Topics Types of Bank Fraud 12 Most Common Types of Bank Fraud Account Takeover (ATO) Fraud Check Fraud ACH Fraud Real-time Payment Fraud First-Party Fraud Wire Fraud Zelle Fraud Types of Card Fraud Credit Card Fraud Debit Card Fraud Lost or Stolen Card Fraud Card Skimming Chargeback Fraud Card Not Present (CNP) Fraud Fraud Defenses Anti-Money Laundering (AML) Crowdsourced Abuse Reporting Device Fingerprinting Real-time monitoring Email Reputation Service IP Reputation Service SR 11-7 Compliance Supervised Machine Learning Suspicious Activity Reports (SARs) Two-Factor Authentication (2FA) Unsupervised Machine Learning Fraud Tactics Bot Attacks Call Center Scams Card Cloning Credential Stuffing Data Breaches Deepfakes Device Emulators GPS Spoofing Money Mule Scams P2P VPN Networks Phishing Attacks SIM Swap Fraud URL Shortener Spam Web Scraping Fraud Tech Device Intelligence Feature Engineering Identity (ID) Graphing Fraud Types Application Fraud Transaction Fraud Payment Fraud Bust-Out Fraud Buyer-Seller Collusion Content Abuse Money Laundering Loan Stacking Romance Scams Synthetic Identity Theft Cryptocurrency Scams Pig Butchering Scams Anti-money Laundering (AML): Rules for Catching Financial Crime In the hit TV show Breaking Bad, lawyer Saul Goodman gives a funny yet precise explanation of money laundering. He persuades his client to buy a nail salon to kickstart the three elements of money laundering: placement, layering, and integration. First, they put dirty money in the salon (placement). The money mixes in with the clean cash flow (layering). Finally, their profits appear as pure, taxable income (integration). Every year, somewhere between $800 billion and $2 trillion dollars go through this exact process. What’s worse, more than 90% of money laundering isn’t caught. That’s why regulations require anti-money laundering (AML) at all financial institutions. In this wiki we’ll explain everything you need to know about AML. What is anti money laundering? Anti-Money Laundering (AML) is a program financial institutions follow to stop money laundering. AML also commonly refers to the set of laws they must follow to create that program. AML regulations mandate institutions actively monitor for, prevent, detect, and report money laundering. These rules also include taking the same measures against terrorism financing. Anti-money laundering compliance Under AML regulations, financial institutions legally must have certain capabilities. There are several laws that mandate different parts of AML. The Bank Secrecy Act (BSA) mandates AML must have: Internal controls, policies, and procedures A designated AML officer Ongoing employee training Independent testing The USA Patriot Act, which expands the scope of the BSA, mandates AML must: Include customer identification procedures Have enhanced due diligence for high-risk customers Report suspicious activity to the Financial Crimes Enforcement Network (FinCEN) Under the Office of Foreign Assets Control (OFAC) Regulations, AML must enforce OFAC, economic, and trade sanctions. This includes those against targeted foreign countries, terrorists, international narcotics traffickers, and others. Finally, as part of Customer Due Diligence (CDD) requirements, FinCEN requires AML must: Collect and verify beneficial ownership information for legal entity customers Conduct ongoing monitoring of customer transactions Make risk assessments These regulations aren’t limited to banks and traditional financial institutions. FinCEN issues AML regulations for money services, casinos, and precious metals, stones, or jewelry dealers too. Failure to follow AML regulations can result in significant fines and even legal action. How does anti-money laundering work? Traditional rules-based AML systems use predefined scenarios or “rules” to identify suspicious activity. These systems first analyze huge volumes of transactional data. Then they identify any transactions that meet the rules indicating money laundering and flag them. Flagged transactions go to a fraud investigator for further review as part of the case management process. Traditional rules-based AML systems follow a similar framework: Systems analyze transactional data for scenarios that break the “rules” and flag them. Transactions that meet the predefined rules generate alerts and flag the activity as suspicious. AML analysts investigate to determine if the transaction is unlawful. If it is, the analyst may file a suspicious activity report (SAR) with regulatory authorities. Predefined rules are regularly updated based on new data and feedback to improve the accuracy of the system. This process detects layering money laundering and placement by catching strange behavior. In some cases, it can also detect money laundering past the integration phase if a criminal makes an irregular transaction. Although traditional rules-based AML systems are effective, they have limitations. Chief among them is generating a high number of false positives. These require significant time and resources to investigate, and disrupt good customer experience. Older rules-based systems also struggle to identify new and emerging patterns of money laundering. What is AML transaction monitoring? AML transaction monitoring is the actual process an AML system follows to detect patterns of money laundering. It involves all the steps listed above, and is a key component of any AML system. In recent years, AI and machine learning have boosted AML transaction monitoring capabilities. Some transaction monitoring systems, like DataVisor’s, detect suspicious transactions in milliseconds. Learn more about AML transaction monitoring from a CPO’s perspective What is anti-money laundering certification? AML certifications are credentials awarded to individuals who prove expertise in AML compliance. Some common AML certifications include: Certified Anti-Money Laundering Specialist (CAMS): This is the most widely recognized AML certification. Certified Fraud Examiner (CFE) Certified Financial Crime Specialist (CFCS) Who needs an anti-money laundering certificate? AML certification benefits any professional working in risk management, compliance, or fraud operations. While they’re not always required by law, its common for employers to require or prefer AML-certified staff. Regulators often mandate that institutions have a certain number of AML-certified staff members. Modern anti-money laundering using artificial intelligence AI and machine learning have upgraded AML by making it both cheaper and more effective. These algorithms analyze massive volumes of data far exceeding what traditional rules systems can do. They also detect suspicious patterns much faster and more accurately. Plus, reduced false positives and fewer manual reviews help institutions get more on their investment. AI and ML can also assess the risk of money laundering associated with individual transactions or customers. They analyze historical data, customer behavior, and digital fingerprint to assign risk scores. These help focus investigations and trigger enhanced due diligence measures. AI is always learning and adapting to new patterns of money laundering, so its accuracy over time improves too. With money laundering techniques evolving rapidly, this is particularly important. Anti-money laundering with DataVisor DataVisor’s AML platform leverages a best-in-class AI-powered platform to: Surface unknown crimes and uplift threat detection by over 35% Reduce false positives by over 50% Enhance identity resolution with data enrichment and linkage analysis Boost efficiency by 10x with alert triage and prioritization Get a personalized demo of how DataVisor can revolutionize your AML solution by speaking with one of our experts.