{"id":59559,"date":"2025-11-03T13:31:41","date_gmt":"2025-11-03T10:31:41","guid":{"rendered":"https:\/\/ondatolive.wpenginepowered.com\/?p=59559"},"modified":"2026-03-12T18:44:05","modified_gmt":"2026-03-12T15:44:05","slug":"aml-false-positives-and-negatives","status":"publish","type":"post","link":"https:\/\/ondato.com\/pl\/blog\/aml-false-positives-and-negatives\/","title":{"rendered":"AML False Positive Reduction: Practical Methods That Don\u2019t Miss Risk"},"content":{"rendered":"\n<p>Due to AML (Anti-Money Laundering) regulations, financial institutions and many other regulated businesses must constantly monitor customers and transactions for suspicious activity. Yet this vigilance often comes at a cost: a flood of false positives that waste resources \u2014 or, conversely, false negatives that miss real threats.<\/p>\n\n\n\n<p>Completely eliminating these errors is impossible, but with the right data discipline, calibrated rules, and governed use of machine learning, organizations can dramatically reduce both without compromising compliance.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"h-understanding-aml-false-positives-and-negatives\">Understanding AML False Positives and Negatives<\/h2>\n\n\n\n<p>The idea of false positives and negatives is quite simple. It describes results that are wrongfully flagged as positive or negative. A simple example would include financial institutions <strong>flagging a person as a sanctioned entity and denying them access to the service when they are not sanctioned.<\/strong> Although simple on the surface, avoiding false positives and negatives requires a complicated and extensive check process. Just in the US, there are over 40 thousand people named John Smith. If one of them ends up sanctioned, this could cause issues for all 40 thousand potential customers.&nbsp;&nbsp;<\/p>\n\n\n\n<p>This is not to say that false positives or negatives are hard to investigate. All it requires is another check to solve the mystery. However, <strong>this means that the KYC or KYB processes must slow down, compliance teams have extra, unnecessary tasks, and the user needs to wait longer to get through<\/strong>. As we know, 40% of users abandon onboarding processes that last longer than 10 minutes. That is why investing in an effective <a href=\"https:\/\/ondato.com\/blog\/anti-money-laundering-compliance\/\">AML compliance<\/a> program is so important for financial institutions and other industries. As reducing false negatives and false positives also means keeping more customers.&nbsp;<\/p>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"h-what-do-aml-false-positives-and-negatives-entail\">What do AML False Positives and Negatives Entail?<\/h2>\n\n\n\n<figure class=\"wp-block-image size-full\"><img loading=\"lazy\" decoding=\"async\" width=\"670\" height=\"377\" src=\"https:\/\/ondato.com\/wp-content\/uploads\/2023\/03\/v01_2025-10_AML_False_Positive_Reduction.webp\" alt=\"\" class=\"wp-image-152307\" srcset=\"https:\/\/ondato.com\/wp-content\/uploads\/2023\/03\/v01_2025-10_AML_False_Positive_Reduction.webp 670w, https:\/\/ondato.com\/wp-content\/uploads\/2023\/03\/v01_2025-10_AML_False_Positive_Reduction-300x169.webp 300w\" sizes=\"auto, (max-width: 670px) 100vw, 670px\" \/><\/figure>\n\n\n\n<p>False positives and negatives have operational, financial, and regulatory consequences:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Investigation backlog and analyst fatigue<\/strong> \u2013 thousands of unnecessary alerts clog workflows.<br><\/li>\n\n\n\n<li><strong>Customer friction<\/strong> \u2013 onboarding and service delays drive abandonment and complaints and lower customer satisfaction.<br><\/li>\n\n\n\n<li><strong>Missed genuine risk<\/strong> \u2013 poor triage lets real criminal behavior go unnoticed.<br><\/li>\n\n\n\n<li><strong>Audit and examination costs<\/strong> \u2013 regulators scrutinize ineffective systems, leading to remediation expenses.<\/li>\n<\/ul>\n\n\n\n<p>These effects ripple through compliance teams and customer experience alike, making efficient detection a competitive advantage, not just a regulatory requirement.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"h-what-causes-false-positive-and-negative-results\"><strong>What Causes False Positive and Negative Results?<\/strong><\/h2>\n\n\n\n<p>False alerts rarely stem from a single flaw. They\u2019re usually the outcome of <strong>interconnected weaknesses<\/strong> across data, technology, and human processes. Understanding these root causes is the first step toward sustainable reduction.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"h-data-quality-and-enrichment\"><strong>Data Quality and Enrichment<\/strong><\/h3>\n\n\n\n<p>AML systems are only as good as the data that feeds them. Incomplete or inaccurate data, such as missing nationality, date of birth, or occupation \u2014 can drastically lower match precision. <strong>Stale KYC data<\/strong> means risk profiles don\u2019t evolve as customers\u2019 behavior changes, causing irrelevant alerts or, worse, missed risk signals.<\/p>\n\n\n\n<p>Equally problematic is <strong>limited external enrichment<\/strong>. Without access to data like adverse media, beneficial ownership, or transactional patterns, systems lack context to differentiate a genuine anomaly from normal customer activity. High-quality enrichment data enables smarter decisions and fewer false triggers.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"h-name-matching-and-transliteration\"><strong>Name Matching and Transliteration<\/strong><\/h3>\n\n\n\n<p>Names vary across languages, alphabets, and cultures, making automated matching inherently complex. <strong>Fuzzy matching<\/strong> or <strong>phonetic algorithms<\/strong> that aren\u2019t tuned for regional nuances can easily misfire \u2014 flagging \u201cJon Smyth\u201d as \u201cJohn Smith\u201d or missing a match altogether.<\/p>\n\n\n\n<p><strong>Inconsistent transliteration<\/strong> between Latin and non-Latin scripts (e.g., Arabic, Cyrillic, or Chinese) adds another layer of difficulty. If the same person\u2019s name is spelled differently in different systems, it can create duplicate alerts or missed hits. Effective AML screening requires name-matching logic that respects cultural context and dynamically adjusts for variations.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\"><strong>Entity Resolution<\/strong><\/h3>\n\n\n\n<p>Customers, counterparties, and related entities often appear under multiple identities across products, subsidiaries, or jurisdictions. Without <strong>entity resolution<\/strong> \u2014 the ability to consolidate records referring to the same person or company \u2014 systems treat these fragments as separate entities.<\/p>\n\n\n\n<p>This results in duplicate alerts and fragmented investigations, hiding the true scope of a relationship. Similarly, when different individuals share identifiers (like addresses or business names), poor resolution can merge them incorrectly, obscuring real risk. A robust entity resolution framework connects identifiers, relationships, and history to present a complete risk picture.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"h-scenario-design-and-thresholds\"><strong>Scenario Design and Thresholds<\/strong><\/h3>\n\n\n\n<p>Detection scenarios \u2014 the rules defining \u201csuspicious\u201d \u2014 often age poorly. Over time, <strong>static thresholds<\/strong> and <strong>one-size-fits-all logic<\/strong> become disconnected from real customer behavior.<\/p>\n\n\n\n<p>For example, a rule that flags every international wire over \u20ac10,000 may have made sense years ago, but today it generates noise if not segmented by <strong>customer type, product, channel, or geography<\/strong>. Modern systems require <strong>dynamic calibration<\/strong> that evolves with transaction volumes, risk typologies, and market context. Otherwise, outdated scenarios create repetitive false positives and fail to catch new laundering patterns.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\"><strong>Watchlist Quality<\/strong><\/h3>\n\n\n\n<p>Not all watchlists are created equal. Many publicly available or aggregated lists include outdated, incomplete, or irrelevant entries. Screening against low-quality lists leads to inflated alert volumes that analysts must manually dismiss.<\/p>\n\n\n\n<p>The <strong>absence of secondary identifiers<\/strong>, such as nationality, birth date, or address, exacerbates confusion between individuals with common names. Without list curation and scoring (assigning trust levels to data sources), organizations waste resources chasing non-threats while missing nuanced risks from poorly documented entities.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\"><strong>Model Issues<\/strong><\/h3>\n\n\n\n<p>Even sophisticated machine learning models can create false positives or negatives if poorly designed or maintained. <strong>Class imbalance<\/strong> skews models toward over-flagging or under-detecting.<\/p>\n\n\n\n<p>Without <strong>periodic retraining<\/strong>, models drift as patterns of legitimate and suspicious behavior change. A lack of <strong>threshold optimization<\/strong> or <strong>analyst feedback loops<\/strong> prevents continuous improvement. Over time, even a well-performing model deteriorates without proper tuning, validation, and monitoring \u2014 leading to unpredictable outcomes and regulatory exposure.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\"><strong>Operating Model<\/strong><\/h3>\n\n\n\n<p id=\"h-what-causes-false-positive-and-negative-results\">Technology alone can\u2019t fix operational shortcomings. Many compliance teams still suffer from <strong>inconsistent triage standards<\/strong>, manual decision-making, and limited <strong>quality assurance (QA)<\/strong>. When analysts interpret rules differently or lack guidance, the same case might be cleared by one reviewer and escalated by another.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"h-how-to-reduce-false-positives-without-increasing-risk\">How to Reduce False Positives (Without Increasing Risk)<\/h2>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"h-ai-based-process-nbsp\">AI-based Process&nbsp;<\/h3>\n\n\n\n<p>The great thing about <strong>AI<\/strong> is that it doesn&#8217;t get tired, doesn&#8217;t get overwhelmed, and it <strong>will not succumb to the same problems that humans do<\/strong>. As a high false positive rate is often the result of an ineffective process, AI has become essential to AML compliance. Luckily, with <a href=\"https:\/\/ondato.com\/\">Ondato&#8217;s software<\/a>, you can easily test, change and deploy the AML rules needed for your customers. This ensures both a lower false positive rate and a process that can easily adapt to your needs as well as the ever-changing regulations of anti-money laundering compliance. This way, your AML compliance program is always up to date with the latest methods, both from your competitors and scammers that aim to trick you.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"h-well-organized-data-nbsp\">Well Organized Data&nbsp;<\/h3>\n\n\n\n<p>Manual processes take much longer than automated ones. A big part of that is due to <strong>how easy it is to misplace physical documents<\/strong>. With Ondato OS, you can rest assured that all of your data is in the same place. This allows for easy updating, a quick way to change the risk level of a customer and an efficient way to double-check any positive or negative results to ensure they are not false. This is the best way to make each distinct piece of the enormous stream of data you&#8217;ve gathered for analysis easily reachable for compliance teams.&nbsp;<\/p>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"h-risk-based-approach-nbsp\">Risk-based Approach&nbsp;<\/h3>\n\n\n\n<p>Ondato OS offers a risk-based approach. It&#8217;s the one way to stay at the top of your game with fraudsters. This approach includes creating risk profiles for entities to be monitored and implementing rules and policies appropriately. By developing a risk profile, <strong>you can decrease the amount of data that is relevant for your customer review process<\/strong> while also reducing the number of false positive AML alerts without increasing the likelihood of false negatives.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"h-last-thoughts\">Last Thoughts<\/h2>\n\n\n\n<p>Reducing false positives is a challenge, but necessary for effective AML processes. The most effective AML programs combine <strong>clean, enriched data<\/strong>, <strong>entity resolution<\/strong>, <strong>calibrated detection rules<\/strong>, and <strong>machine learning tuned under a robust control framework<\/strong>.<\/p>\n\n\n\n<p>With this balance, financial institutions can reduce alert noise, protect compliance teams from burnout, and strengthen their defenses against genuine financial crime.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Due to AML (Anti-Money Laundering) regulations, financial institutions and many other regulated businesses must constantly monitor customers and transactions for suspicious activity. Yet this vigilance often comes at a cost: a flood of false positives that waste resources \u2014 or, conversely, false negatives that miss real threats. Completely eliminating these errors is impossible, but with [&hellip;]<\/p>\n","protected":false},"author":9,"featured_media":152305,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":true,"inline_featured_image":false,"footnotes":""},"categories":[12],"tags":[87],"class_list":["post-59559","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-blog","tag-aml-compliance"],"acf":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO Premium plugin v25.6 (Yoast SEO v27.3) - https:\/\/yoast.com\/product\/yoast-seo-premium-wordpress\/ -->\n<title>Reduce AML False Positives &amp; Negatives with AI &amp; Data | Ondato<\/title>\n<meta name=\"description\" content=\"Learn how to minimize AML false positives and negatives with AI, risk-based approaches, and better data. 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