Every peso that flows through the financial system is a target, and AML software makes sure it is clean.

In the Philippines, the pressure to strengthen anti-money laundering controls has never been greater. The country’s removal from the FATF grey list in 2024 was a step forward, but it came with a warning: regulators expect financial institutions to maintain vigilance. With cross-border remittances, a growing fintech ecosystem, and sophisticated fraudsters at play, banks and payment providers must rely on advanced AML software to protect themselves and their customers.

What Is AML Software?

AML software refers to technology platforms that help financial institutions comply with anti-money laundering (AML) regulations. These solutions are designed to detect, prevent, and report suspicious activity.

Core features typically include:

• Transaction Monitoring to spot unusual fund flows.
• Customer Screening against sanctions, watchlists, and politically exposed persons (PEPs).
• Case Management for investigations and audit trails.
• Risk Scoring to classify customers and transactions by risk level.
• Regulatory Reporting for timely Suspicious Transaction Reports (STRs) and Covered Transaction Reports (CTRs).

AML software is no longer just a compliance tool. It is a strategic system that helps safeguard financial institutions against regulatory penalties, reputational harm, and operational loss.

Why AML Software Matters in the Philippines

The Philippines is uniquely vulnerable to money laundering risks, making AML software essential. Key factors include:

1. High Remittance Inflows
   Overseas workers send more than USD 36 billion annually. Criminals exploit this volume for layering and structuring.
2. Fintech Growth
   New digital banks, e-wallets, and online lenders increase the risk surface for laundering and fraud.
3. Cross-Border Crime
   Syndicates exploit correspondent banking and weak regional oversight to funnel illicit funds.
4. Cash Dependency
   Significant reliance on cash complicates tracking and leaves blind spots in compliance systems.
5. Regulatory Demands
   The BSP and AMLC have intensified inspections, holding institutions accountable for weak AML controls.

How AML Software Works

1. Data Collection and Integration

AML systems ingest transaction, KYC, and external data to build a holistic view of customers.

2. Screening

Customer names are checked against global watchlists, sanction databases, and politically exposed persons lists.

3. Transaction Monitoring

Activity is monitored in real time or batch mode. Suspicious patterns such as rapid inflows and outflows, unusual counterparties, or round-tripping are flagged.

4. Alert Generation

Alerts are triggered when thresholds or unusual behaviours are detected.

5. Investigation and Case Management

Compliance officers review alerts using dashboards, supporting documentation, and decision logs.

6. Reporting

If suspicion remains, the software helps generate STRs and CTRs for timely submission to the AMLC.

Key Money Laundering Typologies Detected by AML Software in the Philippines

• Remittance Structuring
  Breaking large amounts into multiple small transactions to avoid reporting thresholds.
• Shell Companies
  Layering funds through entities with no legitimate business operations.
• Casino Laundering
  Rapid inflows and withdrawals at gaming venues inconsistent with customer profiles.
• Trade-Based Money Laundering (TBML)
  Over- or under-invoicing in cross-border shipments disguised as trade.
• Terror Financing Risks
  Frequent small-value transfers directed to or from high-risk geographies.

Challenges in Implementing AML Software

Even with its importance, Philippine financial institutions face obstacles in deploying AML systems effectively:

• Legacy Systems
  Outdated banking infrastructure complicates integration with modern AML solutions.
• Data Silos
  Customer data spread across products and channels reduces effectiveness.
• Resource Constraints
  Smaller banks may lack budgets to acquire advanced systems.
• Skills Gap
  There is a shortage of AML specialists and data scientists to run these platforms.
• Evolving Criminal Techniques
  Fraudsters use new tools such as AI, crypto, and social engineering faster than institutions can respond.

Best Practices for AML Software Deployment

1. Adopt a Risk-Based Approach
   Prioritise monitoring of high-risk customers and transactions.
2. Invest in Explainability
   Choose solutions that provide clear reasoning for flagged activity to satisfy regulators.
3. Integrate Across Channels
   Consolidate customer and transaction data for a 360-degree view.
4. Retrain Models Regularly
   Update detection capabilities with the latest fraud and laundering patterns.
5. Collaborate Across Institutions
   Participate in federated learning or typology-sharing ecosystems to strengthen monitoring.

Regulatory Expectations in the Philippines

The BSP and AMLC require AML software to:

• Monitor transactions continuously.
• Flag and report suspicious activity promptly.
• Apply enhanced due diligence for high-risk customers.
• Maintain auditable case management records.
• Demonstrate effectiveness during audits and inspections.

Non-compliance can result in penalties, reputational damage, and restricted operations.

The Tookitaki Advantage: Smarter AML Software for Philippine Banks

Tookitaki’s FinCense platform is built to provide Philippine financial institutions with a next-generation AML system.

Key benefits include:

• Agentic AI Detection that adapts to evolving risks in real time.
• Federated Intelligence via the AFC Ecosystem, offering scenarios and typologies contributed by experts across Asia-Pacific.
• Reduced False Positives through advanced behavioural analytics.
• Smart Disposition Engine that automates investigation summaries for faster STR filing.
• Explainable Outputs aligned with BSP and AMLC requirements.

By combining advanced AI with collaborative intelligence, FinCense acts as a trust layer, enabling banks to detect risks faster, investigate more effectively, and build regulator-ready compliance programs.

Conclusion: AML Software as a Strategic Necessity

AML software is not just about checking regulatory boxes. It is about protecting financial institutions, securing customer trust, and ensuring the stability of the Philippine financial system.

As criminals innovate and regulators raise the bar, banks and fintechs need systems that are intelligent, adaptive, and collaborative. The future of compliance belongs to those that invest in AML software that goes beyond rules, delivering real-time detection and long-term resilience.

Money laundering moves fast, but machine learning moves faster.

Why Malaysia Needs Smarter Anti Money Laundering

Malaysia’s financial system is facing unprecedented challenges. With the rise of digital wallets, QR-based payments, and instant transfers, financial institutions process millions of transactions every day. While these innovations drive convenience and economic growth, they also create new opportunities for money launderers.

From mule accounts that funnel illicit proceeds to cross-border layering through remittances, criminals are becoming more sophisticated. Bank Negara Malaysia (BNM) has tightened regulations, aligning with the Financial Action Task Force (FATF) to ensure that banks and fintechs adopt risk-based approaches.

Yet many institutions still rely on outdated, rule-based systems that cannot keep up. The need for smarter tools is clear. This is where machine learning in anti money laundering (AML) becomes the new compliance frontier.

Why Anti Money Laundering Needs an Upgrade

Traditional AML systems are struggling against modern financial crime. Consider the challenges:

• Mule networks recruit young workers, students, and vulnerable individuals to move illicit funds.
• Shell companies are used to disguise ownership and funnel proceeds of corruption or fraud.
• Cross-border transactions make it difficult to track illicit flows across jurisdictions.
• Scams and frauds are increasingly digital, often slipping past static monitoring systems.

BNM expects institutions to not only comply with reporting requirements but also demonstrate proactive detection and risk management. This makes machine learning more than a technology upgrade. It is a regulatory and business imperative.

What is Machine Learning in Anti Money Laundering?

Machine learning (ML) refers to algorithms that learn from data to identify patterns, anomalies, and risks without needing explicit programming for every scenario.

In AML, this means going beyond static rules like “flag all transactions over RM50,000” to instead detect suspicious patterns such as:

• A customer suddenly making multiple high-value transfers inconsistent with their history.
• Rapid in-and-out flows across different accounts suggesting layering.
• Multiple unrelated customers sending funds to the same recipient, often linked to mule accounts.

Unlike traditional systems, ML models evolve. They improve accuracy with every transaction reviewed, reducing false positives and detecting new laundering techniques.

Key Benefits of Machine Learning in AML

1. Real-Time Detection

Machine learning models analyse transactions instantly, allowing banks to stop suspicious activity before funds leave the system.

2. Reduced False Positives

By understanding context and behaviour, ML reduces unnecessary alerts, freeing compliance teams to focus on real risks.

3. Adaptability to New Typologies

ML models continuously learn, spotting new laundering methods that static rules may miss.

4. Scalability

ML systems handle millions of daily transactions, essential in Malaysia’s high-volume digital payments environment.

5. Regulatory Alignment

Explainable ML models provide transparency, ensuring that regulators can understand why a transaction was flagged.

Challenges with Legacy AML Systems

Older AML monitoring systems are increasingly unfit for purpose:

• Static rules fail to detect evolving threats.
• Alert fatigue from high false positives overwhelms compliance staff.
• Lack of explainability undermines regulator confidence.
• High compliance costs make operations inefficient, especially for smaller banks.

Malaysia’s financial sector cannot afford to rely on systems designed for a slower, less complex world.

Why Malaysia Must Adopt Machine Learning in AML Now

Several factors make ML adoption urgent in Malaysia:

The rise of instant payments and QR codes

With DuitNow QR becoming a national standard, funds move instantly. Manual reviews are too slow to prevent laundering.

Remittance vulnerabilities

As a regional remittance hub, Malaysia faces high exposure to cross-border laundering and trade-based money laundering.

Scam proliferation

Fraudsters use phishing, fake investments, and even deepfakes to deceive customers, funnelling proceeds through banks and fintechs.

Escalating compliance costs

BNM’s regulatory requirements are expanding. Manual monitoring is too costly, pushing banks to seek automation and intelligence.

Tookitaki’s FinCense: Machine Learning in AML in Action

This is where Tookitaki’s FinCense comes in. Positioned as the trust layer to fight financial crime, FinCense brings machine learning into AML with a design tailored for Malaysia and ASEAN.

Agentic AI Workflows

FinCense uses Agentic AI, where intelligent agents automate the entire AML investigation cycle. From alert triage to generating regulator-ready narratives, Agentic AI reduces workload and improves accuracy.

Federated Learning with the AFC Ecosystem

Through the AFC Ecosystem, FinCense leverages shared insights from more than 200 financial institutions. Malaysian banks benefit from early detection of new laundering patterns first seen in neighbouring markets.

Explainable AI

Transparency is essential in compliance. FinCense provides clear reasoning for every alert, making it regulator-friendly and audit-ready.

End-to-End Integration

Instead of siloed systems, FinCense unifies transaction monitoring, screening, fraud detection, and case management. This gives institutions a single view of risk.

ASEAN Localisation

Scenarios and typologies are tuned to ASEAN realities, from mule accounts to QR exploitation, ensuring accuracy and relevance.

Step-by-Step: How Banks Can Adopt ML in AML

For Malaysian banks, adopting ML in AML can be broken into practical steps:

Step 1: Map Current Risks

Identify primary threats such as mule networks, layering, or shell company misuse.

Step 2: Integrate Data Sources

Consolidate customer, transaction, and behavioural data to give ML models the depth they need.

Step 3: Deploy Machine Learning Models

Use supervised learning for known typologies and unsupervised learning for detecting new anomalies.

Step 4: Build Explainability

Choose solutions that provide clear reasons for alerts to maintain regulator trust.

Step 5: Continuously Update with New Typologies

Leverage networks like the AFC Ecosystem to stay ahead of criminals.

Scenario Example: Laundering through QR Payments

Imagine fraudsters attempting to launder illicit proceeds by splitting them into dozens of small QR-based transactions. Funds are layered through merchant accounts and eventually remitted overseas.

With a traditional system:

• Transactions may appear too small to trigger thresholds.
• Laundering could go undetected until much later.

With FinCense’s ML-driven AML:

• Anomaly detection identifies unusual transaction clustering.
• Federated learning recognises the mule pattern from cases in Singapore and the Philippines.
• Agentic AI triages the alert and generates a clear case narrative for the compliance officer.

The laundering attempt is stopped in real time, preventing further abuse.

Benefits for Malaysian Banks and Fintechs

Adopting machine learning in AML delivers:

• Lower compliance costs through automation and efficiency.
• Faster detection and prevention of laundering.
• Regulatory confidence with explainable models.
• Improved customer trust in digital banking services.
• Competitive advantage in attracting partners and investors.

The Future of Machine Learning in AML

Looking forward, machine learning will only deepen its role in compliance:

• Integration with open banking data will give richer customer insights.
• AI-driven scams will push banks to rely on equally intelligent defences.
• Regional collaboration through federated learning will strengthen collective resilience.
• Hybrid models of AI and human expertise will strike the right balance of speed and judgement.

Malaysia has the opportunity to lead ASEAN by adopting machine learning not just as a tool, but as the core of its compliance framework.

Conclusion

Machine learning in anti money laundering is no longer a future vision. It is the practical solution Malaysia’s financial sector needs today. Traditional rule-based systems cannot keep up with the scale and complexity of modern laundering risks.

With Tookitaki’s FinCense, banks and fintechs in Malaysia gain a trust layer that combines machine learning, explainability, and collective intelligence. The result is a compliance framework that is proactive, adaptive, and ready for the future.

For Malaysian institutions, the path forward is clear: embrace machine learning to turn AML from a regulatory burden into a strategic advantage.

Remittance and cross-border payments are lifelines for many Australians, but they also present high risks for money laundering that demand stronger AML frameworks.

Australia is one of the world’s most active remittance markets. Migrant communities regularly send money home to countries across Asia, the Pacific, and Africa. Businesses also rely heavily on international payments for trade. In 2024 alone, Australians sent more than AUD 12 billion abroad through formal remittance channels.

While remittances support families and economies, they are also vulnerable to misuse by criminals. Fraudsters exploit cross-border payments to launder illicit funds, finance terrorism, and evade regulatory oversight. For banks, fintechs, and money transfer operators, AML in remittance and cross-border payments is a top priority, particularly as AUSTRAC continues to tighten supervision.

Why Remittance and Cross-Border Payments Are High Risk

1. Complex Transaction Chains

Payments often pass through multiple institutions and jurisdictions, making it difficult to track the origin and destination of funds.

2. Informal Remittance Channels

Some funds bypass formal systems entirely, moving through unregistered money transfer operators or informal hawala networks.

3. High Volume of Small Transactions

Criminals break large sums into smaller transactions to avoid detection thresholds.

4. Cross-Border Regulatory Gaps

Different jurisdictions have varying AML standards, creating loopholes for laundering.

5. Customer Base Diversity

Remittance services often cater to migrant workers and underserved populations, which increases exposure to identity fraud and mule account risks.

AUSTRAC and Cross-Border Payment Obligations

Under the AML/CTF Act 2006, reporting entities offering remittance and cross-border services must:

• Register with AUSTRAC as a remittance service provider.
• Implement AML/CTF programs with tailored risk assessments.
• Verify customer identities through KYC/CDD processes.
• Report suspicious transactions via Suspicious Matter Reports (SMRs).
• File International Funds Transfer Instructions (IFTIs) for cross-border payments over AUD 10,000.
• Keep records for at least seven years.

Failure to comply can result in significant fines, reputational damage, and even loss of licence.

Common Laundering Typologies in Remittance and Cross-Border Payments

1. Structuring (Smurfing): Breaking large sums into smaller remittances to avoid reporting thresholds.
2. Mule Accounts: Criminals recruit individuals to receive and transfer illicit funds internationally.
3. Trade-Based Money Laundering (TBML): Over- or under-invoicing trade transactions to disguise illicit fund movements.
4. Third-Party Transfers: Using unrelated accounts to obscure true beneficiaries.
5. Round-Tripping: Funds sent abroad and quickly returned as “legitimate” investments.
6. Terrorism Financing: Small-value remittances used to fund terrorist networks.

Red Flags in Remittance Transactions

• Customers making frequent transfers just below reporting thresholds.
• Transfers to or from high-risk jurisdictions with limited transparency.
• Multiple senders using the same beneficiary account.
• Sudden increase in remittance activity inconsistent with customer profile.
• Customers reluctant to provide source-of-funds documentation.
• Beneficiaries linked to politically exposed persons (PEPs) or sanctions lists.

Challenges in AML for Cross-Border Payments

• Real-Time Risks: With NPP and PayTo integration, funds can move overseas instantly.
• High False Positives: Traditional monitoring generates large volumes of irrelevant alerts.
• Data Silos: Fragmented systems make it hard to track cross-border fund flows.
• Cost of Compliance: Remittance operators often operate on thin margins, making compliance investments challenging.
• Technology Gaps: Smaller institutions may lack access to advanced monitoring platforms.

Best Practices for AML in Remittance and Cross-Border Payments

1. Adopt Real-Time Monitoring: Batch reviews cannot keep pace with instant payments.
2. Apply a Risk-Based Approach: Allocate resources based on jurisdiction, transaction type, and customer profile.
3. Use Advanced Analytics and AI: Detect anomalies in remittance behaviour more effectively.
4. Leverage Federated Intelligence: Access insights from other banks and remittance operators to spot emerging typologies.
5. Strengthen KYC/CDD: Use biometric and digital onboarding tools for identity verification.
6. Collaborate Across Borders: Work with international regulators and financial intelligence units (FIUs) to close loopholes.

Case Example: Community-Owned Banks Strengthening Cross-Border AML

Community-owned banks such as Regional Australia Bank and Beyond Bank have integrated advanced compliance solutions to manage cross-border AML risks. By adopting AI-powered monitoring platforms, they ensure compliance with AUSTRAC requirements while maintaining trust with their customer base.

Spotlight: Tookitaki’s FinCense for Cross-Border AML

FinCense, Tookitaki’s end-to-end compliance platform, is designed to address the complexities of remittance and cross-border payments.

• Real-Time Monitoring: Detects suspicious activity across NPP, PayTo, and international corridors.
• Agentic AI: Continuously learns from evolving laundering typologies.
• Federated Intelligence: Draws from global scenarios shared through the AFC Ecosystem.
• Regulator Reporting: Automates IFTI and SMR submissions for AUSTRAC.
• Audit Trails: Tracks every investigator action for transparency.
• Cross-Channel Coverage: Integrates banking, remittance, wallets, cards, and crypto.

FinCense empowers institutions to stay ahead of evolving risks while reducing the operational burden of cross-border AML compliance.

Future of AML in Remittance and Cross-Border Payments

1. AI-First Compliance: AI copilots will automate investigations and improve reporting quality.
2. Deeper Integration with NPP/PayTo: Remittance transactions will increasingly move in real time.
3. Global Collaboration: More regulators will push for federated learning and cross-border intelligence sharing.
4. Digital Identity Solutions: Biometric verification will become standard for remittance customers.
5. Focus on Cost Efficiency: Automation will be critical as compliance costs rise.

Conclusion

Remittance and cross-border payments are essential to Australia’s economy and society, but they also pose significant money laundering risks. AUSTRAC’s regulations make it clear that institutions must strengthen their AML frameworks to address these vulnerabilities.

Community-owned banks like Regional Australia Bank and Beyond Bank show that even mid-sized institutions can deliver strong cross-border compliance by adopting advanced technology. Platforms like Tookitaki’s FinCense, with its Agentic AI and federated intelligence, provide the tools to detect suspicious activity, reduce false positives, and meet AUSTRAC standards.

Pro tip: The key to AML in remittance is collaboration. By sharing intelligence and leveraging AI-powered platforms, institutions can turn compliance from a challenge into a competitive advantage.