Beyond Alert Fatigue: How AI Sharpens Adverse Media Screening for AML Teams

Financial crime risk is evolving faster than traditional monitoring approaches can keep up, and senior leaders in compliance, sanctions, fraud operations and risk oversight are feeling the pressure.

Across the world, global heads of financial crime compliance, sanctions directors and strategic risk advisors are tasked with ensuring their institutions detect threats early, adapt to regulatory developments, and maintain demonstrable oversight across complex, high-volume operations.

Institutions are facing a steady rise in fraud attempts, increasingly sophisticated threat actors and growing expectations from regulators to demonstrate clear oversight—proving they can detect risk earlier, adapt to regulatory developments, and act faster.

Yet one challenge quietly undermines even the most experienced teams: alert fatigue.

Despite massive investments in data sources and screening tools, too many teams still spend a lot of time clearing irrelevant cases rather than investigating true signals. The problem isn’t a lack of data—banks have more data at their fingertips than ever before—but the ability to interpret that data accurately, continuously and in context. Emerging AI techniques are now offering a practical way to address this challenge.

When More Data Creates More Noise

Adverse media monitoring is meant to surface risk-relevant information about entities or individuals—sanctions exposure, criminal investigations, corruption, regulatory warnings and more, across onboarding periodic reviews and ongoing customer monitoring. But even with curated data sources, the volume is overwhelming.

Banks report increasing focus on fraud and security, with 89% of U.S. banking executives prioritizing investments in security and fraud prevention this year, according to KPMG’s 2025 Banking Technology Survey. Still, resourcing isn’t the core issue. Teams are being buried by irrelevant alerts generated from ambiguous references, outdated reporting and content that lacks clear risk context.

An article naming a person who shares a common name with a politically exposed individual (PEP) shouldn’t trigger dozens of alerts. Nor should historical mentions of misconduct with no relevance to current activity dominate analyst queues. But it happens every day, particularly when screening for sanctions exposure or PEP connections where ambiguity drives spikes in false positives.

This inefficiency becomes more costly as regulatory expectations increase, digital channels expand and fraud patterns become more complex. The result is high false positives, delayed investigations and rising operational pressure.

Context Is the Missing Ingredient

Traditional adverse media screening tools rely heavily on keyword matching or basic machine learning. These methods are good at retrieval but less suited to interpreting signals in the context of risk, timing, jurisdiction or regulatory relevance.

Such tools struggle with:

• Disambiguating entities (Who is this referring to?)
• Understanding sentiment and status (Is this accusation, acquittal, rumor or conviction?)
• Distinguishing relevance (Is the risk current? Does it relate to the party in question?)
• Connecting relationships (How does this individual or company tie to known risks or sanctioned entities?)

Without this context, teams struggle to assess risk accurately and also to demonstrate appropriate due diligence under evolving regulatory expectations and change cycles.

What’s missing is context, and context is what transforms raw text into meaningful insight.

The Hybrid AI Advantage

Hybrid AI approaches—combining large language models (LLMs), machine learning (ML) and knowledge graphs—introduce structure and semantic understanding to unstructured content. Instead of simply matching terms, hybrid AI models can:

• Understand who the text is actually about. Entity resolution helps distinguish between two people with the same name or identify when a mention refers to the wrong corporate entity.
• Capture the meaning, sentiment and risk relevance. Models can interpret whether coverage involves allegations, indictments, sentencing or unrelated commentary and score relevance accordingly.
• Build contextual relationships. Knowledge graphs can map connections to known PEPs, sanctions lists, industries, suppliers, past events and geography to reduce ambiguity, strengthen case context and improve risk scoring.
• Prioritize what really matters. By fusing structured and unstructured knowledge, AI can intelligently route only meaningful alerts to analysts.

The outcome isn’t just better detection, it’s fewer false positives, faster triage, more consistent documentation and clearer rationale for decisions. The ability to interpret information and link it to regulatory or procedural context improves explainability and auditability for compliance stakeholders.

Bringing Focus Back to Real Risk

The promise of AI isn’t to replace analysts, it’s to help them be more effective. AML professionals shouldn’t be spending their day eliminating duplicate matches, clarifying mis-tagged entities or justifying why a years-old local article about a namesake isn’t relevant. They should be investigating risk—following leads, building narratives and applying judgment during onboarding, periodic reviews, and continuous monitoring.

By reducing noise and elevating only the most relevant alerts, hybrid AI allows teams to:

• Detect risk earlier and more consistently
• Improve investigation quality
• Respond to regulatory scrutiny with more confidence
• Lower operating costs without sacrificing oversight

In an environment where 75% of banking executives say they’ve seen an increase in cybersecurity attacks in the last year, efficiency and precision are no longer optional, they’re survival requirements.

Where AML Teams Go from Here

As fraud, cybersecurity pressure, sanctions exposure and regulatory expectations accelerate, banks can’t solve the adverse media challenge by scaling staff indefinitely. And they can’t manually process their way to precision. They need tools that interpret information—continuously, and in context—not just retrieve it.

Staying ahead of regulatory change also means maintaining screening and reporting alignment with new obligations without rebuilding core workflows or documentation from scratch. The ability to quickly understand how changing rules affect processes or customer profiles is essential for sanctions, ABC and broader FCC teams.

Hybrid AI offers a practical next step—not by automating decisions, but by sharpening them, while improving transparency so investigators and compliance teams understand the rationale behind alerts. Its ability to bring together language understanding, statistical learning, and structured knowledge gives analysts a clearer, more accurate picture of who and what represents real risk.

For senior compliance, sanctions and fraud leaders, partnering with expert.ai makes this practical next step actionable. With solutions purpose-built for financial institutions, expert.ai combines hybrid AI—including large language models, machine learning and knowledge graphs—to continuously monitor and analyze adverse news, PEP and sanctions lists and regulatory updates. This approach helps teams reduce false positives, improve operational efficiency and maintain compliance alignment across onboarding, ongoing monitoring and reporting. This gives AML, sanctions and financial crime compliance leaders the clarity and confidence to focus on real risk and strategic oversight.

The result is an AML function that is more targeted, more efficient, and more aligned to the complexity of today’s financial crime landscape—with less time spent clearing false positives and more time focused on meaningful investigation and compliance assurance.

The question isn’t whether banks will embrace hybrid AI, but how quickly they can put it to work.