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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Why static detection frameworks can’t keep up with dynamic criminal behavior
For many insurance compliance teams, rules-based monitoring has been the foundation of their AML strategy for years. It’s familiar, auditable and easy to implement. Historically, it’s been enough to satisfy regulatory requirements.
But that view is quickly becoming outdated.
In this round of the “Compliance myth-buster series: Insurance edition”, we tackle the belief that static rules are sufficient to combat modern financial crime and show why insurers must move toward adaptive, intelligence-led approaches.
The myth #3: Rules-based monitoring covers the essentials
Most legacy AML systems rely on rules that trigger alerts when certain thresholds are crossed or predefined conditions are met. These might include:
- A premium payment above a set amount
- A policy surrendered within 30 days
- A change in beneficiary close to maturity
- Duplicate claims across regions
These rules are typically hardcoded and reviewed infrequently. As long as alerts are being generated and investigated, the system is considered to be ‘working’.
But here’s the problem: Criminals don’t play by static rules. Criminal behavior is dynamic and evasive. Today’s financial criminals use complex, evolving tactics to hide their activity. They know what thresholds trigger alerts. They know how to space out transactions, route activity through multiple intermediaries, and exploit product and jurisdictional gaps.
Why rules alone aren’t enough
The simple truth is that rules alone aren’t enough. Here’s why:
- Rules miss subtle patterns: Rule-based systems typically trigger alerts based on single actions like a large payment or an early surrender. But financial crime often unfolds through combinations of behaviors that, on their own, seem harmless. For example, in a real case reported by the Council of Europe typology report, a subject deposited €1 million in cash into two single-premium life insurance contracts. Shortly after, they surrendered both policies, accepted a financial loss and transferred the proceeds to a family member in another jurisdiction. Each step appeared legitimate in isolation, but taken together, it formed a classic layering scheme to obscure the origin of illicit funds. Rules failed to flag the activity because the transactions didn’t break thresholds or violate policy terms, but the behavioral pattern told a different story.
- High false positives: Static thresholds (e.g., any claim above $10,000) often flag legitimate transactions as suspicious, overwhelming investigators and delaying resolution. For example, UK insurers, including Allianz, reported a surge in motor insurance fraud involving photo manipulation cases. Static claim-value rules failed to catch the fraud.
- Failure to evolve: Once set, rules rarely adapt unless manually tuned, making them easy for criminals to circumvent. A real-world example: In the UK, an insurance agent used static processes within life insurance systems to launder over $1.5 million. The agent collected premiums via wire transfers from overseas accounts, and the policies were later surrendered early — a classic layering method within insurance. The insurer’s detection rules didn’t evolve to flag this pattern over time, and because each policy transaction appeared valid in isolation, the laundering went undetected for years. This highlights how criminals exploit rigidity in legacy systems, knowing that what isn’t explicitly prohibited often goes unchallenged.
- Operate in silos: Most rules don’t connect behavior across policy lines, customers, or geographies. For example, life and non-life data are monitored separately. A policyholder who cancels a motor policy for a refund, then uses those funds to top up a life policy, may never trigger an alert because the system can’t connect the dots.
Example: The limitations in action
Let’s say an individual tops up a policy with small amounts multiple times over three months with none exceeding your organization’s risk threshold. They then surrender the policy for a full refund. A rule that flags single large payments or early surrenders might miss this entirely.
Meanwhile, dozens of legitimate policyholders are caught in alerts because they paid in lump sums during onboarding, which drives up false positives and consumes investigator time.
The net effect? Wasted resources, delayed detection, and growing regulatory risk.
The fix: Intelligence that learns and adapts
What’s needed is AI-powered detection that learns from behavior, not just predefined conditions. Machine learning models can:
- Detect risk based on combinations of actions, not just single events
- Adapt to new typologies without manual rule changes
- Prioritize alerts based on risk scoring, reducing triage workload
- Identify emerging patterns that rules haven’t accounted for yet
By integrating historical case data, policy behavior, regional risk profiles, and entity linkages, these models build a dynamic view of risk, constantly updating as new information comes in.
What compliance teams should do now
The good news is that it isn’t too late to upgrade your approach to AML. Here is a quick five-step process to immediately improve your institution’s efforts and effectiveness:
- Audit your current detection rules — what are they missing? What are they over-flagging?
- Use AI to complement rules for a hybrid approach. Note: Not to replace existing systems but work with them via AI overlays.
- Train models on your real case data to improve contextual awareness.
- Integrate fraud, claims, and AML data to detect cross-domain signals.
- Monitor and evolve; your detection strategy should be dynamic, not static.
Bottom line: Rules provide coverage, AI provides context
Rules are important. They form the backbone of a compliance program.
But alone, they’re like a smoke alarm that triggers every time someone cooks toast. You’re constantly reacting and you risk missing the real fire.
By pairing rules with adaptive AI, insurers can cut noise, detect hidden threats, and deliver on the regulator’s growing focus: effectiveness.
Coming up next in the “Compliance myth-buster series: Insurance edition”
The myth #4: “If it’s not regulated, it’s not a risk” – why ignoring unregulated product lines could be your biggest blind spot.
Related resources:
Compliance myth-busters: Insurance edition: The myth #1: AML insurance—still low risk?
Redefining Risk: The Insurance Industry’s New Reality
Webinar: Regulators, risk & reinsurers: AML’s New Frontier
Want to future-proof your detection program?
Download our white paper “Elevating compliance in insurance: A risk-driven, AI-powered approach to AML and sanctions screening” to explore how top insurers are integrating AI, reducing false positives, and meeting global compliance demands.
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.
