{"id":121690,"date":"2023-05-01T12:02:16","date_gmt":"2023-05-01T12:02:16","guid":{"rendered":"https:\/\/feedzai.com\/?p=121690"},"modified":"2024-04-09T09:13:32","modified_gmt":"2024-04-09T09:13:32","slug":"how-feedzai-embeds-next-generation-anti-money-laundering-solution-optimization","status":"publish","type":"post","link":"https:\/\/feedzai.com\/blog\/how-feedzai-embeds-next-generation-anti-money-laundering-solution-optimization\/","title":{"rendered":"How Feedzai Embeds Next-Generation Anti-Money Laundering Solution Optimization"},"content":{"rendered":"

[vc_row row_height_percent=”0″ override_padding=”yes” h_padding=”2″ top_padding=”1″ bottom_padding=”2″ overlay_alpha=”50″ gutter_size=”3″ column_width_percent=”100″ shift_y=”0″ z_index=”0″][vc_column width=”1\/1″][vc_row_inner][vc_column_inner width=”1\/12″][\/vc_column_inner][vc_column_inner width=”10\/12″][vc_single_image media=”121699″ dynamic=”yes” media_width_percent=”100″ uncode_shortcode_id=”977980″][\/vc_column_inner][vc_column_inner width=”1\/12″][\/vc_column_inner][\/vc_row_inner][vc_row_inner][vc_column_inner column_width_percent=”100″ gutter_size=”3″ overlay_alpha=”50″ shift_x=”0″ shift_y=”0″ shift_y_down=”0″ z_index=”0″ medium_width=”0″ mobile_visibility=”yes” mobile_width=”0″ width=”2\/12″][\/vc_column_inner][vc_column_inner width=”8\/12″][vc_custom_heading heading_semantic=”h3″ text_size=”h3″ text_weight=”400″ uncode_shortcode_id=”115666″]The challenges of modern financial crime are pushing banks ever more into the vanguard. Banks worldwide are grappling with a $2T money laundering problem that requires next-generation anti-money laundering<\/a> solutions. These organizations need to be armed with the latest technology to avoid being overwhelmed.<\/span>[\/vc_custom_heading][vc_column_text uncode_shortcode_id=”213925″]Rules-based legacy systems simply aren\u2019t able to address the sophistication of today\u2019s financial crime tactics. Banks need to optimize their anti-money laundering (AML) alerts to effectively detect and intercept suspicious activity.\u00a0<\/span><\/p>\n

Here\u2019s how Feedzai addresses the challenge using patented AI technology to optimize AML alerts \u2014 without requiring banks to immediately retire existing rules-based systems entirely.\u00a0<\/span><\/p>\n

4 AML Challenges for Compliance Officers<\/span><\/h3>\n

Before looking at how next-generation anti-money laundering solutions can help, let’s first look at the common pain points caused by rules-based legacy systems.<\/span><\/p>\n

Many Banks\u2019 AML Systems Were Built in the Pre-Digital Age<\/span><\/h4>\n

The financial services industry has experienced a digitalization boom in the past few years. However, many existing AML systems use a rules-only approach to transaction monitoring. Without next-generation anti-money laundering methods in place, banks\u2019 legacy systems will be unable to meet the demands of the digital banking workflow. The result is a large amount of false positive alerts. This leaves compliance teams scrambling to address non-issues instead of focussing on the real threats.\u00a0<\/span>[\/vc_column_text][vc_single_image media=”121712″ media_width_percent=”100″ uncode_shortcode_id=”905066″ el_class=”hide-in-mobile”][vc_single_image media=”121716″ media_width_percent=”100″ uncode_shortcode_id=”134971″ el_class=”hide-in-desktop-tablet”][vc_column_text uncode_shortcode_id=”208908″]<\/p>\n

Compliance Officers Feel Burned Out<\/span><\/h4>\n

It\u2019s estimated that about <\/span>95% of a bank\u2019s system-generated alerts are false positives<\/span><\/a>. Banks\u2019 compliance officers must address this struggle to remain focused and avoid being disheartened. A recent <\/span>survey<\/span><\/a> found that nearly half (49%) of compliance officers feel overwhelmed at least weekly. If compliance officers can\u2019t trust the quality of AML alerts, they risk spending valuable time on unproductive suspicious activity reports (SARs). In other words, compliance professionals often chase false positives instead of focusing on high-value alerts and identifying financial crimes. No wonder so many compliance officers feel burned out on the job.<\/span><\/p>\n

Despite Heavy AML Investments, Banks Still Face Fines\u00a0<\/span><\/h4>\n

Banks worldwide spend billions on financial crime compliance each year. Yet despite these heavy investments, global regulators <\/span>fined banks roughly $5 billion<\/span><\/a> for anti-money laundering failures. That\u2019s a 50% increase in fines issued a year earlier. In other words, after investing large sums of money to fix a problem, the problem persists.\u00a0<\/span><\/p>\n

Banks clearly need a more effective alternative if they want to stay ahead of financial crime trends. This means adopting next-generation anti-money laundering solutions for next-generation challenges while causing minimal disruption to existing processes. That\u2019s how Feedzai\u2019s AML solution optimizes alerts for more effective compliance.<\/span><\/p>\n

Barriers to Embedding Machine Learning in AML<\/span><\/h4>\n

Machine learning is critical to enhancing AML reporting. However, there has been some reluctance in the past to implement it. There are several reasons for this. First, regulators have been busy clarifying the guidelines for appropriately using artificial intelligence (AI) and machine learning. For example, an EU-based challenger bank was <\/span>recently fined<\/span><\/a> for using AI in its AML workflows by its central bank but successfully overturned the decision in the courts. <\/span>[\/vc_column_text][vc_single_image media=”121691″ media_width_percent=”100″ uncode_shortcode_id=”901531″ media_link=”url:https%3A%2F%2Fhubs.la%2FQ01N4FpG0|target:_blank”][vc_column_text uncode_shortcode_id=”178948″]Second, while some banks understand machine learning\u2019s potential, they are concerned that the investment required to shift to machine learning will mean too much disruption for their operations. Taken together, banks have been reluctant to invest in machine learning technology.<\/span><\/p>\n

How Feedzai Delivers Next-Generation Anti-Money Laundering Solutions<\/span><\/h3>\n

These are significant obstacles to machine learning implementation for AML operations. But Feedzai\u2019s AML solution optimizes alerts to help reduce compliance officers\u2019 workloads. Here\u2019s how.<\/span><\/p>\n

Compliance Teams Appreciate Optimized AML Alerts<\/span><\/h4>\n

Next-generation anti-money laundering capabilities start with more effective AML alerts. Feedzai\u2019s AML optimization capabilities prioritize the most critical alerts, creating a domino effect that addresses many other obstacles outlined here. Compliance officers will be able to focus on higher-priority alerts. These alerts require the greatest attention because they are more likely to result in a SAR filing. When the teams are at capacity, they will know exactly what to address first without feeling they\u2019ve missed potentially critical alerts. In other words, AML optimization can reduce work fatigue.<\/span><\/p>\n

Keep Existing Rules-based Systems<\/span><\/h4>\n

Many banks put off implementing AI and machine learning investments. This shift can be especially challenging for banks that still rely on rules-only legacy banking systems. Fortunately, banks that cannot immediately shift away from their rules-based system can still benefit from using machine learning for AML. Instead, banks can keep their legacy systems and add machine learning capabilities on top of them. It also helps evaluate existing governance and risk-based approaches with an experienced vendor.\u00a0<\/span><\/p>\n

Embed AML Optimization into Existing Solutions<\/span><\/h4>\n

Feedzai\u2019s AML optimization capability can enhance existing systems by adding an intelligent machine learning layer. With this approach, banks don\u2019t have to wait until a contractual agreement with another vendor expires. They can start seeing the benefits of next-generation anti-money laundering solutions immediately.\u00a0<\/span><\/p>\n

A rules-based approach to AML is insufficient in the digital age. Banks need to embrace next-generation anti-money laundering solutions to meet today\u2019s financial crime challenges. They also need to optimize their AML alerts to reduce false positives, employee burnout, and avoid fines. Machine learning is the smart way to supplement existing rules-based systems and deliver on regulatory priorities.<\/span>[\/vc_column_text][\/vc_column_inner][vc_column_inner column_width_percent=”100″ gutter_size=”3″ overlay_alpha=”50″ shift_x=”0″ shift_y=”0″ shift_y_down=”0″ z_index=”0″ medium_width=”0″ mobile_visibility=”yes” mobile_width=”0″ width=”2\/12″][\/vc_column_inner][\/vc_row_inner][\/vc_column][\/vc_row]<\/p>\n<\/div>","protected":false},"excerpt":{"rendered":"

The challenges of modern financial crime are pushing banks ever more into the vanguard. Banks worldwide are grappling with a $2T money laundering problem that requires next-generation anti-money laundering solutions. These organizations need to be armed with the latest technology to avoid being overwhelmed.<\/p>\n","protected":false},"author":110,"featured_media":121703,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"inline_featured_image":false,"footnotes":""},"categories":[75],"tags":[68,77],"acf":[],"yoast_head":"\nNext-Generation Anti-Money Laundering Solutions | Feedzai<\/title>\n<meta name=\"description\" content=\"Feedzai\u2019s next-generation anti-money laundering solution prioritizes alerts to address key pain points without retiring rules-based systems.\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/feedzai.com\/blog\/how-feedzai-embeds-next-generation-anti-money-laundering-solution-optimization\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" 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