Transaction Monitoring and Filtering Programs (TMPs)

To be effective, transaction monitoring and filtering programs must be designed with several core elements:

• Risk-Based Approach: TMPs should align with the institution’s specific risk profile, adjusting monitoring thresholds according to customer types, geographic exposure, products, and services.

• Automated and Manual Processes: While automation helps flag unusual patterns at scale, human oversight remains crucial for investigating and validating alerts to reduce false positives.

• Real-Time and Batch Monitoring: Institutions may use real-time systems to block suspicious transactions before processing, alongside batch monitoring that analyzes completed transactions for patterns indicative of financial crime.

• Alert Management and Case Investigation: TMPs must have structured workflows for handling alerts, escalating cases, conducting investigations, and, where necessary, filing Suspicious Activity Reports (SARs).

• Model Validation and Tuning: Regular testing and validation of monitoring models ensure that systems stay effective as typologies and customer behaviors evolve.

Despite their critical role, TMPs face several operational and strategic challenges:

• High False Positive Rates: Many systems generate excessive alerts, overwhelming compliance teams and causing potential delays in detecting real threats.

• Evolving Criminal Techniques: Criminals continually develop new methods to bypass traditional monitoring parameters, requiring constant adaptation.

• Data Quality Issues: Incomplete or inaccurate data inputs can severely limit the effectiveness of monitoring and filtering activities.

• Regulatory Pressure: Increasingly stringent regulatory expectations demand not just the presence of a TMP but proof of its effectiveness and adaptability over time.

Financial institutions are under pressure to invest in smarter solutions that balance compliance obligations with operational efficiency.

Recent advances in technology are transforming transaction monitoring and filtering:

• Artificial Intelligence (AI) and Machine Learning (ML): These technologies enable the identification of complex, non-linear transaction patterns that traditional rule-based systems might miss.

• Behavioral Analytics: Systems can now establish customer behavior baselines and detect deviations in real time.

• Graph Analytics: Mapping relationships between accounts and transactions can reveal hidden networks indicative of fraud or money laundering.

• Natural Language Processing (NLP): Helps filter unstructured data sources (such as payment messages) to better detect sanctions risks and prohibited activities.

These technologies are helping financial institutions move towards more predictive, rather than reactive, monitoring.

Regulators worldwide emphasize the importance of robust transaction monitoring programs. Key best practices include:

• Documentation: Institutions must maintain thorough documentation of TMP policies, procedures, model tuning, and system changes.

• Independent Testing: Regular audits and third-party validations ensure that monitoring and filtering systems are effective and compliant.

• Training: Compliance and operations staff must be continuously trained on emerging risks, typologies, and system usage.

• Governance: Senior management should actively oversee the transaction monitoring framework, ensuring it aligns with the broader compliance strategy.

Failure to maintain effective TMPs can result in severe financial penalties, reputational damage, and regulatory sanctions.

As financial ecosystems become more complex with digital payments, cryptocurrencies, and instant transfers, the future of TMPs will likely involve:

• Real-Time, Cross-Border Monitoring: Seamless tracking of international transactions for quicker detection of suspicious activity.

• Greater Integration of External Data Sources: Leveraging open banking data, third-party intelligence, and public registries to enhance risk profiling.

• Explainable AI (XAI): Ensuring that advanced AI-driven systems can clearly justify why alerts are generated, a critical factor for regulatory transparency.

Money laundering and related financial crime pose a massive global challenge, with an estimated 2–5% of the world’s GDP (up to $2 trillion) flowing through illicit channels each year. To combat this, financial institutions employ Transaction Monitoring and Filtering Programs (TMPs) as a core part of their anti-money laundering (AML) defenses. Broadly, a TMP refers to automated systems and processes that monitor customer transactions for suspicious activity and filter transactions against watchlists (like sanctions lists) to block prohibited payments. Regulators now mandate these programs – for example, New York’s banking rule requires firms to maintain a transaction monitoring program for BSA/AML violations and a filtering program to prevent OFAC-sanctioned transactions. The primary goal is to detect and report potential money laundering, terrorist financing, and other illicit activities in real time, while preventing forbidden transactions from ever executing. In an era of fast digital payments and cross-border fund flows, effective TMPs have become indispensable to safeguarding the financial system’s integrity and complying with AML laws.

Transaction monitoring is a legal obligation for banks and many other financial businesses. Under AML regulations (such as the U.S. Bank Secrecy Act and EU directives), institutions must scrutinize transactions and file Suspicious Activity Reports (SARs) when detecting possible money laundering or terrorist financing. TMPs serve as the “eyes” of an institution’s AML program – continuously reviewing deposits, withdrawals, transfers, and payments for red flags. These systems help institutions adhere to the “know your customer” and reporting requirements by identifying unusual patterns that may indicate the placement, layering, or integration of illicit funds (classic stages of money laundering). They also overlap with fraud prevention by flagging anomalies like unauthorized transactions or identity theft attempts. Importantly, modern AML compliance is risk-based: banks must focus resources on higher-risk customers, products, and geographies. This means customer risk scores and profiles guide the intensity of monitoring applied. For example, a client with overseas shell company links or politically exposed status will merit closer, continuous monitoring than a low-risk local customer. Regulators emphasize that an effective AML program should be reasonably designed and risk-based, dedicating more attention to higher-risk activities. Overall, TMPs are the frontline tools that help compliance officers detect and interrupt illicit finance in day-to-day operations, supporting the broader AML mission of protecting national security and the financial system.

Behind the scenes, transaction monitoring relies on a combination of data analytics, rule-based logic, and increasingly, artificial intelligence and machine learning (AI/ML). At a high level, TMPs work through several stages:

1. Data Collection: The system aggregates transaction data from across the institution – including payment records, account activity, customer profiles, and reference data (e.g. country or industry risk indices). This may span multiple sources and channels, from wire transfers and credit card swipes to cryptocurrency wallet movements. High-quality, comprehensive data is the foundation of effective monitoring.
2. Rule-Based Filtering: Institutions configure rules and scenarios that define what “suspicious” activity looks like. These rules are often derived from known red flags (e.g. transactions above a certain dollar amount, multiple rapid transfers to new beneficiaries, use of high-risk foreign jurisdictions). When a transaction meets a rule’s criteria or threshold, it is flagged for review. Traditional systems have hundreds of such rules covering patterns like structuring (breaking up large cash deposits), round-number transfers, or rapid movement of funds through many accounts.
3. Anomaly Detection and Risk Scoring: Beyond static rules, modern TMPs use advanced analytics and ML algorithms to detect anomalies – patterns that deviate from a customer’s normal behavior or peer group. Machine learning models ingest granular behavioral data and can uncover complex patterns that rigid rules might miss. These models continuously improve by learning from past alerts. The system may also assign risk scores to transactions or customers (e.g. 0–100) based on myriad factors (amount, origin/destination, customer profile, etc.), helping prioritize alerts. FinCEN has noted that relying solely on simplistic scoring (such as categorizing risk only by customer type or product) is inadequate – instead, scores should dynamically reflect the customer’s actual activity and be updated over the relationship.
4. Alert Generation: When a rule trigger is hit or an AI model flags an anomaly, the TMP generates an alert. Each alert indicates a transaction or account that may require investigation. Modern systems often include an alert ranking or scoring mechanism, so that higher-risk alerts (for example, involving known typologies or higher risk ratings) are prioritized for faster review. This helps compliance teams manage large volumes of alerts efficiently.
5. Investigation and Review: Compliance analysts review alerts to determine if they truly indicate suspicious behavior. This involves examining the transaction in context – looking at related account activity, customer KYC information, and sometimes external intelligence (like adverse media or blockchain analyses for crypto transactions). The goal is to weed out “false positives” (legitimate behavior that tripped a rule) and escalate true suspicious cases. Effective TMPs provide case management tools, linking related alerts and accounts to give investigators a holistic view (for instance, grouping multiple alerts across accounts that belong to the same customer or scheme).
6. Suspicious Activity Reporting and Resolution: If after investigation a transaction is deemed suspicious, the institution files a SAR or equivalent report to the national Financial Intelligence Unit. The TMP workflow must capture documentation of the investigation and support timely reporting to authorities. At the same time, the account may be subject to enhanced monitoring, restrictions, or even closure depending on severity. Feedback loops are crucial here – if an alert was a false positive, analysts will feedback that information to tune the system (adjust thresholds, suppress irrelevant rules, etc.), improving accuracy over time. Regulators expect that TMPs be regularly tested and tuned in this way to ensure scenarios are effective and data feeds complete.

Early-generation transaction monitoring systems were mostly rule-based and generated many false positives, keeping compliance teams busy chasing benign alerts. Today, the trend is to augment or replace these with AI/ML-driven systems that can adapt to evolving patterns. Machine learning models can analyze vast datasets and identify nonlinear patterns indicative of money laundering, achieving higher detection rates with fewer noise alerts. In fact, one major bank reported improving its suspicious activity detection by up to 40% and efficiency by 30% after deploying ML in place of traditional rules. These technologies enable real-time monitoring, where certain transactions (e.g. online payments or crypto transfers) can be screened instantaneously before completion, rather than only in after-the-fact batches. Natural language processing (NLP) is also emerging – for example, to analyze unstructured data (payment memos, email communications) alongside transactions for context.

Crucially, TMPs also include filtering systems that screen transactions and customers against sanctions, terrorist lists, or other watchlists. A filtering engine will automatically compare payment details (names, account numbers, countries, etc.) to lists like the U.S. OFAC sanctions list and flag or block any matches. While conceptually separate from AML monitoring, sanctions screening often works in tandem with transaction monitoring – both are typically integrated into an institution’s compliance software. The technical challenge is balancing sensitivity and specificity: systems must catch illicit activity without overwhelming staff with false alarms. This requires ongoing calibration, good data quality, and periodic model validation. Regulators like the Hong Kong Monetary Authority (HKMA) advise banks to integrate external data and automation to enhance monitoring, but also to actively manage system parameters to reduce false positives and conduct ongoing reviews of detection rules. In sum, a well-designed TMP combines expert-defined rules with adaptive analytics, underpinned by strong governance and data management, to efficiently sift through millions of transactions and pinpoint the few truly suspicious ones.

The rise of cryptocurrencies and blockchain technology has introduced both new challenges and tools for transaction monitoring. On one hand, cryptocurrencies enable peer-to-peer value transfer without traditional intermediaries, creating new avenues for money laundering, fraud, and sanctions evasion. In 2023, illicit cryptocurrency transactions surged by over 80%, according to Chainalysis, underscoring the urgency for oversight in this arena. Money launderers have taken advantage of crypto mixers, privacy coins, and decentralized finance (DeFi) protocols to obscure fund flows. This means that crypto exchanges and other virtual asset service providers must implement TMPs analogous to banks, but tailored to blockchain activity. Indeed, by the end of 2025, all major crypto platforms will likely be required by regulators (and the latest FATF guidelines) to enforce robust KYC, transaction monitoring, and suspicious activity reporting programs.

On the other hand, blockchain’s public ledger offers a novel data source for monitoring. Sophisticated AML platforms now incorporate blockchain analytics – specialized tools that trace cryptocurrency transactions through the blockchain, flagging coins that have passed through darknet markets, ransomware wallets, or sanctioned addresses. These tools can deanonymize patterns by clustering wallet addresses and applying risk scores (e.g. identifying that funds came from a mixing service or a high-risk exchange). In practice, a crypto exchange’s TMP will generate alerts if, say, a customer’s bitcoin deposit can be traced back to a known illicit entity or if they attempt to send crypto to a sanctioned wallet. Traditional financial institutions are also increasingly interested in crypto-related monitoring: banks that permit transfers to and from crypto platforms use blockchain intelligence to assess the cleanliness of those funds before accepting them.

Blockchain is also spurring new regulatory responses. Regulators in major financial centers now require crypto businesses to follow AML/CFT rules similar to banks, and to share information about customers’ crypto transactions (the “Travel Rule”). Furthermore, as financial assets become tokenized (e.g. digital tokens representing real estate, art, or securities), TMPs will need to adapt to monitor transactions in these new markets. The tokenization trend poses challenges in tracing ownership and value transfers, leading to calls for expanded AML standards for tokenized asset platforms. In summary, blockchain has expanded the frontiers of transaction monitoring – introducing complex, pseudonymous transaction flows that demand cutting-edge analytics, while also providing transparent ledgers that can be harnessed to follow the money trail. A well-rounded TMP in 2025 and beyond must account for crypto-related risks and integrate blockchain data to truly cover the full spectrum of illicit finance.

TMPs are designed to mitigate a wide range of financial crime risks. The foremost target is money laundering – the process of making illicit money appear legitimate – but transaction monitoring also helps catch fraud schemes and other threats:

• Classic Money Laundering Patterns: Monitoring systems look for red flags in customer transactions that may indicate laundering. Common scenarios include unusual structuring of cash deposits (e.g. multiple just-under-the-threshold cash deposits to avoid reporting), rapid movements of funds through accounts (layering), transfers that don’t match a customer’s profile (like a local retail business wiring large sums abroad without clear reason), and use of known high-risk jurisdictions. Regulators have highlighted specific typologies to incorporate into monitoring, such as wire transfers from one originator to many different beneficiaries (or vice versa, many senders to one receiver) which could signal a funnel account or pooling scheme. Transactions passing through numerous countries or bank hops – especially if involving secrecy havens – are another warning sign of possible layering. Even certain instruments like frequent use of remote deposit capture (scanning and depositing checks digitally) have been flagged by U.S. authorities as potentially suspicious in context. TMPs use such patterns, among others, to detect when criminals attempt to cleanse dirty money through intricate webs of transfers.
• Fraud and Cybercrime: Modern transaction monitoring often converges with fraud detection, as many fraud scams leave telltale patterns in account activity. For example, account takeover fraud may result in atypical outbound transfers or purchases, triggering anomaly alerts. Identity theft or synthetic identity fraud can be spotted when “customers” behave in ways inconsistent with their stated profile. Monitoring can also catch unauthorized transactions – such as a surge of small-dollar debits that might indicate testing of stolen cards, or large purchases that deviate from a cardholder’s normal spending. As one RegTech provider notes, by analyzing transactions in real time, fintech companies can identify unusual patterns or anomalies that indicate fraud and take immediate action. Moreover, TMPs are being tuned to detect proceeds of cybercrime (e.g. ransomware payments, where funds might move into crypto exchanges or flow to known fronts). The convergence of AML and cybersecurity is growing; banks are increasingly integrating fraud analytics with AML monitoring to holistically tackle financial crime risk.
• Terrorist Financing and Sanctions Evasion: While less frequent, monitoring systems are also calibrated to detect potential terrorist financing, which often involves small transactions or unusual combinations of parties/locations. Certain patterns – like frequent international funds transfers to charities or entities in conflict regions – can hint at terror financing and prompt a closer look. Sanctions evasion is another risk addressed mainly through transaction filtering: the system will halt transfers involving blacklisted individuals, entities, or countries (for example, payments involving a sanctioned Russian bank). Given the geopolitical tensions and expanding sanctions regimes, screening for sanctioned parties in transactions is critical to avoid serious legal violations. Many banks have enhanced their sanctions screening with AI as well, to better catch spelling variations and complex ownership structures that bad actors use to evade filters.
• Trade-Based Money Laundering (TBML): In certain sectors like trade finance and commodities, launderers use the movement of goods and falsified invoices to disguise money flows. TMPs support detection of TBML by flagging transactions that don’t align with trade documentation or that involve known risky trade routes and counterparties. For instance, anLetter of credit payment that is far above the market value of the goods, or a chain of trade transactions with no logical business purpose, would appear anomalous. Regulators in Asia have pinpointed misuse of trade finance as a major risk area; Singapore’s new COSMIC platform (discussed below) specifically targets red flags in trade transactions and shell company activities.

In all these cases, the TMP’s job is to raise an alert so that humans can investigate further. It’s worth noting that no system catches everything – criminals constantly adapt, and false negatives (missed suspicious activities) can occur, especially if a program is poorly calibrated. That’s why regulators stress ongoing risk assessment and updating of monitoring controls. The Sixth EU AML Directive explicitly promotes rapid information sharing on suspicious transactions across borders, recognizing that money launderers often exploit gaps between jurisdictions. By addressing the above risks, robust TMPs not only help institutions avoid regulatory penalties, but also protect consumers and markets from the damages of financial crime (from fraud losses to the societal harms of organized crime and terrorism financed through the banking system).

While banks pioneered transaction monitoring, today TMPs are implemented across a spectrum of financial and even non-financial sectors, each with its own nuances:

• Retail and Commercial Banking: Banks apply TMPs to monitor standard deposit accounts, loans, wire transfers, and credit card transactions. For retail banking, a typical use case is detecting structuring — e.g. a customer who ordinarily deposits small amounts suddenly starts making frequent cash deposits just under the reportable threshold, which would be flagged for review. In commercial banking, TMPs might flag a small business account that suddenly channels large international transfers unrelated to its normal business (possible money laundering via shell companies). Banks also monitor cross-border payments for compliance with sanctions and look for known fraud patterns (like an email compromise scam where a business suddenly sends funds to a new overseas beneficiary on fake instructions). Correspondent banking, where one bank processes transactions for another, is a high-risk area; banks use TMPs to monitor flows for nested accounts or unusual volume that could indicate downstream client risk.
• Payments and Fintech Companies: Payment processors, money remitters, and fintech platforms (including mobile payment apps and neobanks) handle fast, high-volume transactions, often with a tech-savvy client base. They use TMPs to conduct real-time monitoring of transactions on their platforms. For example, an international remittance service will automatically screen transfers against sanction lists and flag any that exceed certain corridors or amounts. Fintech lenders might monitor loan disbursements and repayments for signs of illicit use of funds or fraud. Many fintech firms lean heavily on machine learning-driven monitoring to reduce false positives and cope with real-time decisioning. As a result, they often tout lower fraud rates due to instant flagging of suspicious activities. According to industry analysis, fintechs employ transaction monitoring not just for AML compliance but for fraud prevention, risk management, and customer protection, thereby safeguarding both the company and its users from losses.
• Cryptocurrency Exchanges and Virtual Asset Providers: Crypto exchanges must monitor both on-chain crypto transactions and traditional fiat money flows. Use cases here include flagging when a customer sends cryptocurrency to a wallet address associated with known illicit activity (using blockchain analytics feeds), or when a large amount of crypto is rapidly converted to cash and withdrawn (potentially indicative of a laundering technique). Exchanges also monitor for structuring – e.g. multiple small crypto deposits that are then aggregated and sent to an external wallet could suggest layering. They are increasingly required to report suspicious crypto transactions to authorities just like banks do. A notable example occurred in 2023 when U.S. authorities fined a crypto exchange for AML failures, illustrating that crypto businesses are held to similar TMP expectations as banks. The crypto sector’s adoption of TMPs is expected to harden as global standards (like FATF’s guidance) get implemented, making robust monitoring a baseline for operating legally.
• Securities and Investments: Brokerage firms and investment banks also use transaction monitoring, albeit focused on different transaction types (security trades, wire movements in brokerage accounts). They watch for things like suspicious penny stock liquidations (which might be part of a pump-and-dump and money-out scheme) or atypical wire transfers from brokerage accounts unrelated to investment activity. The sector-specific risk here is that securities transactions can be used to launder money (through mechanisms like market manipulation or risky trades that deliberately lose money to obscure gains elsewhere). Thus, brokers implement surveillance to spot unusual trading patterns or account flows, and they integrate their monitoring with broader AML systems to report any suspicious fund movements.
• Insurance and Other Non-Bank Sectors: Life insurance companies monitor policy payments, loans, and surrenders for red flags – for instance, a customer rapidly overfunding an insurance policy and then cancelling it to receive a large refund (possible laundering). Casinos and gaming firms track buy-ins, bets, and cash-outs of chips, looking for patrons who attempt to “clean” money by gambling it briefly and cashing out. Real estate agencies and luxury goods dealers (jewelers, art dealers) are increasingly brought under AML regimes too; while they may not have automated monitoring like banks, they must still watch transaction patterns (like rapid flipping of properties or high-value sales paid in cash) and report suspicions. Notably, the regulatory net is widening – the EU’s latest AML laws even bring certain high-value sectors like sports clubs into scope. For example, by 2029 top-tier football clubs in Europe will be required to perform KYC on major transactions (such as player transfers and sponsorship deals), monitor those flows, and report suspicious activity to authorities. This reflects a broader recognition that money laundering can occur outside conventional banking, and thus sector-specific controls are needed. In summary, while the contexts differ – from bank accounts to bitcoin to baccarat – the fundamental use case of a TMP remains: identify and stop the misuse of the financial system, wherever it may occur.

In the United States, regulators have sharpened their focus on transaction monitoring as a cornerstone of AML compliance. FinCEN, the U.S. Treasury’s Financial Crimes Enforcement Network, administers the Bank Secrecy Act (BSA) and sets nationwide AML/CFT standards. A significant recent development is the implementation of the 2020 Anti-Money Laundering Act (AMLA), which is modernizing how AML programs (including TMPs) are regulated. FinCEN issued the first-ever national AML/CFT Priorities in 2021, highlighting key threats (such as corruption, cybercrime, and terrorist financing), and it is now updating its rules to require that financial institutions incorporate these priorities into their AML programs. In mid-2024, FinCEN proposed new rules that explicitly mandate AML programs to be effective, risk-based, and reasonably designed – language that reinforces the expectation that banks tailor their monitoring to their risk profile and actively mitigate illicit finance risks. Crucially, the proposal also requires institutions to conduct formal risk assessments to inform their transaction monitoring controls, ensuring that higher-risk products, customers, and geographies receive enhanced scrutiny.

U.S. regulators are also encouraging innovation in TMPs. In fact, the AMLA directs FinCEN to foster the use of new technology to fight money laundering, recognizing that static, checkbox approaches won’t suffice. The U.S. Treasury’s 2022 Illicit Finance Strategy and prior interagency statements have encouraged banks to test AI, machine learning, and other innovative techniques in their AML programs. This support reflects a shift in regulatory tone – agencies acknowledge that effective monitoring may require non-traditional tools, and they have promised to be flexible in examining AI-driven systems as long as banks can explain and validate them. Banks are responding: a recent industry survey found over 80% of major North American banks had begun adopting machine learning in their transaction monitoring, in part due to this regulatory encouragement.

Another notable U.S. development is the emphasis on governance and accountability for TMPs. Regulators have made clear that bank leadership must ensure these systems are up to par. New York’s Department of Financial Services (NYDFS) took a pioneering step with its Part 504 Rule (effective 2017), which requires banks under NY jurisdiction to maintain certified transaction monitoring and filtering programs. Under this rule, a bank’s chief compliance officer must personally attest each year that their TMP is in compliance – a requirement that has put pressure on boards and executives to pay close attention. The NYDFS created Part 504 after finding “systemic inadequacies” in banks’ BSA/AML and OFAC compliance, often due to weak senior management oversight. The rule spells out minimum standards (such as comprehensive risk-based monitoring scenarios, watchlist filtering, regular tuning, and end-to-end testing of systems) and holds institutions accountable through potential criminal penalties for false compliance certifications. This New York approach has influenced expectations elsewhere in the country, even for banks not directly under NYDFS.

Enforcement actions in the U.S. further illuminate regulatory expectations for TMPs. In late 2023, FinCEN penalized Shinhan Bank America $15 million for willful AML violations, citing an array of deficiencies in its monitoring program. The bank had used an overly rigid customer risk scoring method that didn’t dynamically adjust with customer behavior, and it failed to aggregate related accounts or transaction types in its surveillance, meaning it missed big-picture patterns. FinCEN’s order even listed examples of scenarios that banks should ensure their systems cover – such as the multi-beneficiary wire transfers and multi-jurisdiction flows mentioned earlier. Moreover, U.S. regulators expect regular model validation and tuning: in Shinhan’s case, the bank had not adequately tested its monitoring system updates, contributing to gaps. Another FinCEN action in 2023 against a trust company (Kingdom Trust) underscored that even newer or non-bank entities must have solid monitoring – Kingdom Trust processed $4 billion in suspicious transactions with virtually no automated system and only a single person manually reviewing daily transactions. These enforcement trends signal that U.S. authorities will not hesitate to penalize institutions – bank or non-bank – that fail to implement robust TMPs commensurate with their risk. On a positive note, the government is also pursuing public-private partnerships (like information-sharing initiatives) to enhance banks’ ability to detect threats. All told, the U.S. regulatory landscape is moving toward stricter, yet more technologically open, oversight of transaction monitoring, with FinCEN and other regulators pushing for smarter systems that produce meaningful intelligence (valuable SARs) for law enforcement.

Europe has likewise been overhauling its AML regulatory framework, with significant implications for transaction monitoring across EU member states. The Sixth Anti-Money Laundering Directive (6AMLD) came into force in December 2020 and was fully implemented by 2021, marking a major update to the EU’s AML laws. 6AMLD expanded the scope of offenses – adding cybercrime, environmental crime, tax evasion and more to the list of 22 predicate offenses for money laundering – and crucially, it closed loopholes by explicitly criminalizing aiding and abetting money laundering. This means individuals (and companies) that facilitate laundering, even if they didn’t launder funds themselves, can be prosecuted. The directive also toughened penalties: EU countries must now provide for at least four-year prison terms for serious money laundering convictions, along with fines up to €5 million (for individuals) or greater amounts and asset confiscations to strip illicit profits. Further, 6AMLD introduced corporate liability, so legal entities can be held accountable for AML failures – companies could face heavy fines or even closure, and executives can be held personally liable if their organization commits AML offenses. This escalation of punishments in turn pressures financial institutions to strengthen their AML controls (including monitoring) to avoid being complicit in criminal activity.

From a systemic perspective, 6AMLD and associated EU measures put a spotlight on improving information-sharing and oversight. The directive calls for rapid and effective exchange of information on suspicious transactions among EU member states’ Financial Intelligence Units (FIUs), and greater cross-border cooperation in investigations. Practically, this encourages the development of platforms and protocols for sharing SARs and risk data across borders, so that a scheme detected in one country can quickly be flagged in another. It also underscores the need for more consistency in how banks monitor and report suspicious activity EU-wide. To that end, the EU is moving toward a single rulebook for AML. In 2024, the EU adopted a new AML/CFT Regulation (AMLR) that will replace some directives with directly applicable rules – by 2027, many AML requirements (such as customer due diligence and transaction monitoring standards) will be uniform across the Union. Accompanying this, a brand new supranational regulator, the European Anti-Money Laundering Authority (AMLA), has been established as of mid-2024. AMLA will be headquartered in Frankfurt and is slated to become operational in 2025, growing to about 400 staff. Its mandate is to supervise the riskiest financial institutions (like some cross-border banks and crypto firms) directly, harmonize AML supervision across EU countries, and coordinate joint investigations. The creation of AMLA was a direct response to major failures like the Danske Bank scandal, which exposed the lack of a unified EU oversight for AML. Indeed, after Danske Bank’s Estonian branch was found to have laundered an estimated €200 billion in suspicious funds, EU authorities realized that inconsistent national enforcement had left gaps. AMLA will aim to “centralize” AML efforts and ensure high standards for TMPs and other controls are consistently applied, no matter where a firm operates.

Meanwhile, the European Supervisory Authorities (ESAs), such as the European Banking Authority (EBA), have been active in issuing guidance to strengthen AML controls. The EBA has updated its guidance on money laundering risk factors and internal controls, which includes expectations for transaction monitoring systems. For example, in 2023 the EBA published guidelines on policies and controls for effective ML/TF risk management, emphasizing that institutions must have monitoring triggers tailored to their risk assessment and should leverage modern analytics to detect complex criminal schemes. The ESAs also push for greater use of technology; they acknowledge that legacy manual monitoring is insufficient for today’s large transaction volumes. In fact, the 6AMLD’s implementation guidance explicitly notes that companies should invest in advanced transaction monitoring and analysis technologies to proactively detect suspicious activity. This dovetails with an increasing focus on RegTech in Europe: national regulators are encouraging banks to adopt AI and data-driven tools to improve detection and reduce false positives, much as in the U.S. For instance, the HKMA in Hong Kong (while not in the EU, an example of regulator guidance) has shared case studies on successful AML analytics – and European regulators exchange these best practices through forums and the FATF.

Another area of regulatory development is a broadened scope of AML laws to new sectors and emerging risks, which we touched on earlier. The EU’s AML package extends certain requirements to crypto-asset service providers and even beyond finance (like the mention of sports clubs needing KYC by 2029). All these changes mean that any entity covered by EU AML laws will need a capable TMP. Failure to monitor transactions and report suspicions is explicitly identified as a breach: EU regulators have levied fines against banks and payments firms for failing to implement proper monitoring, which allowed illicit transactions to flow unchecked. High-profile enforcement in Europe – such as the €775 million fine against ING Bank in the Netherlands in 2018 for AML lapses, or the recent $2 billion in fines against Danske Bank by US and Danish authorities in 2022 – have reinforced that inadequate transaction monitoring has serious consequences. The trend in the EU is clear: harmonize and tighten AML rules, empower a central authority (AMLA) to ensure compliance, and mandate that institutions use state-of-the-art systems to detect and prevent money laundering across the single market.

Across Asia, financial hubs like Singapore and Hong Kong have been proactive in updating regulations and guidance to bolster transaction monitoring and filtering programs. In Singapore, the Monetary Authority of Singapore (MAS) has taken a forward-looking approach that combines strict enforcement with innovation. Singapore’s regulations (e.g. MAS Notices 626 for banks and similar notices for other sectors) require a comprehensive AML/CFT program, including ongoing monitoring of customer transactions and prompt STR filing for any suspicious activity. A recent and groundbreaking initiative by MAS is the launch of the COSMIC platform in 2024 – a centralized digital system for collaborative information sharing among banks. COSMIC (which stands for Collaborative Sharing of Money Laundering/Terrorism Financing Information & Cases) allows participating banks to securely share data on customers who exhibit multiple red flag indicators of potential financial crime. Six major banks co-developed COSMIC with MAS, focusing initially on risks like abuse of shell companies and trade-based money laundering in commercial banking. Under a new law (the Financial Services and Markets Act amendment effective April 2024), banks are permitted and in some cases expected to exchange information on suspicious accounts via COSMIC, with robust safeguards in place. This regulatory move is significant – it recognizes that no single bank may see the full picture of a complex laundering scheme, so regulators are enabling a controlled platform where institutions can “pool” their red flags and detect criminal networks that span multiple banks. Over the next few years, MAS plans to expand COSMIC’s reach and potentially mandate certain information sharing once the framework matures. For transaction monitoring, COSMIC effectively adds a new dimension: inter-bank monitoring of risk, supplementing each bank’s internal TMP. It exemplifies how Singapore is marrying regulation with technology to get ahead of sophisticated financial crime.

MAS has also been aggressive in enforcing AML requirements. In recent years, Singapore investigated numerous banks in connection with the 1MDB money laundering scandal, levying penalties and even shutting down branches of banks that egregiously failed to detect illicit flows. More recently, in 2020–2021, MAS has fined local and international banks for failures in their monitoring and controls, demonstrating that even a strong regulatory framework requires consistent supervision. Additionally, MAS frequently issues guidance and circulars to industry about improving transaction surveillance, sometimes in partnership with industry committees. One area MAS emphasizes is the use of data analytics and machine learning to better pinpoint suspicious behavior – Singapore has hosted an AML tech innovation lab and encourages financial institutions to incorporate RegTech solutions for real-time monitoring and risk scoring. By balancing strict penalties for failures with support for adopting new tech, Singapore aims to maintain a reputation as both a clean and innovative financial center.

In Hong Kong, the Hong Kong Monetary Authority has similarly tightened expectations for banks’ TMPs, especially in response to some high-profile cases of AML failings in the region. The HKMA’s Anti-Money Laundering and Counter-Financing of Terrorism guidelines require banks (Authorized Institutions) to continuously monitor customer transactions and report anything suspicious, very much in line with global standards. What stands out is HKMA’s strong push for AML Regtech adoption. In February 2023, HKMA published a detailed guidance paper on Transaction Monitoring, Screening, and Suspicious Transaction Reporting (an update to prior 2018 guidance) to share best practices and common gaps observed in banks’ systems. The guidance urges banks to implement risk-based, technology-enabled monitoring – including suggestions like using automation to link customer profiles with transaction monitoring, integrating external datasets (for example, trade data or digital footprint information) to enrich alerts, and employing machine learning to complement rule-based detection. HKMA highlighted five core design elements for effective transaction monitoring systems, touching on governance, data quality, scenario development, ongoing tuning, and review processes. Banks are expected to generate not only alerts but also useful management information (MI) reports from their systems, to help oversee the health of the monitoring program (e.g. trend metrics on alert volumes, scenario effectiveness, etc.).

On the enforcement front, HKMA has increased penalties for AML control failures. A notable case occurred in 2024, when HKMA fined Fubon Bank (Hong Kong) HK$4 million after the bank self-reported issues in its transaction monitoring system. The investigation found that over a three-year period, Fubon had not maintained effective ongoing monitoring: there were inadequate procedures for managing changes to the monitoring system, a failure to address a sudden significant drop in generated alerts, and a lack of regular review of what the system’s coverage was. In other words, when the bank tweaked its system and alerts plunged, they didn’t promptly investigate whether the tuning went wrong – a lapse that could mean transactions were slipping by unmonitored. HKMA also found the bank wasn’t scrutinizing certain customers’ transactions sufficiently and missed updating customer due diligence information when “trigger events” occurred. The Fubon case is instructive: it shows regulators expect banks not only to install monitoring software, but to actively govern and maintain it – watching for anomalies in alert output and ensuring scenarios stay current with the risk profile. Hong Kong has also taken action against banks for failures in screening (the “filtering” aspect), such as cases where sanctioned or politically exposed persons were not detected due to list updating failures or name-match errors.

Beyond Singapore and Hong Kong, many other Asian jurisdictions are raising standards. In Australia, AUSTRAC has levied record fines (e.g. Westpac’s A$1.3 billion penalty in 2020) for AML breaches, including failure to detect and report millions of cross-border transactions related to possible child exploitation. China has been bolstering its AML laws and conducting on-site inspections of banks’ transaction monitoring capabilities, driven by FATF recommendations. Japan and South Korea have updated their AML regulations in the past couple of years to cover cryptocurrency exchanges and tighten KYC/monitoring for banks. And jurisdictions like the UAE and India have also introduced stricter requirements for real-time monitoring in remittance and exchange houses after being urged to address deficiencies. A common thread in Asia’s regulatory developments is aligning with global AML norms (many of which are set by FATF) while dealing with region-specific risks (such as trade-based money laundering through Hong Kong’s trading hub, or the use of offshore companies in Singapore’s private banking scene). Regulators are increasingly explicit about expecting effective TMPs – meaning systems that actually catch illicit activity. They are backing this up by sharing typologies, promoting regtech solutions, and taking enforcement action when institutions fall short of the mark. The trajectory is toward more integrated AML controls in Asia’s financial sector, where transaction monitoring, sanctions filtering, and even cybersecurity intel are combined to guard against a wide array of financial crimes.

Real-world cases offer sober lessons on what can go wrong when transaction monitoring and filtering programs are inadequate. Over the past decade, banks and financial institutions globally have faced billions in fines and severe reputational damage due to AML compliance failures – often tracing back to weaknesses in their TMPs. Here we examine a few emblematic examples:

• Danske Bank (Estonia) Scandal: Perhaps the most notorious AML failure in modern times, Danske Bank’s Estonian branch was revealed in 2018 to have facilitated a staggering amount of illicit flows – about €200 billion in suspicious non-resident transactions from Russia, ex-Soviet states and elsewhere, over 2007–2015. The branch’s transaction monitoring was virtually non-existent for high-risk foreign clients; shell companies with hidden owners moved huge sums through with little scrutiny. Warning signs (such as an inordinately high volume of cross-border payments for a small Baltic branch) were ignored or missed. This failure led to Danske Bank being fined $2 billion by U.S. and Danish authorities in 2022, the CEO’s resignation, and a collapse of trust in the bank. It also spurred regulatory reforms across Europe (including the creation of the EU’s AML Authority). The Danske case illustrated how a bank’s lack of robust monitoring – and poor group oversight – enabled money laundering on a vast scale, and it underscored the importance of centralized controls and information-sharing to prevent “orphan” branches from becoming laundromats.
• Shinhan Bank America (USA) Fine: In 2023, FinCEN’s action against the U.S. unit of Shinhan Bank highlighted that even smaller institutions must implement sophisticated TMPs. Shinhan’s U.S. operations were fined $15 million after regulators found it used primitive risk scoring (categorizing customers only by broad type rather than individual risk factors) and failed to link related accounts in monitoring. For example, if the same customer had multiple accounts or used different product types, the system didn’t aggregate their activity – a major blind spot, since many laundering schemes involve moving funds between accounts under common control. Moreover, Shinhan did not include certain known risky scenarios in its rules, and it lacked formal procedures to adjust customer risk ratings over time. As a result, obvious red flags like large round-dollar transfers through many intermediaries went undetected. FinCEN’s consent order not only penalized the bank but also served as guidance to the industry, enumerating scenarios every bank should cover and stressing the need for periodic testing of systems to ensure data feeds and detection logic are working as intended.
• Kingdom Trust (USA) Case: Also in 2023, FinCEN took its first enforcement action against a trust company – Kingdom Trust, a non-bank that offered crypto custodial services among other things. The firm had a negligible AML program: one person was manually glancing over transactions, which proved grossly insufficient as roughly $4 billion in suspect transactions flowed through without a single SAR filed. Some of these transactions involved customers who were later revealed to be linked to criminal investigations. The case underscored that automation is essential once volumes reach a certain point; manual monitoring of daily transactions cannot reliably catch sophisticated patterns or large-scale activity. FinCEN’s $1.5 million penalty (while small relative to bank fines) sent a message that even fintechs, trust companies, and others on the periphery of traditional banking must invest in proper monitoring systems or face enforcement.
• Fubon Bank (Hong Kong) Penalty: In 2024, as mentioned, HKMA fined Fubon Bank HK$4 million for failing to properly maintain its transaction monitoring between 2019 and 2022. The issues read like a checklist of “what not to do” in managing a TMP: the bank didn’t have a good process for handling system changes (suggesting that scenario or software updates were made without adequate validation), they observed a significant drop in alerts at one point but did not treat it as a red flag (implying the system might have been mis-tuned or broken without anyone noticing), and they weren’t regularly reviewing whether their monitoring coverage matched the bank’s current risk profile. Additionally, some higher-risk transactions weren’t being scrutinized properly and customer profiles weren’t updated after big events, meaning the monitoring rules might have been using stale risk assumptions. HKMA’s action here demonstrates the importance of change management and ongoing calibration in TMPs – it’s not “set and forget.” A dramatic fall in alert volume, for instance, could indicate a serious malfunction or an overly aggressive threshold change, and should prompt an immediate investigation by compliance staff. Regulators expect banks to have controls to catch such anomalies (e.g. generating MI reports that show alert trends, as HKMA’s guidance suggests) and to conduct annual validations of their monitoring models.
• Global Bank Sanctions Cases: Transaction filtering failures have also led to some of the largest fines on record, especially when they involve sanctions evasion. A prime example is the $8.9 billion fine in 2014 against BNP Paribas (France’s largest bank) for processing transactions for sanctioned Sudanese, Iranian, and Cuban entities through the U.S. financial system. BNP employees had deliberately stripped or obscured information in payment messages to evade U.S. filters – a willful misconduct. While that was an intentional breach, other banks have been fined for more inadvertent filtering failures, such as not updating sanctions lists in a timely manner or not catching close name matches. These cases highlight that the “filtering” side of TMPs (sanctions screening) is as critical as the “monitoring” side. Banks must ensure their systems are regularly updated with the latest lists and that fuzzy matching logic is tuned to catch slight variations of blacklisted names. The U.S. Office of Foreign Assets Control (OFAC) has published compliance frameworks indicating that a common root cause of sanctions violations is a lack of screening or a defect in how the screening was done. In short, a lapse in filtering controls can open an institution to facilitating sanctioned transactions – an outcome regulators penalize even more heavily than money laundering lapses, due to national security implications.
• Casino and Gaming Sector: Outside banking, casinos have occasionally made headlines for AML failures tied to transaction monitoring. For instance, in 2022 Australian regulators took action against Crown Resorts and Star Entertainment, major casino operators, after investigations found that criminals exploited their VIP gambling programs to launder money. The casinos had weak monitoring of high-roller accounts and overlooked obvious red flags (like large cash buy-ins by intermediaries on behalf of others, and rapid cash-outs with minimal play). Authorities imposed fines totaling hundreds of millions of dollars and mandated these companies to overhaul their AML controls. The lesson from casinos is that any business handling large cash flows or transactions must implement a monitoring program proportionate to its risk – and if they don’t, regulators will intervene with force.

These examples collectively teach that inadequate TMPs carry high costs. Whether it’s a bank, a fintech startup, or a non-bank institution, failing to have robust monitoring and filtering can result in illicit funds coursing through the business for years – until regulators or law enforcement eventually uncover the trail. By then, the institution faces not just fines and legal liability, but reputational ruin and remediation expenses that far exceed the cost of doing compliance right in the first place. On the flip side, many enforcement actions also show what good looks like: regulators often detail how the institution should have acted. Common themes include the need for strong governance (senior management attention and accountability), ongoing risk-based tuning of scenarios, integration of customer risk profiling with monitoring, adequate staffing and training for alert investigations, and leveraging technology to cover complex patterns. As financial crime tactics evolve (from cyber-fraud to crypto abuse), TMPs must also evolve. The failures of the past have driven today’s far more prescriptive regulations and industry standards for transaction monitoring. In an environment of heightened regulatory scrutiny, financial institutions worldwide are left with no choice but to continuously enhance their TMPs – not as a mere formality, but as an essential function to keep illicit money out of the financial system and to protect the institution from being the next cautionary tale.

Transaction monitoring and filtering programs sit at the heart of the financial sector’s defense against money laundering, fraud, and sanctions violations. As this deep dive has shown, TMPs are multifaceted systems – combining data-driven technology, risk management strategy, and regulatory compliance obligations. They must sift through immense volumes of transactional data to pinpoint the few signals of wrongdoing, all without unduly hampering legitimate business. The challenge is formidable: criminals are constantly probing for weaknesses and adapting their methods, whether by exploiting new technologies like cryptocurrency or finding blind spots in a bank’s controls. In response, financial institutions and regulators are stepping up their game. We see a clear trend toward smarter, faster, and more collaborative monitoring. Artificial intelligence and machine learning are reducing false positives and uncovering hidden patterns, real-time capabilities are bringing response times down to seconds, and initiatives like Singapore’s COSMIC show the power of collective intelligence in fighting financial crime.

Equally, the regulatory environment is tightening across all major markets. Authorities in the U.S., EU, and Asia – from FinCEN to the forthcoming AMLA to MAS and HKMA – all expect institutions to have effective and evolving TMPs, tailored to their risk and leveraging the best available tools. They are backing these expectations with detailed guidance and, when needed, tough enforcement actions. The tone of regulators is increasingly outcome-focused: it’s not enough to have a paper program or to tick the box by installing software; the program must demonstrably work in identifying illicit finance. This is why there’s so much emphasis now on risk-based calibration, ongoing model tuning, and audits of monitoring effectiveness. Compliance officers, armed with better technology and clearer regulatory guidance, are in a stronger position than ever to detect suspicious activity early and file quality reports that assist law enforcement.