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An Overview of Thailand's AML Laws and Regulations

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Tookitaki
6 min
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Anti-money laundering (AML) laws and regulations are essential for ensuring financial integrity, security, and stability. The government has implemented several AML laws and regulations in Thailand to prevent money laundering, terrorist financing, and other financial crimes. This blog post provides an overview of Thailand's AML laws and regulations, the regulatory bodies responsible for enforcing them, and the penalties for non-compliance.

AML Laws and Regulations in Thailand

Thailand's AML laws and regulations include the Anti-Money Laundering Act B.E. 2542 (1999), the Counter-Terrorism Financing Act B.E. 2559 (2016), and the Financial Institutions Business Act B.E. 2551 (2008). These laws aim to prevent and detect money laundering, terrorist financing, and other financial crimes by requiring financial institutions to implement AML programs, conduct customer due diligence (CDD), and report suspicious transactions to regulatory authorities.

In 1999, the Anti-Money Laundering Act (No. 6) B.E. 2565 (2022) was enacted to eradicate illegal activities' funding in Thailand, including drug trade, corruption, and fraud. The Act has undergone recent revisions to align it with global Anti-Money Laundering legislation standards. The King revised the Act's modifications with Parliament's advice and consent in late 2022, and they were published in the Government Gazette on October 24th, 2022. Under Thai law, the Act's revisions became effective 60 days later, on December 23rd, 2022.

The key areas of the Act that have been amended are the provisions protecting the injured party's rights for predicate offences that have suffered damages to life, body, mind, freedom, health, or reputation and the rights of the property beneficiary requested by the Public Prosecutor.

The regulatory bodies responsible for enforcing Thailand's AML laws and regulations include the Anti-Money Laundering Office (AMLO), the Office of the Narcotics Control Board (ONCB), and the Counter-Terrorism Financing Office (CTFO). These agencies work together to investigate and prosecute AML violations, impose sanctions and penalties, and promote public awareness of AML issues.

Non-compliance with Thailand's AML laws and regulations can result in severe penalties, including fines, imprisonment, and revocation of business licenses. Financial institutions must take proactive steps to ensure compliance with AML laws and regulations to avoid these consequences.

AML Compliance Challenges for Businesses in Thailand

Money laundering poses a significant risk for businesses in Thailand, with non-compliance leading to severe legal and financial consequences. One of the significant challenges for businesses is the complexity and constantly evolving nature of AML regulations in Thailand. It is also difficult to track high-risk customers manually due to the large volume of data that must be analysed. Inefficient communication channels among different departments, lack of training, and insufficient resources also contribute to the challenges businesses face in achieving AML compliance.

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The Role of Technology in AML Compliance for Businesses in Thailand

Technology is changing AML compliance, providing businesses with efficient and effective tools to detect and prevent money laundering activities. Machine learning algorithms and artificial intelligence can be used to analyse vast amounts of data in real time, identify patterns, and detect unusual behaviour that could indicate potential money laundering activities. This technology also automates AML compliance tasks, enabling businesses to comply with regulations without adding extra resources.

Moreover, technology gives businesses a centralized and comprehensive view of all their data, facilitating cross-functional collaboration and providing real-time insights into customer activities. This helps businesses to identify high-risk customers and transactions, allowing them to take preventive measures to avoid non-compliance.

By embracing technology to enhance AML compliance, businesses can improve their risk management strategies, protect their reputation, and avoid financial penalties.

How Tookitaki's AML Solutions Can Help Thailand

Tookitaki's AML solutions offer a comprehensive approach to AML compliance that leverages advanced technology, federated learning, and machine learning algorithms. These solutions help financial institutions automate AML processes, streamline compliance workflows, and reduce false positives and false negatives.

Tookitaki's solutions can help financial institutions in Thailand comply with AML laws and regulations by transaction monitoring, automating customer due diligence, and detecting suspicious activities. The solutions can also provide customised risk assessments, alerts, and reports to support AML compliance efforts and facilitate regulatory reporting.

Tookitaki's AML solutions offer a powerful tool for financial institutions in Thailand to enhance their AML compliance programs and reduce the risk of AML violations. By leveraging Tookitaki's advanced technology and expertise, financial institutions can improve their AML capabilities and protect their businesses from reputational, financial, and regulatory risks.

Tookitaki AFC Ecosystem and AMLS

Tookitaki's AML Solutions for Businesses in Thailand

Tookitaki is a pioneer in the fight against financial crime, leveraging a unique and innovative approach that transcends traditional solutions. Tookitaki’s two platforms: the  Anti-Money Laundering Suite (AMLS) and the Anti-Financial Crime (AFC) Ecosystem, work in tandem to address the limitations of siloed systems in combating money laundering.

The AFC Ecosystem

The AFC Ecosystem is a community-based platform that facilitates the sharing of information and best practices in the battle against financial crime. Powering this ecosystem is our Typology Repository, a living database of money laundering techniques and schemes. This repository is enriched by the collective experiences and knowledge of financial institutions, regulatory bodies, and risk consultants worldwide, encompassing a broad range of typologies from traditional methods to emerging trends.

The AFC Ecosystem consists of two main components: the AFC Network - a global network of subject matter experts who contribute the latest typologies, and the Typology Repository - the largest federated database of money laundering patterns, contributed and validated by experts.

The Typology Repository is an exhaustive, ready-to-use database from which typologies can be searched and integrated into AMLS with a single click. The database operates solely on patterns and does not store any data, ensuring full privacy protection. Using a user-friendly interface, network members can easily create and share their typologies with each other without compromising any customer information. 

The AMLS

The AMLS is an end-to-end operating system that modernises compliance processes for banks and fintechs. AMS is redefining how the financial service industry thinks about and approaches AML compliance. It is an award-winning, self-adaptive machine learning solution that helps FIs to build a comprehensive risk-based anti-money laundering compliance program. It is an enterprise-wide solution built around three core modules: screening, risk scoring and transaction monitoring.

The AMLS platform has a modular design and consists of the following modules.

Benefits of Implementing Tookitaki's AML Solutions

Implementing Tookitaki's AML solutions can provide several benefits for businesses in Thailand. One of the key benefits is increased efficiency. Tookitaki's solutions can automate many of the manual processes involved in AML compliance, such as transaction monitoring and sanctions screening. This can free up compliance teams to focus on higher-value tasks, such as investigating and resolving alerts.

Tookitaki's solutions can also help businesses reduce costs associated with AML compliance. By automating many processes, companies can reduce their reliance on manual labour, which can be expensive. In addition, by identifying and flagging suspicious transactions more accurately, Tookitaki's solutions can help reduce false positives, which can be costly to investigate.

Additionally,  The AFC ecosystem provides a platform for financial institutions to share knowledge and collaborate on fighting financial crime. The Typology Repository, for example, allows institutions to share information on common money-laundering techniques and typologies, making it easier to detect and prevent such activities.

Finally, implementing Tookitaki's AML solutions can help businesses improve their risk management capabilities. By identifying potential AML risks more accurately and efficiently, companies can take proactive measures to mitigate these risks and prevent financial losses, regulatory penalties, and reputational damage.

Enhancing AML Compliance Efficiency: Tookitaki's Solutions for Businesses in Thailand

In conclusion, AML compliance is critical for businesses operating in Thailand to avoid penalties and reputational damage. However, achieving compliance can be challenging due to the complex regulatory environment and limitations of traditional approaches. Technology, such as Tookitaki's AML solutions, can help businesses in Thailand achieve AML compliance efficiently and effectively.

Tookitaki's AML solutions provide businesses with real-time transaction monitoring, data analytics, and AI-powered risk assessments, enabling them to detect and prevent potential money laundering activities. Implementing Tookitaki's AML solutions can increase efficiency, reduce costs, and improve risk management.

To learn more about Tookitaki's AML solutions and how they can be implemented to achieve efficient and effective AML compliance, businesses in Thailand are encouraged to book a demo and explore the benefits of utilizing cutting-edge technology to meet their AML compliance needs.


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Blogs
14 Aug 2025
5 min
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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
read

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
read

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