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Understanding the 3 Stages of Money Laundering

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Tookitaki
20 May 2020
5 min
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Money laundering is a complex financial crime that affects economies worldwide. It's a process that criminals use to make illicit funds appear legitimate.

Understanding the stages of money laundering is crucial. It helps in detecting and preventing this crime.

This article will delve into the three stages of money laundering. These are placement, layering, and integration.

We'll provide examples for each stage. This will give you a clearer picture of how money laundering works in practice.

We'll also discuss the role of financial institutions in combating this crime.

By the end of this article, you'll have a comprehensive understanding of money laundering stages. This knowledge is vital for finance professionals, legal experts, and anyone interested in financial crimes.

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What is Money Laundering?

Money laundering is a financial crime. It involves making illegally-gained proceeds appear legal. The United Nations Office on Drugs and Crime (UNDOC) defines money laundering as “the method by which criminals disguise the illegal origins of their wealth and protect their asset bases, so as to avoid the suspicion of law enforcement agencies and prevent leaving a trail of incriminating evidence.”

Criminals use a series of complex transactions to hide the origin of these funds. The goal is to make the funds seem as if they came from legitimate sources.

Money laundering is not just a single act. It's a process that involves three stages: placement, layering, and integration. Each stage serves a specific purpose in the overall scheme.

Understanding these stages is crucial. It helps in detecting and preventing money laundering. It also aids in the prosecution of those involved in this crime.

Why Money Laundering Matters

Money laundering is a serious global issue. It's not just a problem for the financial sector, but for society as a whole.

This crime fuels other illegal activities, such as drug trafficking and terrorism. It also undermines the integrity of financial institutions and systems.

Moreover, money laundering can distort economies. It can lead to loss of tax revenue and create market instability. Understanding its stages is a step towards combating this crime.

The 3 Stages of Money Laundering Overview

Money laundering is a complex process. It involves three main stages: placement, layering, and integration.

Each stage serves a specific purpose in the laundering process. They are:

  1. Placement: Introducing illicit money into the financial system.
  2. Layering: Concealing the origins of the money through complex transactions.
  3. Integration: Merging the laundered money back into the legitimate economy.

The stages of money laundering are represented in the image below.

stages of money laundering-1

Please note that some of these steps may not be there in some money laundering cases. For example, non-cash proceeds that are already in the financial system need not be placed.

Stage 1: Placement

The placement stage is the initial step in money laundering. It involves the introduction of illicit funds into the financial system.

This stage is risky for criminals. It's where they are most likely to be detected. Large cash deposits or frequent transactions can raise suspicion.

To avoid detection, criminals use various methods. They might break down large amounts of cash into smaller deposits. This is known as smurfing.

Another method is currency exchanges. Criminals might exchange one currency for another to confuse the trail of money.

Placement Examples

Let's consider a drug dealer with a large amount of cash. He might deposit small amounts into different bank accounts. This is an example of smurfing.

Or consider a criminal who buys expensive items with illicit cash. He then sells these items, depositing the proceeds into a bank account. This is another example of placement.

Stage 2: Layering

The second stage of money laundering is layering. This stage involves creating complex layers of financial transactions. The aim is to obscure the origin of the funds.

Criminals use various techniques in this stage. They might make multiple transfers between different accounts. They might also purchase and sell assets to create a confusing paper trail.

Shell companies are often used in this stage. These are companies that exist only on paper. They have no real business operations or assets.

Layering Techniques

Consider a criminal who transfers money between different bank accounts. He might use accounts in different countries. This makes it harder for authorities to trace the money.

Or consider a criminal who uses a shell company. He transfers illicit funds to the company's account. The company then makes 'legitimate' transactions with the money. This is another example of layering.

Stage 3: Integration

The final stage of money laundering is integration. This is where the laundered money is returned to the criminal. It now appears to come from a legitimate source.

Integration often involves investment in legal businesses. The laundered money can be used to purchase assets. These assets can then be sold, with the proceeds appearing legitimate.

Another method is loans. A criminal might arrange for a loan to be given to him from his own laundered money.

Integration Examples

Consider a criminal who invests laundered money in a legitimate business. He might buy property or shares in a company. The profits from these investments appear to be legitimate earnings.

Or consider a criminal who arranges a loan from his own laundered money. The loan repayments are made with illicit funds. But the repayments appear to be legitimate income.

The Role of Financial Institutions in AML

Financial institutions play a crucial role in anti-money laundering (AML) efforts. They are often the first line of defense against money laundering.

Banks and other financial institutions have systems in place to detect suspicious activities. These include large cash deposits, frequent transactions, and complex transfer patterns. When such activities are detected, they are reported to the authorities. This helps in the identification and prosecution of money laundering activities.

Consequences of Money Laundering

Money laundering has far-reaching consequences. It distorts economic data, fuels corruption, and undermines the integrity of financial institutions.

Moreover, it facilitates other criminal activities by providing a safe haven for illicit funds. This can lead to social instability and economic inequality. The fight against money laundering is therefore crucial for maintaining economic stability and social justice.

Case Studies Highlighting Money Laundering Stages

To better understand the 3 stages of money laundering, let's look at some real-world examples. These case studies illustrate how criminals exploit the financial system to launder illicit funds.

One notable case involved a major global bank that was fined for failing to prevent money laundering. Criminals deposited large amounts of cash in multiple small transactions, a classic example of the placement stage. They then moved the money around through complex transactions across different accounts and countries, demonstrating the layering stage. Finally, the laundered money was used to purchase legitimate assets, completing the integration stage.

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Another case involved a high-profile political figure who used shell companies to hide illicit funds. The money was first placed into the financial system through the purchase of luxury goods. It was then layered through a series of transactions involving offshore companies. Finally, the laundered money was integrated back into the economy through investments in legitimate businesses.

These cases underscore the importance of vigilance and robust anti-money laundering measures in financial institutions. They also highlight the need for ongoing education and training to detect and prevent such activities.

Conclusion: The Importance of AML Efforts

Understanding the 3 stages of money laundering is crucial in the fight against financial crime. It equips us with the knowledge to detect suspicious activities and take appropriate action.

The role of anti-money laundering (AML) efforts cannot be overstated. From financial institutions to individuals, we all have a part to play in ensuring the integrity of our financial system. By staying vigilant and adhering to AML regulations, we can help deter criminals and protect our economies from the damaging effects of money laundering.

As money laundering becomes increasingly sophisticated, financial institutions must employ advanced technologies to prevent, detect and manage financial crime effectively and efficiently. Tookitaki is an award-winning provider of AML compliance solutions.

If your financial institution is looking to optimise its AML compliance operations with the help of leading-edge solutions, contact us today and book a demo. 

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Blogs
14 Aug 2025
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Smarter Investigations: The Rise of AML Investigation Tools in Australia

In the battle against financial crime, the right AML investigation tools turn data overload into actionable intelligence.

Australian compliance teams face a constant challenge — growing transaction volumes, increasingly sophisticated money laundering techniques, and tighter AUSTRAC scrutiny. In this environment, AML investigation tools aren’t just nice-to-have — they’re essential for turning endless alerts into fast, confident decisions.

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Why AML Investigations Are Getting Harder in Australia

1. Explosion of Transaction Data

With the New Payments Platform (NPP) and cross-border corridors, institutions must monitor millions of transactions daily.

2. More Complex Typologies

From mule networks to shell companies, layering techniques are harder to detect with static rules alone.

3. Regulatory Expectations

AUSTRAC demands timely and accurate Suspicious Matter Reports (SMRs). Delays or incomplete investigations can lead to penalties and reputational damage.

4. Resource Constraints

Skilled AML investigators are in short supply. Teams must do more with fewer people — making efficiency critical.

What Are AML Investigation Tools?

AML investigation tools are specialised software platforms that help compliance teams analyse suspicious activity, prioritise cases, and document findings for regulators.

They typically include features such as:

  • Alert triage and prioritisation
  • Transaction visualisation
  • Entity and relationship mapping
  • Case management workflows
  • Automated reporting capabilities

Key Features of Effective AML Investigation Tools

1. Integrated Case Management

Centralise all alerts, documents, and investigator notes in one platform.

2. Entity Resolution & Network Analysis

Link accounts, devices, and counterparties to uncover hidden connections in laundering networks.

3. Transaction Visualisation

Graph-based displays make it easier to trace fund flows and identify suspicious patterns.

4. AI-Powered Insights

Machine learning models suggest likely outcomes, surface overlooked anomalies, and flag high-risk entities faster.

5. Workflow Automation

Automate repetitive steps like KYC refresh requests, sanctions re-checks, and document retrieval.

6. Regulator-Ready Reporting

Generate Suspicious Matter Reports (SMRs) and audit logs that meet AUSTRAC’s requirements.

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Why These Tools Matter in Australia’s Compliance Landscape

  • Speed: Fraud and laundering through NPP happen in seconds — investigations need to move just as fast.
  • Accuracy: AI-driven tools reduce false positives, ensuring analysts focus on real threats.
  • Compliance Assurance: Detailed audit trails prove that due diligence was carried out thoroughly.

Use Cases in Australia

Case 1: Cross-Border Layering Detection

An Australian bank flagged multiple small transfers to different ASEAN countries. The AML investigation tool mapped the network, revealing links to a known mule syndicate.

Case 2: Crypto Exchange Investigations

AML tools traced a high-value Bitcoin-to-fiat conversion back to an account flagged in a sanctions database, enabling rapid SMR submission.

Advanced Capabilities to Look For

Federated Intelligence

Access anonymised typologies and red flags from a network of institutions to spot emerging threats faster.

Embedded AI Copilot

Assist investigators in summarising cases, recommending next steps, and even drafting SMRs.

Scenario Simulation

Test detection scenarios against historical data before deploying them live.

Spotlight: Tookitaki’s FinCense and FinMate

FinCense integrates investigation workflows directly into its AML platform, while FinMate, Tookitaki’s AI investigation copilot, supercharges analyst productivity.

  • Automated Summaries: Generates natural language case narratives for internal and regulatory reporting.
  • Risk Prioritisation: Highlights the highest-risk cases first.
  • Real-Time Intelligence: Pulls in global typology updates from the AFC Ecosystem.
  • Full Transparency: Glass-box AI explains every decision, satisfying AUSTRAC’s audit requirements.

With FinCense and FinMate, Australian institutions can cut investigation times by up to 50% — without compromising quality.

Conclusion: From Data to Decisions — Faster

The volume and complexity of alerts in modern AML programmes make manual investigation unsustainable. The right AML investigation tools transform scattered data into actionable insights, helping compliance teams stay ahead of both criminals and regulators.

Pro tip: Choose tools that not only investigate faster, but also learn from every case — making your compliance programme smarter over time.

Smarter Investigations: The Rise of AML Investigation Tools in Australia
Blogs
13 Aug 2025
5 min
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Smarter Defences: How Machine Learning is Transforming Fraud Detection in Philippine Banking

Fraud in banking has never been faster, smarter, or more relentless — and neither have the defences.

In the Philippines, the rapid rise of digital banking, mobile wallets, and instant payments has created unprecedented opportunities for growth — and for fraudsters. From account takeovers to synthetic identity scams, financial institutions are under constant attack. Traditional rule-based detection systems, while useful, are no longer enough. Enter machine learning (ML) — the technology redefining fraud detection by spotting suspicious activity in real time and adapting to new threats before they cause damage.

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The Growing Fraud Threat in Philippine Banking

Digital banking adoption in the Philippines has surged in recent years, driven by initiatives like the BSP’s Digital Payments Transformation Roadmap and the expansion of fintech services. While these advancements boost financial inclusion, they also open the door to fraud.

According to the Bankers Association of the Philippines, reported cyber fraud incidents have increased steadily, with phishing, account takeover (ATO), and card-not-present (CNP) fraud among the top threats.

Key trends include:

  • Instant payment exploitation: Fraudsters leveraging PESONet and InstaPay for rapid fund transfers.
  • Social engineering scams: Convincing victims to disclose personal and banking details.
  • Cross-border fraud networks: Syndicates funnelling illicit funds via multiple jurisdictions.

In this environment, speed, accuracy, and adaptability are critical — qualities where ML excels.

Why Traditional Fraud Detection Falls Short

Rule-based fraud detection systems rely on predefined scenarios (e.g., flagging transactions over a certain threshold or unusual logins from different IP addresses). While they can catch known patterns, they struggle with:

  • Evolving tactics: Fraudsters quickly adapt once they know the rules.
  • False positives: Too many alerts waste investigator time and frustrate customers.
  • Lack of contextual awareness: Rules can’t account for the nuances of customer behaviour.

This is where machine learning transforms the game.

How Machine Learning Enhances Fraud Detection

1. Pattern Recognition Beyond Human Limits

ML models can process millions of transactions in real time, identifying subtle anomalies in behaviour — such as unusual transaction timing, frequency, or geolocation.

2. Continuous Learning

Unlike static rules, ML systems learn from new data. When fraudsters switch tactics, the model adapts, ensuring defences stay ahead.

3. Reduced False Positives

ML distinguishes between legitimate unusual behaviour and true fraud, cutting down on unnecessary alerts. This not only saves resources but improves customer trust.

4. Predictive Capability

Advanced algorithms can predict the likelihood of a transaction being fraudulent based on historical and behavioural data, enabling proactive intervention.

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Key Machine Learning Techniques in Banking Fraud Detection

Supervised Learning

Models are trained using labelled datasets — past transactions marked as “fraud” or “legitimate.” Over time, they learn the characteristics of fraudulent activity.

Unsupervised Learning

Used when there’s no labelled data, these models detect outliers and anomalies without prior examples, ideal for spotting new fraud types.

Reinforcement Learning

The system learns by trial and error, optimising decision-making as it receives feedback from past outcomes.

Natural Language Processing (NLP)

NLP analyses unstructured data such as emails, chat messages, or KYC documents to detect potential fraud triggers.

Real-World Fraud Scenarios in the Philippines Where ML Makes a Difference

  1. Account Takeover (ATO) Fraud – ML flags login attempts from unusual devices or geolocations while analysing subtle session behaviour patterns.
  2. Loan Application Fraud – Models detect inconsistencies in credit applications, cross-referencing applicant data with external sources.
  3. Payment Mule Detection – Identifying suspicious fund flows in real time, such as rapid inbound and outbound transactions in newly opened accounts.
  4. Phishing-Driven Transfers – Correlating unusual fund movement with compromised accounts reported across multiple banks.

Challenges in Implementing ML for Fraud Detection in the Philippines

  • Data Quality and Availability – ML models need vast amounts of clean, structured data. Gaps or inaccuracies can reduce effectiveness.
  • Regulatory Compliance – BSP regulations require explainability in AI models; “black box” ML can be problematic without interpretability tools.
  • Talent Gap – Limited availability of data science and ML experts in the local market.
  • Integration with Legacy Systems – Many Philippine banks still run on legacy infrastructure, complicating ML deployment.

Best Practices for Deploying ML-Based Fraud Detection

1. Start with a Hybrid Approach

Combine rule-based and ML models initially to ensure smooth transition and maintain compliance.

2. Ensure Explainability

Use explainable AI (XAI) frameworks so investigators and regulators understand why a transaction was flagged.

3. Leverage Federated Learning

Share intelligence across institutions without exposing raw data, enhancing detection of cross-bank fraud schemes.

4. Regular Model Retraining

Update models with the latest fraud patterns to stay ahead of evolving threats.

5. Engage Compliance Early

Work closely with risk and compliance teams to align ML use with BSP guidelines.

The Tookitaki Advantage: The Trust Layer to Fight Financial Crime

Tookitaki’s FinCense platform is built to help Philippine banks combat fraud and money laundering with Agentic AI — an advanced, explainable AI framework aligned with global and local regulations.

Key benefits for fraud detection in banking:

  • Real-time risk scoring on every transaction.
  • Federated intelligence from the AFC Ecosystem to detect emerging fraud typologies seen across the region.
  • Lower false positives through adaptive models trained on both local and global data.
  • Explainable decision-making that meets BSP requirements for transparency.

By combining advanced ML techniques with collaborative intelligence, FinCense gives banks in the Philippines the tools they need to protect customers, meet compliance standards, and reduce operational costs.

Conclusion: Staying Ahead of the Curve

Fraudsters in the Philippines are becoming more sophisticated, faster, and harder to trace. Relying on static, rules-only systems is no longer an option. Machine learning empowers banks to detect fraud in real time, reduce false positives, and adapt to ever-changing threats — all while maintaining compliance.

For institutions aiming to build trust in a rapidly digitising market, the path forward is clear: invest in ML-powered fraud detection now, and make it a core pillar of your risk management strategy.

Smarter Defences: How Machine Learning is Transforming Fraud Detection in Philippine Banking
Blogs
13 Aug 2025
5 min
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Stopping Fraud in Its Tracks: The Future of Transaction Fraud Detection in Singapore

Fraud doesn’t knock—it slips through unnoticed until it’s too late.

As digital payments accelerate across Singapore, financial institutions face a mounting challenge: detecting fraudulent transactions in real time, without slowing down legitimate users. From phishing scams and mule accounts to synthetic identities and account takeovers, transaction fraud has become smarter, faster, and harder to catch.

This blog explores how transaction fraud detection is evolving in Singapore, the gaps still present in legacy systems, and how AI-driven tools are helping financial institutions fight back.

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Why Transaction Fraud Detection Is Critical in Singapore

Singapore’s position as a fintech hub comes with exposure to increasingly sophisticated fraud schemes. According to the Singapore Police Force, scam-related crimes in 2024 accounted for over 70% of all crimes reported, with transaction fraud and unauthorised transfers making up a large portion of the losses.

The government’s drive for real-time payments — from PayNow to FAST — adds pressure on banks and fintechs to detect fraud instantly, without delaying genuine transactions.

Missed fraud isn’t just a financial risk — it erodes trust. And in Singapore’s tightly regulated environment, trust is everything.

Types of Transaction Fraud Facing Financial Institutions

Understanding the tactics fraudsters use is the first step toward stopping them. In Singapore, common forms of transaction fraud include:

1. Account Takeover (ATO)

Fraudsters use stolen credentials to gain control over an account and initiate transfers, bill payments, or cash withdrawals — often within minutes.

2. Social Engineering Scams

Victims are tricked into authorising payments themselves under false pretences — for example, investment scams, job scams, or fake relationships.

3. Money Muling

Fraudsters use mule accounts — often belonging to unsuspecting individuals — to route stolen or laundered funds through multiple hops.

4. Real-Time Payment Exploits

With instant transfer systems, once funds are sent, they’re often impossible to recover. Fraudsters exploit this urgency and invisibility.

5. Business Email Compromise (BEC)

Corporate payments are manipulated through phishing or spoofing attacks, redirecting funds to illicit accounts under false vendor names.

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Challenges in Transaction Fraud Detection

Despite investment in fraud controls, many Singaporean financial institutions still face persistent roadblocks:

1. High False Positives

Basic rules-based systems raise alerts for normal user behaviour, overwhelming fraud teams and increasing friction for genuine customers.

2. Lack of Real-Time Detection

Many systems rely on batch processing or delayed scoring, leaving gaps for fraudsters to exploit instant payment rails.

3. Inability to Detect Novel Patterns

Fraudsters constantly change tactics. Systems that only recognise known fraud signatures are easily bypassed.

4. Poor Cross-Border Visibility

Singapore is deeply integrated into global financial flows. A lack of insight into transaction trails beyond borders makes it harder to detect layered laundering and syndicated fraud.

What Effective Transaction Fraud Detection Looks Like Today

Modern fraud detection is about being predictive, not just reactive. Here's what best-in-class solutions offer:

AI + Machine Learning

Rather than using only static rules, intelligent systems learn from historical patterns, adapt to new behaviours, and improve accuracy over time.

Behavioural Profiling

These systems build user profiles based on login patterns, spending habits, device data, and more — flagging anything outside the norm in real time.

Network Analysis

Sophisticated fraud often involves mule networks or linked entities. Graph analysis helps identify suspicious linkages between accounts.

Federated Intelligence Sharing

Platforms like Tookitaki’s AFC Ecosystem allow institutions to benefit from typologies and red flags contributed by others — without sharing sensitive data.

Explainable AI

Regulators require transparency. Solutions must explain why a transaction was flagged, not just that it was.

How Tookitaki Is Powering Smarter Fraud Detection

Tookitaki’s FinCense platform is purpose-built to detect transaction fraud in real time. Here’s how it helps Singapore-based institutions stay ahead:

  • Agentic AI Framework: Modular AI agents continuously scan transactions, user behaviour, and risk context to identify fraud patterns — even emerging ones.
  • Scenario-Based Detection: Leverages real-world fraud scenarios from the AFC Ecosystem, including scams unique to Southeast Asia like fake job recruitment and QR-enabled mule layering.
  • Real-Time Simulation & Threshold Optimisation: Before deploying rules, institutions can simulate detection impact to reduce false positives.
  • Smart Disposition Engine: AI-generated summaries assist investigators by surfacing key risk insights for flagged transactions.
  • Federated Learning: Combines privacy-preserving AI with community-sourced intelligence for faster, more adaptive detection.

Whether you’re a digital bank, a payment gateway, or a traditional financial institution, FinCense provides the flexibility, speed, and accuracy needed for the Singaporean fraud landscape.

Key Strategies for Singaporean Firms to Strengthen Fraud Defences

1. Upgrade From Rule-Based to Hybrid Systems

A combination of dynamic rules and machine learning provides greater precision and adaptability.

2. Focus on Early Detection

Identify mule accounts, layered transfers, and behaviour anomalies before the fraud is completed.

3. Enable Seamless Analyst Workflows

Reduce alert fatigue with AI-driven prioritisation and investigation summaries.

4. Join Intelligence-Sharing Networks

Collaborate with platforms like the AFC Ecosystem to keep up with evolving fraud typologies.

5. Design for Real-Time Action

Ensure that fraud decisions can be made in milliseconds — and tie detection systems directly to block/hold actions.

Conclusion: Fraudsters Are Getting Smarter. Are You?

In Singapore’s fast-moving financial ecosystem, transaction fraud detection is no longer just a compliance function — it’s a competitive advantage.

Banks and fintechs that invest in modern, intelligent fraud prevention are not only protecting their bottom line — they’re protecting their brand and customer relationships.

📌 The future of fraud detection is proactive, predictive, and powered by community-led intelligence. Don’t just keep up — get ahead.

Stopping Fraud in Its Tracks: The Future of Transaction Fraud Detection in Singapore