July 2, 2026
Last Updated: June 2, 2026
Money laundering is the process of making illegally obtained funds appear legitimate. Criminals route proceeds from drug trafficking, fraud, human trafficking, and other predicate offenses through financial systems to disguise their origin.
The three standard stages are placement (introducing dirty money into the financial system), layering (moving funds through complex transactions to obscure the trail), and integration (reinserting funds into the legitimate economy through asset purchases or ordinary business activity).
For payment companies, specifically acquirers, issuers, processors, and fintechs, this creates direct regulatory exposure because the same infrastructure that moves legitimate funds also moves illicit ones. When monitoring systems fail to catch it, the penalties are severe and reputational damage compounds. The money laundering statistics throughout this article quantify what those penalties look like in practice.
Financial services institutions face the greatest money laundering exposure because illicit funds must pass through the financial system to be laundered. They can't reach the layering or integration stage without first passing through a payment institution.
Within financial services, the exposure isn't evenly distributed:
Payment processors and acquirers sit at the first point of entry for illicit commercial activity. Fraudulent merchants and transaction launderers introduce criminal proceeds through the merchant acceptance layer, which acquirers and payment facilitators hold liability for. Any institution growing its merchant base through digital onboarding is actively expanding that exposure.
Digital banks, wallet providers, and instant payment networks face growing risk from mule account networks; these accounts receive stolen or laundered funds and disperse them rapidly across multiple wallets and payment corridors before monitoring systems can flag the pattern.
Depository institutions broadly are the primary enforcement target for federal regulators. The concentration of federal money laundering cases in five US judicial districts, namely the Southern District of Florida, Eastern District of Texas, Southern District of New York, Southern District of California, and District of Massachusetts, maps directly onto the geographic corridors where payment flows are densest and financial institution operations are most concentrated.
The TD Bank case is the most data-rich illustration of what institutional exposure looks like in practice. Three separate money laundering networks identified the same institution simultaneously and exploited it for years. That's not a coincidence; it's the natural behavior of criminal networks probing for institutions with the weakest monitoring coverage. The money laundering statistics in the next section show how that behavior has translated into enforcement outcomes at scale.
For financial institutions, money laundering risk shows up on two fronts: regulatory penalties and the ongoing burden of compliance systems that can't keep pace with the threat. Both are growing, and the data suggests neither is producing proportional protection.
In October 2024, TD Bank paid $3.1 billion in combined penalties to the DOJ, FinCEN, OCC, and the Federal Reserve, the largest AML enforcement action against a US bank in history. The DOJ found that the bank deliberately excluded entire transaction categories from its monitoring system, allowing three separate money laundering networks to transfer over $670 million undetected across a six-year window.
Beyond the fine itself, TD Bank became the first US bank in history to plead guilty to conspiring to launder money, and also accepted multi-year independent monitoring and operational restrictions.
Across the wider enforcement landscape, FinCEN issued over $1.3 billion in civil money penalties across its entire FY2025 enforcement portfolio. That figure represents a single fiscal year of activity, not a one-time event.
A 2024 study by LexisNexis Risk Solutions, conducted by Forrester Consulting, found that financial crime compliance costs in the US and Canada reached $61 billion, while EMEA institutions spent $85 billion in the same period. 99% of institutions surveyed reported that their compliance costs increased.
The gap between these two data points, record compliance spend and record enforcement penalties in the same period, is the most important observation in this data. Higher spending hasn't translated into fewer large-scale failures. The TD Bank case wasn't a budget failure; it was a coverage failure, caused by an architecture that excluded major transaction categories from monitoring entirely.
For payment companies, the cost equation runs both ways. Underinvesting in monitoring creates conditions for eight-figure penalties; investing in the wrong architecture, specifically high-cost systems that don't cover the full range of transaction volume, produces the same result at greater expense.
Every statistic below was confirmed from the body content of its source URL. Each source URL is linked once, on its first citation. Subsequent statistics from the same source reference the source name in italics only.
1. The US Sentencing Commission recorded 1,170 federal money laundering offenses in FY2025. (USSC FY2025)
2. Federal money laundering offenses have increased 41% since FY2021, from 830 cases to 1,170. (USSC FY2025)
3. The average sentence for a money laundering conviction in FY2025 was 69 months. (USSC FY2025)
4. 90% of individuals sentenced for money laundering offenses in FY2025 received a prison sentence. (USSC FY2025)
5. The median loss amount for money laundering offenses in FY2025 was $446,049, down from $566,530 in FY2023 and $526,000 in FY2024. (USSC FY2025)
6. 55% of sentences for money laundering in FY2025 fell below the Guidelines Manual range. (USSC FY2025)
7. 25% of money laundering defendants were convicted of an offense carrying a mandatory minimum penalty; 49% of those were relieved of the penalty. (USSC FY2025)
8. The Southern District of Florida recorded the most federal money laundering cases in FY2025 with 75, followed by the Eastern District of Texas (53) and the Southern District of New York (50). (USSC FY2025)
9. US financial institutions filed 4.8 million Suspicious Activity Reports in FY2025, an increase of approximately 0.1 million compared to FY2024. (FinCEN FY2025)
10. FinCEN issued over $1.3 billion in civil money penalties in FY2025 against institutions that failed to meet Bank Secrecy Act and AML requirements. (FinCEN FY2025)
11. Since 2015, FinCEN has returned over $991 million to fraud victims through its Rapid Response Program. (FinCEN FY2025)
12. TD Bank agreed to pay $3.1 billion in combined penalties to the DOJ, FinCEN, OCC, and the Federal Reserve in October 2024, the largest AML enforcement action against a US bank in history. (ABA Banking Journal)
13. Three money laundering networks collectively transferred more than $670 million through TD Bank accounts between 2019 and 2023. (DOJ)
14. 92% of total transaction volume at TD Bank went unmonitored from January 1, 2018 to April 12, 2024. (DOJ)
15. That monitoring gap amounted to approximately $18.3 trillion in transaction activity passing through the bank without adequate controls. (DOJ)
16. TD Bank became the first US bank in history to plead guilty to conspiring to launder money. (DOJ)
17. Financial crime compliance costs in the US and Canada reached $61 billion in 2024, according to LexisNexis Risk Solutions and Forrester Consulting. (LexisNexis / Forrester)
18. 99% of financial institutions reported that financial crime compliance costs increased in 2024. (LexisNexis / Forrester)
19. Financial crime compliance costs in EMEA reached $85 billion in 2024. (LexisNexis Risk Solutions)
These money laundering statistics don't stand alone. Read together across federal sentencing records, FinCEN enforcement data, DOJ case filings, and two independent compliance cost studies, five consistent patterns emerge, each with a direct implication for payment companies and risk teams.
Between FY2021 and FY2025, federal money laundering cases rose 41%, but median loss per offense followed a different trajectory, starting at $293,359 in FY2021, rising sharply to $566,530 in FY2023, then declining to $526,000 in FY2024 and $446,049 in FY2025.
More cases, but smaller offenses per case. This isn't a contradiction; it's a structural shift in how laundering activity is distributed. The FY2023 peak likely reflects large conspiracies, including COVID-era fraud proceeds, working through the federal system. The subsequent decline in median offense size, while case volume continues rising, points toward more frequent, smaller-value activity, with laundering spreading across higher transaction counts rather than concentrating in fewer large movements.
Any risk team calibrating its detection around high-value transaction thresholds is increasingly missing the enforcement target, because the threat is distributed, not concentrated.
Convictions carry a 90% imprisonment rate and average sentences of 69 months, among the harshest financial crime penalties in the federal system.
Despite this, federal money laundering offenses grew in three of the four years between FY2021 and FY2025; the one year it declined (FY2024, 1,095 cases) was followed immediately by a new five-year high in FY2025 (1,170 cases). The enforcement trajectory is upward, prosecution severity is among the highest in financial crime, and yet activity keeps expanding.
This pattern shifts where the burden lies. If criminal deterrence at the prosecution level isn't suppressing laundering volume, the only effective control point is prevention at the infrastructure level, at the moment funds enter the payment system, before layering begins, and long before any prosecution can occur.
US and Canadian institutions spent $61 billion; EMEA institutions spent $85 billion; 99% reported cost increases. These are the highest AML compliance cost figures on record.
The same data period produced over $1.3 billion in FinCEN civil penalties in a single fiscal year and a $3.1 billion enforcement action against a single institution. The disconnect between total compliance spend and monitoring outcomes is real and measurable.
The TD Bank case explains the mechanism. The bank's failure wasn't caused by insufficient budget; its monitoring system was deliberately structured to exclude entire transaction categories while costs were managed through a "flat cost paradigm." Compliance spending that doesn't translate into transaction coverage doesn't translate into regulatory protection. How resources are deployed matters more than how much is deployed.
The TD Bank case provides the clearest public record of an AML failure in the current enforcement era. 92% of transaction volume went unmonitored, representing approximately $18.3 trillion in activity without scrutiny. Three separate money laundering networks operated through the same institution simultaneously for years, collectively moving more than $670 million. The combined penalty was $3.1 billion; TD Bank became the first US bank in history to plead guilty to money laundering conspiracy.
The cause wasn't algorithmic weakness or inadequate rules. The bank deliberately excluded domestic ACH transactions, most check activity, and numerous other transaction types from its automated monitoring system, and didn't update that system for nearly a decade despite known deficiencies.
This failure mode isn't unique to TD Bank. Any institution whose monitoring system doesn't cover the full scope of its transaction types carries an equivalent structural gap. The sophistication of the rules applied to monitored transactions is secondary to what fraction of total volume is monitored at all.
The top five federal districts for money laundering cases in FY2025 aren't randomly distributed. The Southern District of Florida (75 cases), Eastern District of Texas (53), Southern District of New York (50), Southern District of California (44), and District of Massachusetts (37) account for a disproportionate share of total federal prosecutions.
Three of the five are in states with major international payment corridors and high cross-border transaction volumes, while Southern New York reflects the density of financial institution operations in the country's largest financial center. This pattern has held across multiple USSC fiscal years; it's not a one-year anomaly.
Enforcement follows where payment flows are densest and regulatory infrastructure is most established. For payment companies with significant operations in these corridors, that means statistically higher proximity to federal enforcement activity and a clear case for prioritizing monitoring resources accordingly.
The five patterns above point to three questions that matter more than any feature list when evaluating anti-money laundering solutions.
The clearest lesson from the TD Bank case is that 92% of transaction volume went unmonitored. Before evaluating detection capability, you need to know what percentage of your total transaction flow actually passes through your AML system. A monitoring system that scores accurately within a narrow transaction scope produces worse outcomes than a less sophisticated system with full transaction coverage.
The median money laundering offense isn't a single large transaction; it's a pattern across multiple transactions, with a median value of $446,049 distributed across time. Systems that score transactions individually without maintaining behavioral profiles of accounts and merchants will miss the velocity changes, counterparty anomalies, and inflow-to-outflow patterns that define modern laundering activity. Entity-level monitoring is what surfaces the behavior that individual transaction scoring can't see.
When three separate money laundering networks can operate through the same institution simultaneously, as in the TD Bank case, that institution's own historical data contains no signal of coordination or shared origin.
A system that draws on centralized transaction data from across the payments network, including data from issuers, acquirers, and payment facilitators, can identify the network-level patterns that no single institution's siloed data reveals. That's the structural ceiling in-house models can't overcome, regardless of how well they're calibrated.
The data in this article is US-sourced, but the coverage-failure thesis this data supports isn't US-specific. European payment companies face equivalent regulatory pressure through EU AML legislation, EBA supervisory guidelines on AML/CFT, and the mandate of AMLA, the EU's Anti-Money Laundering Authority established in 2024. Any institution that can't demonstrate comprehensive transaction monitoring faces the same consequences under European rules, including fines, license conditions, and operational restrictions. The three questions above apply wherever you operate.
Explore how these dimensions compare across vendors in the best AML software assessment.
Payment companies absorb money laundering risk on two fronts: penalties when monitoring systems fail and rising compliance costs that don't automatically produce better coverage. The five patterns in this research point to the same root cause, a monitoring coverage gap that legacy AML tools are structurally unable to close.
Most AML systems train on each institution's isolated transaction history. Many processors legally can't combine their issuing and acquiring data. The result mirrors the TD Bank case, where monitoring covered only a fraction of actual transaction volume while the rest passed without scrutiny.
Fraudio's AML product combines rules-based controls with AI-driven modeling and link analysis, trained on a patented centralized dataset from issuers, acquirers, fintechs, and processors across the payments network. Because models see activity across the full network, coordinated laundering patterns become visible from the first transaction processed, and integration takes days rather than months.
No precise global figure exists because successful laundering is designed to go undetected. The UNODC and FATF estimate the amount laundered annually is equivalent to between 2% and 5% of global GDP, but these are estimates by nature, not confirmed counts. For the visible portion, the US Sentencing Commission recorded 1,170 federal money laundering offenses in FY2025, up 41% from FY2021.
The three stages of money laundering are placement, layering, and integration. Placement introduces illicit funds into the financial system through cash deposits, merchant transactions, or digital transfers. Layering obscures the trail through complex, multi-step transactions across institutions and jurisdictions. Integration reintroduces the funds into the legitimate economy through investments, real estate, or ordinary business activity that appears indistinguishable from clean money.
In FY2025, the average sentence was 69 months, and 90% of convicted individuals received a prison sentence. Median loss per offense was $446,049, down from a FY2023 peak of $566,530. 25% of defendants faced mandatory minimum penalties, and 55% of sentences fell below the Guidelines Manual range.
Payment infrastructure, including acquirers, issuers, processors, digital banks, and wallet providers, carries the highest concentration of exposure because illicit funds must pass through the financial system to be laundered. The TD Bank case is the clearest public record of institutional exposure at scale; a monitoring gap covering 92% of transaction volume allowed three separate networks to move more than $670 million undetected for years. Geographic enforcement data from the USSC further shows that prosecutions concentrate in states with major international payment corridors.
AML compliance costs reached $61 billion for US and Canadian institutions in 2024, according to LexisNexis Risk Solutions and Forrester Consulting. EMEA institutions spent $85 billion in the same period. 99% of institutions surveyed reported cost increases. Despite that level of spending, FinCEN issued over $1.3 billion in civil money penalties in FY2025 alone, showing that a bigger budget doesn't automatically produce adequate monitoring coverage.
The TD Bank case settled in October 2024 with $3.1 billion in combined penalties. Between January 2018 and April 2024, 92% of total transaction volume went unmonitored, approximately $18.3 trillion in activity, which allowed three money laundering networks to transfer more than $670 million through TD Bank accounts. TD Bank became the first US bank in history to plead guilty to conspiring to launder money. The failure wasn't a technology failure; it was a deliberate coverage failure caused by excluding entire transaction categories from the monitoring system.
Federal money laundering prosecutions in the US have increased 41% since FY2021, reaching 1,170 cases in FY2025. The trend isn't linear; cases rose from 830 in FY2021 to a peak of 1,131 in FY2023, declined to 1,095 in FY2024, then rebounded to a new five-year high in FY2025. FinCEN also issued over $1.3 billion in civil money penalties in FY2025. Enforcement activity at both criminal and civil levels is at multi-year highs.
Payment companies evaluating the best AML software should focus on three questions: what percentage of total transaction volume does the system actually cover, whether it monitors entity behavior over time rather than scoring individual transactions in isolation, and whether it trains on network-level data from across the payments network or only on the institution's own siloed history. Coverage of transaction volume is the most urgent of the three, given that it's the variable the TD Bank case identifies as the defining failure mode in the largest AML enforcement outcome in US banking history.
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