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.

Transaction screening is a frontline defence in Australia’s AML framework, helping banks stop suspicious activity before it becomes financial crime.

Introduction

In the fight against money laundering and terrorism financing, one of the most critical safeguards is AML transaction screening. Every transaction a bank processes is an opportunity to either catch suspicious activity or allow it to slip through the cracks. In Australia, where AUSTRAC enforces strict compliance standards and real-time payments like the New Payments Platform (NPP) dominate, transaction screening has become more vital than ever.

But screening is not without challenges. Financial institutions must balance regulatory expectations, rising alert volumes, and evolving fraud typologies while ensuring customers enjoy seamless experiences. This blog explores why transaction screening matters in Australia, common pitfalls, and how banks can get it right.

What is AML Transaction Screening?

AML transaction screening is the process of checking financial transactions against risk indicators such as:

• Sanctions lists (e.g., United Nations, OFAC, AUSTRAC directives).
• Politically Exposed Persons (PEPs) lists.
• Adverse media sources highlighting high-risk individuals or entities.
• Watchlists for terrorism financing or organised crime networks.

The goal is to stop suspicious or prohibited transactions before they are processed, keeping financial systems safe and compliant.

Why Transaction Screening is Critical in Australia

1. AUSTRAC Expectations

AUSTRAC requires reporting entities to screen transactions in real time and report any suspicious activity promptly. Weak screening exposes banks to fines and reputational damage.

2. Real-Time Payment Challenges

With NPP and PayTo, transactions settle instantly. Banks have milliseconds to screen and act.

3. Global Sanctions Landscape

Geopolitical events frequently update sanctions lists. Australian banks must ensure their systems adapt immediately.

4. Fraud and Laundering Typologies

Criminals use mule accounts, shell companies, and layering through remittances to bypass weak screening controls.

5. Reputation and Trust

A single missed sanctions breach can erode years of customer confidence and brand value.

Common Pitfalls in Transaction Screening

1. High False Positives
   Poorly calibrated systems generate thousands of unnecessary alerts, overwhelming investigators.
2. Data Quality Issues
   Inconsistent customer records lead to mis-matches and missed detections.
3. Latency in Real-Time Systems
   Delays in screening can disrupt customer experience and create friction.
4. Outdated Watchlists
   Failure to update sanctions or PEP lists leads to compliance breaches.
5. Fragmented Systems
   Disjointed platforms make it hard to connect alerts with case investigations.

Red Flags Identified During Screening

• Transfers involving high-risk jurisdictions.
• Transactions just below reporting thresholds.
• Payments linked to newly opened or inactive accounts.
• Frequent small transfers inconsistent with customer profile.
• Transfers involving PEPs or sanctioned entities.
• Transactions linked to negative news or adverse media reports.

AUSTRAC’s Role in Transaction Screening

AUSTRAC requires reporting entities to:

• Screen all transactions against sanctions and PEP lists.
• Submit Suspicious Matter Reports (SMRs) when screening reveals unusual activity.
• File Threshold Transaction Reports (TTRs) for cash transactions over AUD 10,000.
• Monitor cross-border flows through International Funds Transfer Instructions (IFTIs).
• Keep detailed records of all screening outcomes.

Institutions that fail to comply face not only financial penalties but also reputational consequences.

Best Practices for AML Transaction Screening in Australia

1. Adopt Real-Time Screening Tools
   Batch processing is not enough in the era of NPP and PayTo.
2. Integrate AI and Machine Learning
   Adaptive models reduce false positives while improving detection accuracy.
3. Maintain Up-to-Date Watchlists
   Automate updates for sanctions, PEPs, and adverse media databases.
4. Use a Risk-Based Approach
   Prioritise screening intensity based on customer and jurisdiction risk.
5. Invest in Data Quality
   Clean, consistent customer data ensures better screening outcomes.
6. Link Screening with Case Management
   Ensure alerts feed directly into investigation workflows for faster resolution.
7. Train Compliance Teams Continuously
   Equip staff to understand new fraud typologies and screening updates.

Case Example: Community-Owned Banks Strengthening Screening

Community-owned banks such as Regional Australia Bank and Beyond Bank are improving transaction screening with advanced compliance platforms. Despite smaller budgets than Tier-1 banks, they have successfully implemented real-time screening and reduced false positives while maintaining strong customer trust.

Spotlight: Tookitaki’s FinCense for Screening

FinCense, Tookitaki’s compliance platform, integrates advanced transaction screening capabilities for Australian institutions.

• Real-Time Screening: Monitors transactions instantly across NPP, PayTo, and cross-border corridors.
• Agentic AI: Learns from screening outcomes to improve accuracy and reduce false positives.
• Federated Intelligence: Accesses global typologies contributed by the AFC Ecosystem.
• Integrated Case Management: Links screening alerts to investigations and regulator-ready reports.
• Sanctions, PEP, and Adverse Media Screening: Ensures compliance with AUSTRAC and global standards.
• Cross-Channel Coverage: Unifies monitoring across banking, cards, remittances, and wallets.

FinCense helps banks strike the balance between compliance, efficiency, and customer experience.

The Future of Transaction Screening in Australia

1. Explainable AI Models
   Banks will increasingly adopt AI tools that regulators can understand and audit.
2. Deeper Integration with Real-Time Payments
   Screening systems must align seamlessly with NPP and PayTo.
3. Industry Collaboration
   Shared watchlists and federated learning will strengthen defences.
4. Automation of Reporting
   SMRs, TTRs, and IFTIs will increasingly be generated automatically.
5. Customer-Centric Security
   Screening systems will minimise disruption to legitimate customers while targeting fraud more precisely.

Conclusion

AML transaction screening is one of the most important compliance safeguards for Australian banks, fintechs, and remittance providers. With AUSTRAC demanding strong oversight and real-time payments making detection harder, the stakes have never been higher.

Community-owned banks like Regional Australia Bank and Beyond Bank demonstrate that effective screening is achievable even without Tier-1 budgets. Platforms like Tookitaki’s FinCense provide the tools to modernise transaction screening, reduce false positives, and build customer trust.

Pro tip: The best screening systems do not just block risks. They create trust by protecting customers while keeping banking seamless.

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.