How to Reduce False Positives in Anti-Money Laundering

Current Anti-Money Laundering (AML) transaction monitoring systems are often dependent on outdated technology and focus on prioritizing regulatory requirements over effectiveness. Unfortunately compliance with regulations, as most professionals in the industry are aware, is only a start - not a full solution. I think where our conventional approaches to transaction monitoring fall short, is in the use of predefined red flags without integrating any clear data points or context associated with the underlying reasons or patterns driving suspicious behaviors. In other words, we don't recognize the true patterns and typologies of financial crime or their role in the ecosystem. This has to change. By shifting our focus to integrating targeted behavioral data and typologies, such as spending patterns linked to human trafficking or drug-related laundering, we can move beyond generic risk indicators. Instead, we extract truly meaningful signals from the noise, enabling a much more precise and impactful approach to detecting financial crime. Real world example of the impact of this approach: ⚪️ Using only regulation-adherent, red-flag rules at a prior bank, we saw a false positive rate of 94% and filed only 2 actual SARs (basically missing all the real suspicious transactions) ⚪️ After applying more stringent rules that incorporated behavioral data as well, the false positive rate dropped to just 18% and we filed over 600 SARs on the same transaction base. Innovating within compliance is a challenge, but it’s possible, and it’s essential if we want AML to become a real barrier against financial crime.

The Compliance Infrastructure Revolution: How Banks Are Finally Solving the 95% Problem Following up on last week's post around 95% false positive crisis in AML / BaaS Monitoring - after 7+ years of working with BaaS providers and fintechs , I've seen the banks that actually solve this aren't just tweaking AML rules. They're rebuilding infrastructure (the smart way). Here's what we've learned works in practice: 🔧 Six Risk infrastructure shifts that actually move the needle: 1. Real-time data integration The banks succeeding with multiple fintech partnerships all did this first - unified transaction data, customer interactions, and risk signals flowing in real-time. We've seen this reduce investigation time by 20-30% consistently. 2. Lifecycle-aware workflows Instead of treating onboarding, payments, and monitoring as separate systems, the workflows talk to each other. Risk decisions in onboarding actually inform payment monitoring downstream. 3. Product-specific AML strategies This one took us time to figure out - what works for a lending fintech creates chaos for a payments platform. Granular controls by product type (P2P, Cards, B2B, marketplace) make a huge difference. 4. AI for strategy creation, not just detection The breakthrough we're seeing: using GenAI to help create and test AML strategies rapidly. Instead of months tweaking rules manually, teams iterate in days. And then applying HITL (Transparent) Machine Learning and Augmentation and Agent technologies to reduce false positives. 5. Connected case management Single customer view with AI-generated summaries. Sounds basic, but most banks still have fragmented alert systems. This change alone typically cuts case resolution time in half. 6. Open ecosystem approach APIs that actually connect to existing compliance stacks instead of creating new silos. The banks that got this right early saved months in implementation time. What we've seen in practice: Banks implementing this approach consistently onboard fintechs in 90 days vs 12+ months with legacy approaches. They manage 15-20 partnerships while others struggle with 3. The false positive rates? At Oscilar, we typically see 30-40% alert actionability rate vs the industry's 95% - still not perfect, but actually manageable. What's working (or not working) in your environment? The patterns we're seeing vary wildly by bank size and regulatory setup. #DM me to chat about these topics with other Compliance leaders and share best practices. I will be hosting a private zoom session for this. #ComplianceTech #AML #BaaS #FinTech

What if I told you that your sanctions, PEP/RCA, and adverse media screening approach is broken—not because of outdated regulations, but because of flawed engineering? We’ve spent years debating false positives vs. false negatives, yet most organizations still struggle to get the balance right. Why? Because the current screening methodologies rely on rigid matching logic, failing to apply contextually intelligent, whole-entity matching and smart weighting of data attributes to reduce noise while capturing real risk. So what's the problem we're trying to solve? It's two fold. 1 - False positives overwhelm compliance teams, leading to manual overload and wasted resources. 2 - False negatives let bad actors slip through, exposing organizations to regulatory fines & reputational risk. How do I propose it gets fixed? 1 - Instead of simplistic name matching, consider contextual linkages across structured & unstructured data to form a 360° risk profile of an entity. Let's call this Whole-Entity Matching. 2 - Prioritize high-confidence risk indicators over weak matches, using AI-driven relationship mapping, behavioral patterns, and source credibility scoring. Introduce Smart Data Weighting. 3 - Move beyond static rules. Apply adaptive models that adjust in real time based on risk exposure & regional compliance mandates. Adopt Dynamic Risk Thresholds. Financial institutions face soaring compliance costs, with the total cost of financial crime compliance in the U.S. and Canada hitting $61B in 2024, driven by rising screening alerts (LexisNexis Risk Solutions - https://lnkd.in/eXjyXVpf) risk.lexisnexis.com). Regulatory scrutiny is increasing; Starling Bank was fined £29M by the FCA in 2024 for inadequate AML and sanctions controls (Reuters News Agency https://lnkd.in/efnz2xer). Advanced entity resolution technology is helping firms reduce false positives by improving data accuracy, such as metadata-enhanced screening techniques saving time and costs (Chartis Research https://lnkd.in/ez7bAZRd). What is the challenge then? Why are most financial institutions still clinging to outdated matching logic? Is it legacy tech debt, regulatory fear, or just inertia? What’s your take? Are we overcomplicating screening, or are we failing to evolve fast enough? Let’s discuss. 👇 #SanctionsScreening #FinancialCrime #AML #KYC #PEP #AdverseMedia #FraudPrevention #AI #RiskManagement #FinCrime

One thing that we accept that we shouldn't? BSA/AML teams accept high false positive ratios as a cost of doing business. As organizations battle an increasing volume of alerts, SARs, and suspicious activity, some report dealing with false positive rates as high as 95% or 99%. If very few alerts become SARs, this is indicative of a high false positive ratio. This leads to 1. Too much manual review.   2. A horrible customer experience. 3. High costs for the organization. 4. Frustrated analysts. 5. An ineffective BSA/AML program. Trying to battle a high alert volume with a case management UX is backwards. We have to start with alert generation. 👉 Lets deflect alerts first, with much more fine-grained fraud controls, to ensure compliance analysts aren't catching what the fraud tool missed (!!)  👉 If we focus on enriching the quality of transaction data and the readability of rules, we can make sure that when an analyst reviews an alert they know what they're looking at. 👉 If we bring together as much data as possible into a visualization tool like a network graph, the analyst can identify suspicious patterns to file a SAR efficiently 👉 For extra credit we can even have Generative AI help develop a draft SAR narrative. False positives shouldn't be a cost of doing business. Compliance teams shouldn't be a cost center and a drag on the company. They're critical to an effective, well run, trusted organization. Let's help them. Starting with transaction monitoring and working back from there. #aml #amlcompliance #transactionmonitoring #fincrime