Not necessarily. When 90–95% of alerts are false positives (as shown in industry benchmarks), investigators spend the majority of their time clearing non-issues. This means real threats may go undetected, or are investigated too late. High alert volume does not equal high detection quality.
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The myth of ‘false positives in insurance are an inevitability’
It is true that insurance data is messy, customer touchpoints are limited, and behavioral signals are weak. When combined, this creates a perfect storm for high false positive rates in AML detection. Legacy systems, built on static rules, flag anything that might be suspicious but struggle to differentiate noise from real risk.
So, we tolerate it. We build large triage teams and add more layers of manual review. We slow down onboarding or claims processing, and we chalk it up to the cost of doing business.
But this is an incorrect way of looking at things. False positives are not inevitable and reducing them can significantly enhance your compliance efforts.
The reality: False positives are draining your compliance function
False positives don’t just create extra work – they erode the effectiveness of your entire compliance program. Here’s how:
- Time wasted: Investigators spend up to 80% of their time clearing non-issues, leaving real threats under-examined.
- Costs skyrocket: Salaries, vendor tools, consulting hours, and regulatory audit prep all increase.
- Delayed insights: Alert backlogs mean suspicious activity is missed or reported too late.
- Regulatory risk rises: Incomplete or delayed SAR filings open the door to scrutiny.
- Customer friction increases: Legitimate policyholders face unnecessary delays in onboarding or claims payouts.
According to a recent industry analysis by Datos Insights, “The AML models that many financial institutions use routinely generate 90-95% false positive rates”, emphasizing the inefficiencies caused by outdated rules-based approaches.
That’s not just inefficiency. That’s systemic noise drowning out your compliance signal.
The fix: Intelligence, not volume
The insurance sector needs a smarter, more scalable solution, one that doesn’t rely solely on “if-this-then-that” rules.
That’s where AI-powered detection models come in. By learning from historical case outcomes, customer behavior patterns, and typology evolution, AI-enhanced tech can:
- Dramatically reduce false positives by distinguishing real anomalies from common customer behavior
- Prioritize high-risk alerts to streamline investigations and reduce alert backlogs
- Continuously learn and adapt as fraud patterns and typologies evolve
- Support explainability and audit-readiness through model transparency and feedback integration
The result isn’t just fewer alerts, but better alerts. Financial crime teams gain the ability to focus their expertise where it truly matters – on the highest-risk, highest-impact cases, rather than being overwhelmed by noise.
Investigations become faster, more precise, and more defensible, supported by transparent models that regulators can trust and auditors can verify.
Ultimately, AI-driven detection empowers institutions to shift from reactive monitoring to proactive risk management. This strengthens compliance resilience while freeing resources to focus on innovation and customer trust.
What this means for insurance AML teams
With financial crime evolving and regulatory scrutiny rising, AML teams in insurance must make the shift from volume to value.
- Instead of reacting to endless low-quality alerts, focus on triaging real risk.
- Instead of expanding your review team, invest in AI that learns from past alerts.
- Instead of accepting alert noise as inevitable, challenge the systems that produce it.
The future of AML in insurance won’t be defined by how much activity you review, but by how intelligently you detect what truly matters.
The bottom line: False positives aren’t just a nuisance — they’re a liability
Accepting high false positives as “normal” leads to burnout, budget bloat, missed threats, and regulatory exposure.
Banks are ditching legacy rules in favor of adaptive models that cut through the false positives and improve detection accuracy. Insurers must follow suit or risk being left behind in the fight against financial crime.
Coming up next in our “Compliance myth-buster series: Insurance edition”:
The myth #3: “Rules are enough for AML” – Why static detection frameworks can’t keep up with dynamic criminal behavior.
Related resources:
Compliance myth-busters: Insurance edition: The myth #1: AML insurance—still low risk?
Redefining Risk: The Insurance Industry’s New Reality
Dive deeper into AML innovation in insurance
Download our white paper “Elevating compliance in insurance: A risk-driven, AI-powered approach to AML and sanctions screening”.
FAQs
False positives are common because legacy AML systems rely on static, rules-based detection – often designed for banking. These systems struggle to handle sparse customer data, limited behavioral signals, and fragmented insurance workflows, especially in non-life products. As a result, they flag large volumes of normal activity as suspicious without proper context.
Yes. AI can learn from historical case decisions, customer behavior, and known typologies to better distinguish true anomalies from normal variation. This enables fewer, more accurate alerts without compromising regulatory defensibility.
Not with the right solution. Modern AML platforms use explainable AI, which provides clear justifications for alerts, risk scores, and model behavior. This is critical for regulatory audits and internal trust. It’s becoming a must-have under evolving global guidelines.
Accepting high false positives leads to:
- Excessive operational costs
- Alert fatigue and staff burnout
- Slower investigations and delayed SARs
- Increased regulatory scrutiny
- Friction in customer onboarding and claims
Over time, it becomes a competitive disadvantage.
