AI fraud flags create backlogs and appeals at Canadian insurers, prompting regulator scrutiny of oversight controls

AI fraud flags create backlogs and appeals at Canadian insurers, prompting regulator scrutiny of oversight controls

TORONTO — Who: Canada’s insurers and industry trade groups; What: increasing use of automated fraud‑detection systems that are flagging large numbers of claims; When: in the wake of rising fraud activity and rapid AI adoption during 2024–2025; Where: across Canada, with federal and provincial regulators expanding scrutiny; Why: complex AI models, rising adversary use of generative tools and data‑quality problems have produced high volumes of alerts and false positives that are creating manual backlogs, growing appeal workloads and regulatory concern. (globenewswire.com)

Summary

• Canadian life, health and property‑casualty insurers have rapidly deployed AI and machine‑learning systems to triage and score claims for suspected fraud. Industry groups say pooled, AI‑driven detection improves pattern recognition across carriers, but vendors and insurers warn that automated systems can generate many false positives that require human review. (shift-technology.com).
• Insurers report more claims flagged as suspicious after an uptick in sophisticated, AI‑enabled fraud tactics; at least one major carrier’s Canadian unit reported double‑digit increases in fraud investigations in 2024. Those alerts are translating into heavier workloads for special investigations units (SIUs), slower handling times for legitimate claims and more policyholder appeals. (globenewswire.com).
• Federal and provincial supervisors — including the Office of the Superintendent of Financial Institutions (OSFI) and Ontario’s Financial Services Regulatory Authority (FSRA) — have placed AI and model‑risk oversight near the top of their agendas, requiring stronger governance, inventories of models and closer monitoring of third‑party suppliers. (osfi-bsif.gc.ca).

The numbers and the push to automate
Insurers and industry groups say fraud is increasing and technology is part of both the problem and the solution. In a March 2025 release, Aviva Canada said it saw a 46 percent rise in claims flagged for potential fraud and a 76 percent increase in fraud investigations in 2024, with staged auto accidents and AI‑enabled falsified documents cited as growing threats. Aviva and others have singled out generated invoices, manipulated receipts and deepfake identity evidence as tactics that have emerged or accelerated since late 2023. (globenewswire.com)

Against that backdrop, the Canadian Life and Health Insurance Association (CLHIA) in May 2025 announced an expansion of a pooled‑data program that gives member companies industry‑wide analysis based on de‑identified claims and leverages the fraud‑detection technology of vendors such as Shift Technology. CLHIA President and CEO Stephen Frank said expanding pooled analytics “improves the industry’s ability to identify potentially fraudulent activity” and “benefits all Canadians” by protecting plan affordability. Jeremy Jawish, Shift’s CEO and co‑founder, said industry‑wide models help insurers “connect the dots across a huge pool of claims data.” (newswire.ca)

Vendors and consulting firms stress the upside. Deloitte and other consultancies project major efficiency gains when insurers apply automated scoring, multimodal analytics and real‑time triage to claims — potentially saving tens of billions industry‑wide if deployed effectively. But those studies and vendor case studies also warn about the operational cost of false positives — legitimate claims incorrectly classified as suspicious — because every flagged file often requires a manual investigation, slowing payouts and generating appeals. (claimsjournal.com)

How automation turns into backlog
Fraud‑detection systems typically produce a risk score or a prioritized list of alerts that feed SIUs and adjusters. The promise is to route clear, low‑risk claims to straight‑through processing and direct scarce investigator time to complex high‑value cases. In practice, however, model thresholds, poor input data, fragmented legacy systems and new adversary tactics have combined to inflate the volume of flagged items.

An insurer operating at scale can receive millions of claims annually; even a small increase in the fraction flagged can translate into thousands of additional manual reviews. When alerts spike — for example after natural disasters or during periods of coordinated fraud campaigns — SIUs can be swamped. That creates queues for deception investigations, slows settlement of legitimate claims, and leaves policyholders with denials or requests for additional documents that prompt formal appeals. Industry sources say workloads for appeals teams and external review processes have risen materially at several large carriers, though most firms do not publish detailed operational backlogs. (fintechstrategy.com)

Regulators take notice
Canada’s prudential regulator has moved from observing risks to acting. OSFI’s Annual Risk Outlook for 2025–26 names integrity, technology and AI among top supervisory priorities and says the office is conducting thematic reviews and targeted monitoring of AI use and model risk at federally regulated institutions. OSFI also published a revised model‑risk guideline (E‑23) in September 2025 that expands expectations for model governance, inventories, independent validation and board oversight — with an effective date for insurers in May 2027. OSFI has warned that AI can “amplify existing risks, such as fraud and other financial crime,” and it plans to assess institutions’ preparedness, third‑party risk and governance controls. (osfi-bsif.gc.ca)

Provincial supervisors have also signaled concern. Ontario’s FSRA has used data analytics in its supervisory work and published notices noting how AI can reveal systemic noncompliance, conflicts of interest and evidence‑integrity problems in claims files — all of which can feed appeals and delays if insurers rely on flawed inputs. FSRA’s consumer‑advisory and thematic surveillance work has highlighted where evidence or IME (independent medical exam) documentation is unusable or suspicious, creating downstream delay and appeals risk. (fsrao.ca)

Real harms, legal risk and high‑profile U.S. precedents
Regulatory scrutiny is reinforced by high‑profile legal challenges in the United States that illuminate how automated decisioning can cause harm. Plaintiffs in class actions and investigative reporting have alleged that U.S. Medicare Advantage carriers relied heavily on predictive algorithms to limit post‑acute care, leading to denials that were later overturned at appeal. STAT, Ars Technica and other outlets reported internal evidence and litigation claims that many automated denials were reversed on appeal, with plaintiffs asserting that insurers relied on the low appeal rates to limit payouts. Insurers have disputed those characterizations, saying clinical and supervisory review remain in place. The U.S. litigation — including complaints involving nH Predict and related tools — has put attention on governance, human‑in‑the‑loop policies and transparency. Canadian regulators cite these developments as cautionary examples when assessing model risk domestically. (statnews.com)

“Any institution using a model to make, support or drive decisions must be prepared to explain and monitor that model,” OSFI wrote in guidance and public statements, and OSFI officials have told industry they will follow up where oversight appears weak. (osfi-bsif.gc.ca)

Why false positives are stubborn
Experts cite several technical and operational reasons why false positives remain a problem even as detection accuracy improves:

• Data quality and fragmentation. Claims files mix structured billing codes with unstructured text, images and third‑party provider notes; OCR errors and missing fields produce misleading signals.
• Adversarial tactics. Fraudsters increasingly use generative AI to produce plausible invoices, cloned IDs or synthetic identities that evade simple rules and trigger algorithmic suspicion without obvious human clues. (globenewswire.com)
• Model thresholds and business tradeoffs. Insurers tune models to trade missed fraud (false negatives) against investigative cost and customer friction; shifting that balance to reduce payment leakage often produces more false positives.
• Vendor‑standardization and concentration. Many carriers buy third‑party engines; when vendors use similar models or shared data, a systemic model error or a widely adopted threshold can create industry‑wide surges of flagged claims. (shift-technology.com)

Industry leaders emphasize human oversight
Insurers and vendors say they are not abandoning human judgment. CLHIA’s pooled analytics program and Shift Technology’s products are positioned as tools that “surface” suspicious patterns rather than issue final denials. “Using artificial intelligence to identify potential fraud has proven incredibly beneficial for individual insurers,” CLHIA and Shift executives said in public statements — but they also stress explainability, human review and evidence gathering as core elements of responsible use. (newswire.ca)

Consultants recommend layered controls. A Deloitte report cited by industry sources says combining automated business rules, embedded AI and anomaly detection can score millions of claims in real time and reduce false positives, but it also calls for model validation, monitoring and human‑in‑the‑loop safeguards. “Combining data from various modalities…could help identify patterns and anomalies and enhance the investigative process by reducing false positives,” Deloitte wrote. (claimsjournal.com)

Appeals, customer experience and operational costs
The operational reality is blunt: when a legitimate claim is flagged, a customer faces requests for more documents, longer processing times or an outright denial that must be appealed. Appeals processes — internal reconsiderations, ombudsman complaints, external arbitration or litigation — are time consuming and costly. Even when appeals succeed, the delay can mean financial strain for claimants and reputational damage for carriers.

Providers and patient advocates in other jurisdictions have documented cases where appeals reversed most automated denials; plaintiffs in U.S. litigation alleged that more than 90 percent of certain automated denials were later reversed on appeal. Whether the same reversal rates apply in Canada is a matter of ongoing inquiry, but regulators say the risk of erroneous adverse outcomes is real and must be governed. (arstechnica.com)

What insurers are doing
Insurers report a range of operational responses:

• Augment SIU staffing and invest in forensic document and identity verification tools.
• Tune detection thresholds and apply post‑score rules to reduce low‑value false positives.
• Add explainability layers and audit trails so decisions can be reconstructed for auditors and regulators.
• Participate in industry data pools to spot patterns across portfolios and share typologies with law‑enforcement partners. (shift-technology.com)

Regulators insist on governance and third‑party oversight
OSFI’s E‑23 model‑risk guideline requires model inventories, documented owners, independent validation and regular review cycles for models in production — explicit requirements that apply to AI and machine‑learning applications used in claims and fraud detection. Provincial supervisors and consumer offices are also exploring disclosure expectations, accessible appeal remedies and minimum human‑review standards. FSRA’s public material and advisory panels have urged accessible, auditable evidence and stronger verification of provider credentials used in claim decisions. (osfi-bsif.gc.ca)

The policy stakes and the road ahead
The insurance industry faces a classic tradeoff: automation can reduce costs and recover fraudulent losses, but without robust governance and data hygiene it risks creating systematic delays and wrongful denials that harm consumers and invite regulatory or legal action. Deloitte and other consultancies estimate large potential savings from better‑executed AI programs — but emphasize that governance, monitoring and explainability are preconditions for realizing those gains. (claimsjournal.com)

“AI can deliver efficiency, but it can also amplify existing vulnerabilities,” OSFI cautioned in its risk outlook, and the office has signaled stepped‑up supervisory activity and thematic reviews to ensure insurers’ controls keep pace with their deployments. (osfi-bsif.gc.ca)

For now, the immediate effects are operational: more claims routed for manual review, longer resolution times for some policyholders, and a growing pipeline of appeals that industry operations teams and regulators are racing to understand. How quickly insurers can reduce false positives, improve data inputs and document their human‑in‑the‑loop safeguards will determine whether AI becomes a net force for faster, fairer claims or an engine of delay and dispute.

Sources: reporting and public documents from Aviva Canada, the Canadian Life and Health Insurance Association and Shift Technology; the Office of the Superintendent of Financial Institutions’ Annual Risk Outlook and Guideline E‑23; Ontario’s Financial Services Regulatory Authority materials; investigative reporting and litigation coverage in STAT and Ars Technica; and industry analysis from Deloitte. (globenewswire.com)

(Reporting by [Staff writer].)