the pattern exists.
Consider a patient receiving physiotherapy several times a week. A model may identify unusually high utilization. An investigator may initially see a concern. But the patient’s medical circumstances could provide a completely legitimate explanation.
The investigator’s role is therefore not simply to confirm the algorithm. It is to determine whether the underlying concern is valid.
Canada’s next claims challenge
The Canadian insurance industry is likely to become increasingly sophisticated in identifying questionable claims. Fraud costs money and can undermine confidence in benefits plans.
But better fraud detection requires better accountability.
As insurers expand their use of analytics and AI, Canadians should be asking: How many claims are flagged? How many are ultimately found to involve fraud? How many are cleared? How long do investigations take? How often are decisions reversed? What information is shared across insurers? How is claimant privacy protected? And who reviews these technologies to ensure they are producing appropriate results?
These are not anti-insurance questions.
They are accountability questions.
The future of Canadian healthcare claims is unlikely to be a choice between technology and people. It will be a combination of both.
Data and analytics can identify patterns humans cannot easily see. Investigators and claims professionals can provide context algorithms cannot.
The challenge is ensuring one complements the other.
The most effective fraud-detection system may not be the one producing the most alerts. It may be the one producing the right alerts—and giving qualified professionals the information, time and authority to determine what those alerts actually mean.
Because when a healthcare claim is flagged, the person on the other side of that claim isn’t a data point.
It’s a patient.
