As AUSTRAC tightens oversight, Australian banks are rethinking how to build risk-based AML frameworks that are both compliant and future-ready.

Introduction

In 2025, money laundering is not just a criminal issue — it is a systemic challenge for Australia’s financial institutions.
Criminal networks use complex layering techniques, shell companies, and cross-border remittances to conceal illicit proceeds. The result: growing regulatory pressure on banks to demonstrate that their compliance programs are truly risk-based.

A risk-based AML framework ensures that banks allocate resources intelligently — focusing on higher-risk customers, products, and geographies instead of applying the same controls everywhere. It is the cornerstone of effective anti-money laundering (AML) and counter-terrorism financing (CTF) compliance.

What Is a Risk-Based AML Framework?

A risk-based AML framework is a structured approach that allows financial institutions to assess, prioritise, and manage money-laundering and terrorism-financing risks based on their likelihood and potential impact.

This framework enables banks to:

• Tailor controls to their specific risk profile.
• Deploy enhanced due diligence (EDD) where needed.
• Maintain efficient compliance operations.
• Align with AUSTRAC’s guidance and the AML/CTF Act 2006.

In short, it ensures compliance efforts are proportionate, not excessive.

Why Risk-Based Approaches Matter for Australian Banks

1. AUSTRAC’s Expectations

AUSTRAC requires reporting entities to identify, assess, and mitigate money-laundering and terrorism-financing risks. A risk-based program must be reviewed regularly and updated as products or customer profiles change.

2. Increased Complexity of Financial Crime

With digital banking and cross-border payments, traditional rules-based systems can no longer keep up. A dynamic risk framework provides flexibility to respond to emerging threats.

3. Balancing Compliance and Customer Experience

Over-screening legitimate customers frustrates users and increases costs. Risk-based segmentation helps focus scrutiny where it matters most.

4. Avoiding Penalties and Reputational Damage

AUSTRAC has imposed multi-million-dollar fines on institutions that failed to maintain adequate AML programs. A strong risk-based approach demonstrates diligence and accountability.

Core Components of a Risk-Based AML Framework

1. Enterprise-Wide Risk Assessment (EWRA)

The foundation of any AML framework is a thorough risk assessment that covers:

• Products and services offered.
• Delivery channels (digital, branch, agent).
• Customer types and jurisdictions.
• Volume and complexity of transactions.
• Emerging financial-crime typologies.

The EWRA should be data-driven and reviewed annually.

2. Customer Risk Profiling

Banks must categorise customers as low, medium, or high risk based on factors such as occupation, geography, transaction behaviour, and source of wealth.

3. Customer Due Diligence (CDD) and Enhanced Due Diligence (EDD)

CDD procedures apply to all customers, while EDD is reserved for higher-risk entities such as politically exposed persons (PEPs), offshore clients, or entities dealing in high-risk sectors.

4. Ongoing Monitoring

Continuous monitoring of customer activity ensures that risk profiles remain current. Sudden spikes in transaction frequency or value may trigger review.

5. Governance and Accountability

A dedicated compliance officer should oversee framework implementation, supported by internal audit and senior management oversight.

6. Training and Awareness

Regular training keeps staff alert to new typologies, especially those highlighted in AUSTRAC’s national risk assessments.

How AUSTRAC Defines “Risk-Based”

AUSTRAC’s guidance stresses that risk-based does not mean risk-tolerant.
Banks must demonstrate that:

• Risks have been formally identified and rated.
• Controls are proportionate to those risks.
• Systems can adapt dynamically as risks evolve.
• Governance mechanisms ensure accountability.

Institutions should be able to explain why certain controls were chosen and how they mitigate specific risks.

Common Challenges for Australian Banks

• Fragmented Data: Risk information sits in silos across departments.
• Manual Risk Scoring: Static spreadsheets limit scalability and consistency.
• Inconsistent KYC Practices: Variability across products and regions weakens coverage.
• High False Positives: Poorly calibrated thresholds overwhelm investigators.
• Limited Use of Advanced Analytics: Traditional frameworks lack predictive power.

These challenges are pushing banks to embrace automation, AI, and federated intelligence.

Designing a Risk-Based AML Framework: Step-by-Step

Step 1: Define Risk Appetite

Set clear boundaries for acceptable risk, endorsed by the board.

Step 2: Conduct Enterprise-Wide Risk Assessment

Use data analytics to evaluate inherent risks across products, customers, and geographies.

Step 3: Develop Risk-Scoring Models

Assign scores based on probability and potential impact, ensuring transparent logic that can be defended to regulators.

Step 4: Align Controls with Risk Scores

Deploy stronger CDD, monitoring, or escalation paths for higher-risk segments.

Step 5: Implement Automated Monitoring

Adopt AI-enabled tools for continuous, real-time assessment of transactions and customer behaviour.

Step 6: Validate and Review Regularly

Conduct periodic model validation and compliance audits to ensure ongoing alignment with AUSTRAC requirements.

Leveraging Technology for Risk-Based Compliance

AI and Machine Learning

AI models identify patterns that correlate with higher ML/TF risk and refine risk scoring dynamically.

Federated Intelligence

Through networks like the AFC Ecosystem, banks can access anonymised typologies contributed by peers to enhance their own risk models without sharing customer data.

Integrated Case Management

Automation connects alerts, customer information, and audit trails, reducing manual workload and improving accuracy.

Real-Time Risk Scoring

Instead of relying on static KYC data, modern systems update risk scores as customer behaviour changes.

Case Example: Regional Australia Bank

Regional Australia Bank, a community-owned institution, has implemented a dynamic, data-driven AML framework tailored to its customer base. By combining automated monitoring with a risk-based approach, it has reduced false positives and ensured compliance without compromising service quality.

The bank’s proactive adoption of intelligent compliance technology demonstrates how regional and mid-tier banks can meet AUSTRAC’s high standards with agility and innovation.

Spotlight: Tookitaki’s FinCense

FinCense, Tookitaki’s end-to-end compliance platform, is designed to help Australian banks operationalise risk-based AML frameworks effectively.

• AI-Driven Risk Scoring: Continuously evaluates customer and transaction risk in real time.
• Agentic AI: Learns from evolving financial-crime typologies, improving accuracy automatically.
• Federated Learning: Shares anonymised insights across institutions to strengthen detection models.
• Integrated Case Management: Connects AML, fraud, and CFT operations for unified oversight.
• Explainable AI: Provides full transparency to auditors and regulators.
• AUSTRAC-Ready Reporting: Automates SMRs, TTRs, and IFTIs with complete audit trails.

FinCense transforms the traditional rule-based model into a proactive, risk-driven compliance ecosystem.

Best Practices for Building a Strong Risk-Based AML Program

1. Embed Risk in Every Decision: Make risk scoring part of product design, onboarding, and monitoring.
2. Invest in Explainable AI: Ensure all model decisions can be justified to AUSTRAC.
3. Maintain Centralised Risk Data: Unify data from all channels for consistent risk assessment.
4. Update Typologies Regularly: Incorporate insights from external intelligence networks.
5. Train Continuously: Keep staff informed about new risks, such as digital-payment and mule typologies.
6. Engage the Board: Senior leadership should actively review and approve the risk framework.

The Future of Risk-Based AML in Australia

1. AI-Native Compliance Frameworks: AI copilots will assist investigators and automate low-risk cases.
2. Federated Risk Sharing: Banks will collaborate securely to identify systemic risks faster.
3. Dynamic Risk Profiles: Risk scores will evolve in real time based on customer and transaction behaviour.
4. Integration with Real-Time Payments: NPP and PayTo transactions will trigger instant risk evaluation.
5. Stronger Regulatory-Tech Collaboration: AUSTRAC will continue promoting innovation through RegTech partnerships.

Conclusion

Designing a risk-based AML framework is not just a regulatory requirement — it is a strategic advantage for banks aiming to protect customers and strengthen trust.

By combining human expertise with intelligent technology, Australian banks can stay ahead of criminals and regulators alike. Regional Australia Bank’s example shows that a community-focused institution can meet AUSTRAC’s standards while maintaining operational efficiency.

With Tookitaki’s FinCense, institutions can build adaptive, transparent, and data-driven AML frameworks that evolve alongside emerging risks.

Pro tip: A risk-based approach is not a one-time project — it is a living framework that grows smarter with every transaction, every alert, and every lesson learned.

In a world of real-time payments, financial crime moves fast — automation helps banks move faster.

The Philippines is witnessing a rapid digital transformation in its financial sector. Mobile wallets, online banking, and cross-border remittances have brought financial inclusion to millions. But they have also opened new doors for fraudsters and money launderers. As regulators tighten their expectations following the country’s removal from the FATF grey list, institutions are turning to automated transaction monitoring to keep up with the speed, volume, and complexity of financial crime.

What Is Automated Transaction Monitoring?

Automated transaction monitoring refers to the use of technology systems that continuously review, analyse, and flag suspicious financial activity without manual intervention. These systems apply predefined rules, risk models, and artificial intelligence to detect anomalies in customer behaviour or transaction patterns.

Key functions include:

• Monitoring deposits, withdrawals, and transfers in real time.
• Identifying unusual transactions or activities inconsistent with customer profiles.
• Generating alerts for compliance review and investigation.
• Supporting regulatory reporting such as Suspicious Transaction Reports (STRs).

Automation reduces human error, accelerates detection, and allows banks to focus on genuine threats rather than drowning in false alerts.

Why It Matters in the Philippines

The Philippines’ financial ecosystem faces a unique mix of challenges that make automation essential:

1. High Transaction Volume
   Over USD 36 billion in annual remittance inflows and growing digital payments create massive monitoring workloads.
2. Rise of Instant Payments
   With PESONet and InstaPay enabling near-instant fund transfers, manual monitoring simply cannot keep up.
3. Expanding Fintech Landscape
   E-wallets and payment providers multiply transaction data, increasing the complexity of detection.
4. Regulatory Demands
   The BSP and AMLC expect banks to adopt risk-based, technology-enabled monitoring as part of their AML compliance.
5. Customer Trust
   In a digital-first environment, customers expect their money to be secure. Automated systems build confidence by detecting fraud before it reaches the customer.

How Automated Transaction Monitoring Works

Automation doesn’t just replace human oversight — it amplifies it.

1. Data Collection and Integration

Systems collect data from multiple channels such as deposits, fund transfers, remittances, and mobile payments, consolidating it into a single monitoring platform.

2. Risk Profiling and Segmentation

Each customer is profiled based on transaction behaviour, source of funds, occupation, and geography.

3. Rule-Based and AI Detection

Algorithms compare real-time transactions against expected behaviour and known risk scenarios. For example, frequent small deposits below the reporting threshold may signal structuring.

4. Alert Generation

When anomalies are detected, alerts are automatically generated and prioritised by severity.

5. Investigation and Reporting

Investigators review alerts through built-in case management tools, escalating genuine cases for STR filing.

Benefits of Automated Transaction Monitoring

1. Real-Time Detection

Automated systems identify suspicious transactions the moment they occur, preventing potential losses.

2. Consistency and Accuracy

Automation eliminates inconsistencies and fatigue errors common in manual reviews.

3. Reduced False Positives

Machine learning refines models over time, helping banks focus on real threats.

4. Cost Efficiency

Automation lowers compliance costs by reducing manual workload and investigation time.

5. Auditability and Transparency

Every decision is logged and traceable, simplifying regulatory audits and internal reviews.

6. Scalability

Systems can handle millions of transactions daily, making them ideal for high-volume environments like digital banking and remittances.

Key Money Laundering Typologies Detected by Automation

Automated systems can identify typologies common in Philippine banking, including:

• Remittance Structuring: Splitting large overseas funds into smaller deposits.
• Rapid Inflows and Outflows: Accounts used for layering and quick fund transfers.
• Shell Company Laundering: Transactions through entities with no legitimate operations.
• Trade-Based Laundering: Over- or under-invoicing disguised as trade payments.
• Terror Financing: Repeated low-value transactions directed toward high-risk areas.

Challenges in Implementing Automated Systems

Despite the benefits, deploying automated monitoring in Philippine banks presents challenges:

• Data Quality Issues: Poorly structured or incomplete data leads to false alerts.
• Legacy Core Systems: Many institutions struggle to integrate modern monitoring software with existing infrastructure.
• High Implementation Costs: Smaller rural banks and fintech startups face budget constraints.
• Skills Shortage: Trained AML analysts who can interpret automated outputs are in short supply.
• Evolving Criminal Techniques: Criminals continuously test new methods, requiring constant system updates.

Best Practices for Effective Automation

1. Adopt a Risk-Based Approach
   Tailor monitoring to the risk profiles of customers, products, and geographies.
2. Combine Rules and AI
   Use hybrid models that blend human-defined logic with adaptive machine learning.
3. Ensure Explainability
   Select systems that provide clear explanations for flagged alerts to meet BSP and AMLC standards.
4. Integrate Data Sources
   Unify customer and transaction data across departments for a 360-degree view.
5. Continuous Model Training
   Retrain models regularly with new typologies and real-world feedback.
6. Collaborate Across the Industry
   Engage in federated learning and typology-sharing initiatives to stay ahead of regional threats.

Regulatory Expectations for Automated Monitoring in the Philippines

The BSP and AMLC encourage financial institutions to:

• Implement technology-driven monitoring aligned with AMLA and FATF standards.
• File STRs promptly, ideally through automated reporting workflows.
• Maintain detailed audit logs of all monitoring and investigation activities.
• Demonstrate system effectiveness during compliance reviews.

Institutions that fail to upgrade to automated systems risk regulatory sanctions, reputational damage, and operational inefficiency.

Real-World Example: Detecting Fraud in Real Time

A leading Philippine bank implemented an automated transaction monitoring system integrated with behavioural analytics. Within the first quarter, the bank identified multiple accounts receiving frequent small-value remittances from overseas. Further investigation revealed a money mule network moving funds linked to online fraud.

Automation not only accelerated detection but also improved STR filing timelines by over 40 percent, setting a new benchmark for compliance efficiency.

The Tookitaki Advantage: Next-Generation Automated Monitoring

Tookitaki’s FinCense platform provides Philippine banks with an advanced, automated transaction monitoring framework built for speed, accuracy, and compliance.

Key features include:

• Agentic AI-Powered Detection that evolves with new typologies and regulatory changes.
• Federated Intelligence from the AFC Ecosystem, enabling real-world learning from global experts.
• Smart Disposition Engine that automates investigation summaries and reporting.
• Explainable AI Models ensuring transparency for regulators and auditors.
• False Positive Reduction through dynamic thresholding and behavioural analysis.

By integrating automation with collective intelligence, FinCense transforms compliance from a reactive process into a proactive defence system — one that builds trust, efficiency, and resilience across the financial ecosystem.

Conclusion: Automation as the New Standard for Compliance

The fight against financial crime in the Philippines demands speed, precision, and adaptability. Manual transaction monitoring can no longer keep up with the velocity of modern banking. Automated systems empower institutions to detect suspicious activity instantly, reduce investigation fatigue, and ensure seamless regulatory compliance.

The path forward is clear: automation is not just an upgrade, it is the new standard. Philippine banks that embrace automated transaction monitoring today will set themselves apart tomorrow — not only as compliant institutions but as trusted stewards of financial integrity.

With instant payments now the norm, Australian banks must shift from detecting fraud after it happens to preventing it in real time.

Introduction

The rise of real-time payments has redefined both convenience and risk. Australians now move money within seconds through the New Payments Platform (NPP) and PayTo, but this speed has also created an attractive opportunity for fraudsters.

According to the Australian Competition and Consumer Commission (ACCC), Australians lost over AUD 3 billion to scams in 2024. As fraudsters automate their tactics, the window for banks to identify and stop fraudulent activity has narrowed to just milliseconds.

To combat this, financial institutions need more than just advanced technology — they need real-time fraud prevention frameworks that bring together analytics, automation, and collaboration across systems and stakeholders.

Why Real-Time Fraud Prevention Matters

1. Instant Payments, Instant Risks

With NPP and PayTo, once funds leave an account, recovery becomes extremely difficult. Delayed detection means losses are often irreversible.

2. Fraudsters Are Faster Than Ever

Criminals now deploy bots, deepfakes, and social engineering to initiate high-speed scams. Without real-time systems, even the best-trained teams cannot respond quickly enough.

3. Customer Expectations Have Changed

Today’s customers expect frictionless, always-on protection. Delays in identifying or resolving fraudulent activity damage trust and loyalty.

4. Regulatory Scrutiny Is Increasing

AUSTRAC and the Australian Banking Association (ABA) are pressing institutions to enhance their real-time monitoring and reporting capabilities as part of broader scam-prevention efforts.

Understanding Real-Time Fraud Prevention Frameworks

A real-time fraud prevention framework is an integrated system of technologies, policies, and processes designed to detect, block, and report fraudulent activity as it happens.

Core Components:

1. Data Ingestion Layer: Collects data from core banking, payments, onboarding, and digital channels.
2. Real-Time Analytics Engine: Analyses transactions and behavioural data instantly to detect anomalies.
3. Decisioning Layer: Applies AI models and rules to determine whether a transaction should proceed, pause, or be reviewed.
4. Alert and Case Management: Routes flagged activity to investigators with all context attached.
5. Regulatory Reporting and Audit Trails: Generates AUSTRAC-ready reports and maintains full transparency.

The goal is simple: prevent fraud without slowing down legitimate transactions.

Fraud Trends Driving the Shift to Real-Time Prevention

1. Authorised Push Payment (APP) Scams

Victims are deceived into transferring money to fraudsters. Once sent, the funds move across multiple mule accounts in seconds.

2. Account Takeover (ATO) Fraud

Attackers gain access to legitimate customer accounts through phishing or credential theft, initiating unauthorised transfers.

3. Synthetic Identity Fraud

Fraudsters create fake identities by blending real and fabricated data, opening accounts that appear legitimate until exploited.

4. Money Mule Networks

Criminals use layers of recruited individuals or compromised accounts to launder stolen funds.

5. Insider Fraud

Employees or third parties misuse internal access for unauthorised activities.

Each of these threats requires immediate detection, not batch-based monitoring.

AUSTRAC’s Perspective on Real-Time Monitoring

AUSTRAC’s guidance under the AML/CTF Act 2006 emphasises:

• Continuous monitoring of transactions.
• Early detection of suspicious behaviour.
• Prompt filing of Suspicious Matter Reports (SMRs).
• Risk-based allocation of resources.
• Ongoing staff training and technology upgrades.

The regulator expects institutions to demonstrate that their systems are capable of identifying and responding to threats dynamically — a hallmark of a strong real-time framework.

Key Elements of an Effective Real-Time Fraud Prevention Framework

1. Unified Data Architecture

Bring together data from transaction monitoring, KYC, onboarding, and fraud systems. This creates a holistic risk view and eliminates blind spots.

2. AI and Machine Learning

AI models identify emerging typologies by analysing patterns across large data volumes, enabling detection of unknown threats.

3. Behavioural Biometrics

Analysing keystrokes, mouse movements, or mobile usage patterns helps differentiate genuine users from fraudsters.

4. Network Analytics

Map relationships between accounts, devices, and transactions to expose mule clusters or coordinated fraud rings.

5. Cross-Channel Monitoring

Link activity across payments, cards, remittances, and digital platforms to prevent fraud migration between systems.

6. Automated Case Management

Real-time frameworks rely on automation to triage and prioritise alerts, ensuring investigators focus on genuine threats.

7. Continuous Model Calibration

Regular validation ensures AI models remain accurate, fair, and compliant with AUSTRAC and global regulatory standards.

Operationalising the Framework

Step 1: Assess Existing Infrastructure

Evaluate current systems for latency, coverage gaps, and data silos.

Step 2: Integrate Data Sources

Unify KYC, transaction, and fraud data through APIs and cloud infrastructure for faster decisioning.

Step 3: Implement Real-Time Detection Models

Deploy AI-driven engines that monitor all transactions at sub-second speed.

Step 4: Automate Reporting and Audit

Ensure every flagged transaction generates an audit trail and is ready for AUSTRAC reporting.

Step 5: Collaborate Externally

Join industry initiatives such as the Fintel Alliance or AFC Ecosystem for shared intelligence on emerging threats.

Step 6: Educate Customers

Run campaigns explaining scam tactics and prevention steps to reduce victim vulnerability.

Common Implementation Challenges

• Data Fragmentation: Disparate systems delay decision-making.
• Alert Overload: Poorly tuned models create excessive false positives.
• Legacy Systems: Older platforms cannot support real-time throughput.
• Model Explainability: Regulators demand transparency into AI decisions.
• Integration Costs: Connecting fraud, AML, and onboarding tools can be complex.

Modern compliance platforms address these gaps through automation, modular deployment, and explainable AI.

Case Example: Regional Australia Bank

Regional Australia Bank, a community-owned institution, has demonstrated how even mid-sized banks can adopt real-time frameworks effectively. By leveraging advanced analytics and customer behavioural insights, the bank has improved fraud detection speed and accuracy while maintaining seamless customer experiences.

This example underscores that real-time fraud prevention is not about size — it is about adopting the right technology and culture of vigilance.

Spotlight: Tookitaki’s FinCense

FinCense, Tookitaki’s next-generation compliance platform, empowers Australian banks to build true real-time fraud prevention frameworks.

• Real-Time Monitoring: Detects fraudulent transactions instantly across NPP, PayTo, cards, and remittances.
• Agentic AI: Continuously learns from evolving fraud typologies, adapting in real time.
• Federated Intelligence: Shares anonymised insights through the AFC Ecosystem to detect coordinated fraud patterns.
• FinMate AI Copilot: Assists investigators by summarising cases and highlighting root causes instantly.
• Unified AML-Fraud Architecture: Provides a single platform covering transaction monitoring, screening, and case management.
• AUSTRAC-Ready Reporting: Automates compliance submissions with full transparency and traceability.

FinCense bridges the gap between compliance and fraud operations, giving banks real-time intelligence with explainability and control.

Best Practices for Australian Banks

1. Adopt a Holistic Approach: Unify AML, fraud, and cybersecurity functions for full-spectrum protection.
2. Leverage Explainable AI: Regulators expect transparency in automated decisions.
3. Participate in Industry Collaboration: Share intelligence securely to uncover cross-institutional threats.
4. Maintain Continuous Testing: Regularly validate detection models to prevent drift.
5. Invest in Staff Upskilling: Equip compliance teams with data and AI literacy.
6. Balance Security with Experience: Ensure controls do not compromise customer convenience.

The Future of Real-Time Fraud Prevention

1. Predictive Fraud Detection: AI will forecast risk before transactions occur.
2. Federated Learning Networks: Banks will collaborate to train AI models without sharing raw data.
3. Digital Identity Integration: Linking biometric identity to payment authorisation will reduce impersonation fraud.
4. Agentic AI Investigators: AI copilots like FinMate will automate case triage and narrative generation.
5. Real-Time Collaboration with Regulators: AUSTRAC will increasingly use live data feeds for proactive oversight.

Conclusion

Real-time fraud prevention is no longer optional — it is the foundation of customer trust and regulatory resilience in Australia’s instant payments landscape.

Banks that modernise their frameworks can protect both their customers and reputation while ensuring compliance with AUSTRAC’s evolving standards. Regional Australia Bank stands as an example of how innovation and community trust can coexist through proactive fraud prevention.

With solutions like Tookitaki’s FinCense, institutions can build intelligent, adaptable frameworks that detect and block fraud before it happens — safeguarding Australia’s financial ecosystem for the digital era.

Pro tip: The faster the payments, the smarter the prevention needs to be. Real-time fraud prevention is not just a technology upgrade; it is a strategic imperative.