Glossary

Anti-money laundering (AML)

The laws, controls, and analytics financial institutions use to detect and prevent the disguising of illegal funds.

Also called: AML

Anti-money laundering (AML) covers the laws, regulatory obligations, and internal controls that banks, payment firms, and other regulated entities use to detect and prevent the disguising of illegally obtained funds, and to report suspicious activity to authorities.

Programs are built on know your customer checks performed at onboarding, ongoing transaction monitoring that screens activity against rules and known typologies — such as structuring deposits just under a reporting threshold — and periodic risk-scoring of customers based on their profile and behavior. Specific thresholds, reporting requirements, and definitions vary by jurisdiction and regulator, so an AML program is built against the specific rules that apply to it, not a single global standard.

AML systems generate a large volume of alerts, most of which turn out to be false positives, so analytics teams focus heavily on reducing that volume without missing genuine cases, and on documenting model behavior for model risk management and regulatory examination. This description is general background, not legal or compliance advice — specific AML obligations depend on jurisdiction, license type, and applicable regulator guidance.

Last reviewed September 22, 2026

In the index now

Related terms

Know your customer (KYC) Verifying a customer's identity and assessing their risk before and throughout a business relationship. Transaction monitoring Ongoing, largely automated screening of financial transactions to flag activity that may indicate fraud or laundering. Fraud detection Identifying transactions, claims, or behavior that are likely fraudulent, using rules, statistics, or machine learning. Risk scoring Assigning a numeric score to a customer, transaction, or entity that estimates the likelihood of a specific negative outcome. Model risk management (MRM) The governance practice of validating, monitoring, and controlling the risk that a model is wrong or misused.

Related guides

How to choose a banking and credit analytics tool Credit-risk suites, decisioning platforms, origination systems, data infrastructure and model-building tools solve different problems in banking. How to choose a crypto and on-chain analytics tool Crypto analytics splits into price data, wallet-labeling, raw query access and compliance — pick by the job, not the chart quality. How to choose a fraud detection tool Fraud platforms split by primary signal — biometrics, device data, consortium networks, compliance case management — and by who they're built for.