How to Detect Money Laundering Transactions: A Practical Guide for Compliance Teams

Introduction

Financial crime is evolving at a rapid pace, and money laundering remains one of its most pervasive threats. Every year, an estimated $800 billion to $2 trillion in illicit funds are laundered through the global financial system. For financial institutions, this isn’t just a legal issue—it’s a reputational and operational one.

Whether you're a compliance officer at a traditional bank or a risk lead at a fast-growing fintech, one question remains at the heart of your anti-money laundering (AML) efforts:
How can we detect money laundering transactions—effectively and early?

In this guide, we’ll break down the key techniques, red flags, tools, and best practices for detecting money laundering, ensuring your institution is not only compliant but proactive in preventing financial crime.

What Is Money Laundering?

Before diving into detection, it's important to understand what money laundering entails. Money laundering is the process of disguising the origins of illegally obtained money to make it appear legitimate. It typically occurs in three stages:

1. Placement – Illicit funds are introduced into the financial system (e.g., deposits, cash-based purchases).

2. Layering – Funds are moved through multiple accounts or transactions to obscure the trail.

3. Integration – Laundered money is reintroduced into the economy as seemingly legitimate funds.

Your goal as a compliance team is to intercept activity at any of these stages—ideally, before the money is fully laundered.

Why Detecting Suspicious Transactions Is Critical

Failure to detect money laundering can lead to:

• Regulatory penalties and sanctions

• Loss of banking license or operating rights

• Reputational damage and customer churn

• Unwittingly facilitating organised crime, terrorism financing, or corruption

Detection isn’t just about fulfilling a regulatory checkbox—it’s about safeguarding the financial ecosystem and ensuring long-term institutional integrity.

Key Red Flags in Money Laundering Transactions

Money launderers use clever techniques to avoid detection, but there are common patterns and behaviors that serve as red flags:

🔹 Transaction-Level Red Flags

• Unusually large or frequent cash deposits

• Transactions just below reporting thresholds (structuring)

• Sudden movement of funds to high-risk jurisdictions

• Rapid in-and-out transfers across multiple accounts

• Use of third parties to conduct transactions

🔹 Customer Behaviour Red Flags

• Reluctance to provide full identification or documentation

• Vague or inconsistent responses regarding the source of funds

• Involvement in businesses known for high AML risk (e.g., casinos, crypto, shell companies)

• Politically exposed persons (PEPs) without clear reason for account activity

🔹 Account Usage Red Flags

• Account behavior inconsistent with customer profile

• Multiple accounts under the same name or address

• Shared IP addresses or devices across unrelated accounts

Training your staff to recognise these red flags—and equipping your system to act on them—is essential for detection.

How to Detect Money Laundering Transactions Effectively

✅ 1. Implement Transaction Monitoring Systems (TMS)

A robust transaction monitoring system is the first line of defence. It allows institutions to automatically scan transactions against predefined rules or risk scenarios.

Look for solutions that support:

• Real-time and batch monitoring

• Custom scenario creation (e.g., structuring, pass-through accounts)

• Dynamic thresholds based on risk profiles

• Integration with external watchlists and adverse media databases

✅ 2. Use Risk-Based Customer Profiling

Customer risk scoring enables smarter alert prioritisation. Key risk factors include:

• Customer type (retail, business, NGO)

• Jurisdiction and residence

• Source of wealth and income

• Transaction patterns

Risk-based profiling ensures that high-risk customers receive more scrutiny while reducing false positives from low-risk individuals.

✅ 3. Leverage AI and Machine Learning

Traditional rule-based systems often generate high volumes of irrelevant alerts. AI-driven platforms can:

• Learn from past investigations

• Identify unknown patterns and anomalies

• Reduce false positives by over 70%

• Predict potential suspicious behaviour before it escalates

Use machine learning models to refine thresholds, group related alerts, and uncover complex money movement patterns (e.g., layering via multiple small transfers).

✅ 4. Monitor Across Channels and Products

Money laundering doesn’t happen in silos. To catch suspicious activity, you must monitor customer activity across all touchpoints, including:

• Bank accounts

• Credit/debit cards

• Mobile wallets

• Cross-border remittances

• Cryptocurrency platforms (where applicable)

A centralised compliance platform helps consolidate alerts and customer data into a single view for better decision-making.

✅ 5. Conduct Periodic Lookbacks and Pattern Analysis

Sometimes money laundering schemes unfold over weeks or months. Periodic lookbacks can help uncover:

• Recurring beneficiaries

• Multi-layered fund movement

• Dormant accounts suddenly becoming active

Integrate lookback reviews into your internal audit and quality assurance workflows.

Tools That Help in Detecting Suspicious Transactions

To effectively detect money laundering transactions, financial institutions need a strong AML tech stack made up of purpose-built tools that work seamlessly together.

Name screening tools form the first line of defence, checking customer names against global watchlists, including sanctions, politically exposed persons (PEPs), and other high-risk individuals or entities. This helps identify potentially risky customers right from the onboarding stage.

Transaction monitoring systems flag unusual or suspicious transaction patterns by comparing real-time activity against expected customer behaviour. These systems detect anomalies such as structuring, round-tripping, or rapid fund movement across accounts.

Customer risk scoring modules continuously evaluate the risk profile of each customer based on their behaviour, transaction history, geography, and other risk indicators. This allows institutions to dynamically prioritise monitoring efforts based on risk exposure.

To handle high volumes of alerts, smart alert management systems help prioritise alerts based on severity, auto-group related activities, and even generate investigative narratives to reduce analyst effort and speed up decision-making.

Finally, a robust case management system is essential for end-to-end investigations. It enables compliance teams to consolidate alerts, track case progress, document findings, and file suspicious transaction reports (STRs) efficiently and in a regulator-ready format.

Together, these tools form a unified ecosystem that enhances visibility, speeds up investigations, and improves detection accuracy.

How Tookitaki Helps

Modern compliance teams are increasingly turning to AI-native platforms like Tookitaki to power their AML efforts. Tookitaki’s FinCense platform offers an integrated suite of tools—from name screening and transaction monitoring to smart alert management and case workflows. What sets it apart is its ability to combine scenario-based detection, federated intelligence, and explainable AI—enabling teams to reduce false positives, accelerate investigations, and stay ahead of evolving threats.

Best Practices for Compliance Teams

1. Train and empower your team – Ensure investigators understand red flags and investigative protocols.

2. Automate wherever possible – Use technology to reduce manual overhead and human error.

3. Validate your models regularly – Confirm that your detection logic is accurate and up to date.

4. Collaborate with industry peers – Join AML communities or ecosystems that share typologies and trends.

5. Report Suspicious Transactions Promptly – File SARs/STRs as required by your local FIU or regulator.

Case Example: Detecting Layered Transactions

A mid-sized digital bank noticed an account receiving ₱95,000 every few days—always from different senders, just below the ₱100,000 reporting threshold. The funds were then transferred to an offshore account within minutes.

Using AI-based monitoring, the system flagged the pattern of frequent near-threshold inbound payments followed by rapid outflows. This triggered an investigation, revealing the account was part of a money mule network distributing illicit funds.

Thanks to early detection, the account was frozen, the pattern was shared with authorities, and losses were minimised.

Regulatory Expectations Around Detection

Financial regulators globally—including FATF, FinCEN, FCA, MAS, and AMLC (Philippines)—expect institutions to:

• Maintain effective monitoring systems

• Demonstrate governance over detection models

• File reports in a timely and structured manner

• Show evidence of tuning, validation, and internal controls

Regular audits, walkthroughs, and system reviews are essential to stay compliant.

Conclusion

Money laundering detection is both a science and an art. While the stakes are high, modern tools—especially those leveraging AI and community-driven intelligence—offer compliance teams a powerful advantage.

By understanding transaction patterns, leveraging risk scores, and investing in smart monitoring systems, your institution can detect and disrupt suspicious transactions before they pose a regulatory or reputational threat.