Feedzai Anti-Money Laundering

A DATA SCIENCE, RISK-BASED APPROACH FOR AML

Add advanced machine learning to your existing anti-money laundering efforts. Feedzai AML offers new capabilities that assess the risk of your customers and their transactions. Powered by artificial intelligence, Feedzai AML takes a risk-based approach and works for every customer channel.

Reduce transaction monitoring
false positive rates

Automate workflows, including risk
review and case management

A MACHINE LEARNING LAYER

Other AML software contain rules that are rarely updated or modernized. Criminals eventually learn the rules and bypass them.

Feedzai AML augments existing rules systems, mines data to detect new anomalies that indicate evolving money laundering patterns of placement, layering, or integration. Feedzai adds unprecedented machine learning capabilities to reduce false positives and increase anomaly detection.

INVESTIGATIVE TOOLS

Intuitive workflows help AML analysts get started quickly. Feedzai’s Next Alert feature enables them to prioritize alerts. Multi-dimensional analytics and visualizations make the risks easy to spot.

POWERFUL TECHNOLOGY

Segment-of-One profiling

Create hypergranular behavioral profiles in real time for individual attributes, while baselining expected behaviors.

Automated discovery

Feedzai’s integrated development and test environment instantaneously takes models from build to production.

Whitebox explanations

Use human-friendly explanations to quickly review transactions and make decisions to meet compliance standards.

EXISTING AML SOLUTIONS ARE OFTEN…

INEFFICIENT: The typical compliance-based approach introduces unwanted friction for customers. It also generates many unnecessary transaction alerts. In fact, 95% of AML system-generated alerts are closed as “false positives” in the first phase of review.¹

COMPLEX: AML implementations are often fragmented, involving multiple vendors. Furthermore, most financial institutions lack the technology to harness big data effectively for AML.

EXPENSIVE: Manual investigation costs are high, with large financial institutions employing hundreds of people in compliance and investing an average of $60 million a year in Know Your Customer (KYC) alone.²

INEFFECTIVE: Despite AML efforts, worldwide AML fines were $42 billion in 2016.³

THE PROBLEM KEEPS GETTING WORSE

Money laundering is evolving quickly to thwart existing rules-based detection capabilities. Criminals use evasion techniques powered by advanced technology. Money laundering schemes now go beyond trafficking, with successfully laundered funds often linked to bribery, influence peddling, corporate crime, or political intrigue. For financial institutions, these emerging threats are challenging to navigate and are compounded by real-time transactions and a fragmented payment market.

Over $800 billion is laundered globally each year.⁵

Money laundering activity increases 11 percent annually.⁶

Fewer than 1 percent of suspected funds are frozen or confiscated.⁷

IT’S NO WONDER CUMULATIVE FINES SINCE 2009 ARE EXPECTED TO

EXCEED $400 BILLION BY 2020

SEE FEEDZAI IN ACTION

Feedzai keeps banks safe from money laundering. We’ll show you how Feedzai Anti-Money Laundering works, in real time.

Learn More

Ebook


Download this ebook to learn how to choose the right machine learning platform to manage risk as you grow.

Ebook


Read this ebook to learn how machine learning works and how it can be applied to risk management in banking and payments.

Blog


Read this blog post to learn how machine learning will change the way banks fight fraud.

Case Study


Download this case study to learn about a bank's main fraud challenges and the full solution that was deployed alongside the bank’s identity, eligibility and fraud risk verification process.

Webinar


View this on-demand webinar from Feedzai and Aite Group to learn how financial institutions are using machine learning to combat account takeover (ATO) and new account fraud, and why orchestration of authentication is the antidote to ATO fraud.

Ebook


Download this ebook to learn how to build a complete customer view and how machine learning identifies fraud patterns that evade the human eye.
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