As financial crime grows more complex, Australian banks are turning to AI and now Agentic AI to revolutionise how transactions are monitored and risks detected.

Introduction

Australia’s financial landscape is evolving fast. The growth of real-time payments, digital banking, and cross-border transactions has made detecting financial crime more challenging than ever. Traditional rule-based transaction monitoring systems, designed for slower and simpler payment environments, are no longer enough.

In response, Australian banks are increasingly adopting artificial intelligence (AI) to enhance the accuracy, speed, and adaptability of their AML programs. But the latest evolution, Agentic AI, is taking compliance to an entirely new level.

This blog explores how AI, and particularly Agentic AI, is transforming transaction monitoring across Australia’s banking sector, enabling faster detection, smarter investigations, and stronger regulatory alignment with AUSTRAC.

Why Transaction Monitoring Needs a New Approach

1. The Rise of Real-Time Payments

With the New Payments Platform (NPP) and PayTo, transactions clear in seconds. Fraudsters and launderers exploit this speed to move funds through multiple mule accounts before banks can react.

2. Sophisticated Criminal Tactics

Financial crime is no longer limited to simple structuring. Criminals use synthetic identities, cross-border layering, and digital assets to evade detection.

3. High False Positives

Rule-based systems trigger thousands of unnecessary alerts, overwhelming compliance teams and increasing costs.

4. AUSTRAC’s Evolving Standards

AUSTRAC expects continuous monitoring, explainability, and proactive detection. Banks must show they can identify suspicious activity before it spreads across the financial system.

5. Customer Experience Pressures

Delays or false flags impact legitimate customers. AI enables banks to balance security and service quality.

The Limitations of Traditional Monitoring

For years, transaction monitoring relied on static rules and thresholds — for example, flagging transactions over AUD 10,000 or rapid transfers to high-risk countries. While these methods catch known risks, they fail against sophisticated or adaptive schemes.

Limitations include:

• Static logic: Can’t detect new or subtle behaviours.
• Manual reviews: Investigators waste time on low-risk alerts.
• No learning loop: Systems don’t improve automatically over time.
• Fragmented data: Disconnected systems hinder visibility across channels.

In today’s fast-moving financial environment, static systems have become reactive rather than preventive.

How AI Transforms Transaction Monitoring

AI reshapes monitoring from a reactive process into a proactive intelligence system that continuously learns from data.

1. Machine Learning for Pattern Recognition

AI models analyse historical and real-time data to detect patterns that indicate suspicious activity — such as unusual fund flows, velocity changes, or repeated interactions with high-risk entities.

2. Behavioural Analytics

AI builds detailed customer profiles and detects deviations from normal behaviour, flagging potential risks that traditional systems miss.

3. Adaptive Thresholding

Instead of fixed thresholds, AI dynamically adjusts alert sensitivity based on risk context, reducing false positives.

4. Entity Resolution

AI connects fragmented data to identify relationships between customers, accounts, and devices — crucial for uncovering complex laundering networks.

5. Natural Language Processing (NLP)

AI interprets transaction narratives, case notes, and free-text fields, identifying hidden clues like invoice mismatches or unusual descriptions.

6. Continuous Learning

Every investigation outcome feeds back into the model, improving detection accuracy over time.

Agentic AI: The Next Frontier in Compliance

Agentic AI goes beyond traditional AI by combining autonomy, reasoning, and collaboration. Instead of just executing pre-trained models, Agentic AI acts as an intelligent assistant that can:

• Analyse transactions and contextual data.
• Generate risk summaries in natural language.
• Recommend actions based on regulatory frameworks.
• Learn from investigator feedback to improve continuously.

In compliance, this means faster decisions, fewer manual errors, and higher operational efficiency.

How Agentic AI Works in Transaction Monitoring

1. Data Ingestion and Contextual Understanding

Agentic AI continuously consumes structured (transactions, KYC) and unstructured (case notes, communications) data to form a full risk picture.

2. Dynamic Risk Scoring

It assigns real-time risk scores to each transaction, considering behavioural patterns, customer history, and contextual anomalies.

3. Intelligent Narration

When a transaction is flagged, Agentic AI can summarise the alert — describing what happened, why it matters, and what actions are recommended — in clear, regulator-friendly language.

4. Self-Learning Capabilities

Each closed case improves its reasoning. Over time, the system develops institutional knowledge, adapting to new typologies without reprogramming.

5. Investigator Collaboration

Acting as a compliance copilot, Agentic AI assists investigators in triaging alerts, finding linked accounts, and preparing Suspicious Matter Reports (SMRs).

Benefits of AI and Agentic AI for Australian Banks

1. Significant False Positive Reduction: AI models prioritise relevant alerts, cutting investigation workload by up to 90 percent.
2. Improved Accuracy: Continuous learning enhances detection of new typologies.
3. Faster Investigations: Agentic AI copilots summarise and contextualise alerts in seconds.
4. Regulatory Confidence: Explainable AI ensures transparency and auditability for AUSTRAC.
5. Enhanced Customer Trust: Real-time, intelligent monitoring prevents fraud without disrupting legitimate transactions.
6. Operational Efficiency: Reduced manual workload lowers compliance costs.

AUSTRAC’s View on AI in Compliance

AUSTRAC has encouraged innovation in RegTech and SupTech solutions that enhance financial integrity. Under the AML/CTF Act, AI-powered systems are acceptable if they:

• Maintain auditability and explainability.
• Apply risk-based controls.
• Support timely and accurate reporting.
• Are regularly validated and reviewed for bias and accuracy.

AUSTRAC’s collaboration with technology providers reflects a growing recognition that AI is essential to managing modern financial crime risks.

Case Example: Regional Australia Bank

Regional Australia Bank, a community-owned institution, has embraced AI-driven compliance to enhance its transaction monitoring capabilities. By leveraging intelligent analytics, the bank has reduced investigation time, improved accuracy, and strengthened its reporting processes — all while maintaining customer trust and transparency.

Its experience demonstrates that AI adoption is not limited to large institutions; even mid-sized banks can lead in compliance innovation.

Spotlight: Tookitaki’s FinCense and Agentic AI

FinCense, Tookitaki’s flagship compliance platform, integrates Agentic AI to redefine transaction monitoring for Australian banks.

• Real-Time Monitoring: Analyses millions of transactions across NPP, PayTo, and international payments instantly.
• Agentic AI Copilot (FinMate): Assists investigators by narrating alerts, identifying linked parties, and generating regulatory summaries.
• Federated Intelligence: Utilises anonymised typologies contributed by the AFC Ecosystem to detect new risks collaboratively.
• Explainable AI: Ensures every model decision is transparent, auditable, and regulator-ready.
• End-to-End Case Management: Combines fraud, AML, and sanctions monitoring into a unified workflow.
• AUSTRAC Alignment: Automates SMRs, TTRs, and IFTIs with full compliance assurance.

With Agentic AI at its core, FinCense transforms transaction monitoring from a static process into an intelligent, adaptive system that anticipates risk before it happens.

Implementing AI-Driven Monitoring: Best Practices

1. Start with Clean Data: High-quality data ensures reliable model performance.
2. Adopt Explainable Models: Regulators prioritise transparency in AI decision-making.
3. Integrate AML and Fraud Operations: Unified systems enhance efficiency.
4. Invest in Investigator Training: Equip teams to work alongside AI tools effectively.
5. Validate Models Regularly: Continuous testing maintains fairness and accuracy.
6. Collaborate through Federated Intelligence: Shared insights strengthen detection across institutions.

Future of Transaction Monitoring in Australia

1. Predictive Compliance: Systems will forecast risks and block suspicious transactions before they occur.
2. Hyper-Personalised Risk Scoring: AI will assess risk at the individual customer level in real time.
3. Industry-Wide Collaboration: Federated learning will connect banks for collective intelligence.
4. Agentic AI Investigators: Autonomous copilots will handle tier-one alerts end to end.
5. RegTech-Regulator Integration: AUSTRAC will increasingly rely on direct system data feeds for oversight.

Conclusion

The future of transaction monitoring in Australia lies in intelligence, not volume.
AI enables banks to uncover complex, hidden risks that traditional systems miss, while Agentic AI brings a new level of automation, reasoning, and transparency to compliance operations.

Regional Australia Bank shows that innovation is achievable at any scale. With Tookitaki’s FinCense and its built-in Agentic AI, Australian banks can move beyond reactive monitoring to real-time, proactive financial crime prevention — strengthening both compliance and customer trust.

Pro tip: The smartest transaction monitoring systems don’t just detect suspicious activity; they understand it, explain it, and learn from it.

Money laundering is evolving. So is the technology built to stop it.

In Singapore, a financial hub with deep global links, criminals are using more advanced techniques to disguise illicit funds. From cross-border shell firms to digital platform abuse and real-time payment layering, the tactics are getting smarter. That’s why financial institutions are turning to next-generation money laundering technology — solutions that use AI, behavioural analytics, and collaborative intelligence to detect and disrupt suspicious activity before it causes damage.

This blog explores the key technologies powering AML efforts in Singapore, the gaps that still exist, and how institutions are building faster, smarter defences against financial crime.

What Is Money Laundering Technology?

Money laundering technology refers to systems and tools designed to detect, investigate, and report suspicious financial activities that may involve the movement of illicit funds. These technologies go beyond basic rules engines or static filters. They are intelligent, adaptive, and often integrated with broader compliance ecosystems.

A typical tech stack may include:

• Real-time transaction monitoring platforms
• Customer due diligence and risk scoring engines
• AI-powered anomaly detection
• Sanctions and PEP screening tools
• Suspicious transaction reporting (STR) modules
• Investigation workflows and audit trails
• Federated learning and typology sharing systems

Why Singapore Needs Advanced Money Laundering Technology

Singapore’s position as a regional financial centre attracts legitimate business and bad actors alike. In response, the Monetary Authority of Singapore (MAS) has built one of the most stringent AML regimes in the region. But regulations alone are not enough.

Current challenges include:

• High-speed transactions via PayNow and FAST with little room for intervention
• Cross-border trade misinvoicing and shell firm layering
• Recruitment of money mules through scam job ads and phishing sites
• Laundering of fraud proceeds through remittance and fintech apps
• Growing sophistication in synthetic identities and deepfake impersonations

To address these, institutions need tech that is not only MAS-compliant but agile, explainable, and intelligence-driven.

The Technology Stack That Drives Modern AML Programs

Here are the core components of money laundering technology as used by leading institutions in Singapore.

1. Real-Time Transaction Monitoring Systems

These systems monitor financial activity across banking channels and flag suspicious behaviour as it happens. They detect:

• Unusual transaction volumes
• Sudden changes in customer behaviour
• Transactions involving high-risk jurisdictions
• Structuring or smurfing patterns

Advanced platforms use streaming data and in-memory analytics to process large volumes instantly.

2. Behavioural Analytics Engines

Instead of relying solely on thresholds, behavioural analytics builds a baseline for each customer’s typical activity. Alerts are raised when transactions deviate from established norms.

This is crucial for:

• Spotting insider fraud
• Detecting ATO (account takeover) attempts
• Identifying use of dormant or inactive accounts for money movement

3. AI and Machine Learning Models

AI transforms detection by finding patterns too complex for humans or rules to catch. It adapts over time to recognise new laundering behaviours.

Use cases include:

• Clustering similar fraud cases to spot mule networks
• Predicting escalation likelihood of flagged alerts
• Prioritising alerts based on risk and urgency
• Generating contextual narratives for STRs

4. Typology-Based Scenario Detection

A strong AML system includes real-world typologies. These are predefined scenarios that mirror how money laundering actually happens in the wild.

Examples relevant to Singapore:

• Layering through multiple fintech wallets
• Use of nominee directors and shell companies in trade deals
• Fraudulent remittance transactions disguised as payroll or aid
• Utility payment platforms used for pass-through layering

These models help institutions move from rule-based detection to scenario-based insight.

5. Investigation Platforms with Smart Disposition Tools

Once an alert is triggered, investigators need tools to:

• View full customer profiles and transaction history
• Access relevant typology data
• Log decisions and attach supporting documents
• Generate STRs quickly and consistently

Smart disposition engines recommend next steps and help analysts close cases faster.

6. Sanctions and Watchlist Screening

Technology must screen customers and transactions against global and local watchlists:

• UN, OFAC, EU, and MAS sanctions
• PEP lists and high-risk individuals
• Adverse media databases

Advanced platforms support fuzzy matching, multilingual aliases, and real-time updates to reduce risk and manual effort.

7. GoAML-Compatible STR Filing Modules

In Singapore, all suspicious transaction reports must be filed through the GoAML system. The right technology will:

• Populate STRs with investigation data
• Include attached evidence
• Support internal approval workflows
• Ensure audit-ready submission logs

This reduces submission time and improves reporting quality.

8. Federated Learning and Community Intelligence

Leading platforms now allow financial institutions to share risk scenarios and typologies without exposing customer data. This collaborative approach improves detection and keeps systems updated against evolving regional risks.

Tookitaki’s AFC Ecosystem is one such example — connecting banks across Asia to share anonymised typologies, red flags, and fraud patterns.

What’s Still Missing in Most Money Laundering Tech Setups

Despite having systems in place, many organisations still struggle with:

❌ Alert Fatigue

Too many false positives clog up resources and delay action on real risks.

❌ Fragmented Systems

AML tools that don’t integrate well create data silos and limit insight.

❌ Inflexible Rules

Static thresholds can’t keep up with fast-changing laundering techniques.

❌ Manual STR Workflows

Investigators still spend hours manually compiling reports.

❌ Weak Localisation

Some systems lack support for typologies and threats specific to Southeast Asia.

These gaps increase operational costs, frustrate teams, and put institutions at risk during audits or inspections.

How Tookitaki’s FinCense Leads the Way in Money Laundering Technology

FinCense by Tookitaki is a next-generation AML platform designed specifically for the Asia-Pacific region. It combines AI, community intelligence, and explainable automation into one modular platform.

Here’s what makes it stand out in Singapore:

1. Agentic AI Framework

FinCense uses specialised AI agents for each part of the AML lifecycle — detection, investigation, reporting, and more. Each module is lightweight, scalable, and independently optimised.

2. Scenario-Based Detection with AFC Ecosystem Integration

FinCense detects using expert-curated typologies contributed by the AFC community. These include:

• Shell firm layering
• QR code-enabled laundering
• Investment scam fund flows
• Deepfake-enabled CEO fraud

This keeps detection models locally relevant and constantly refreshed.

3. FinMate: AI Copilot for Investigations

FinMate helps analysts by:

• Surfacing key transactions
• Linking related alerts
• Suggesting likely typologies
• Auto-generating STR summaries

This dramatically reduces investigation time and improves STR quality.

4. Simulation and Threshold Tuning

Before deploying a new detection rule or scenario, FinCense lets compliance teams simulate impact, test alert volumes, and adjust sensitivity for better control.

5. MAS-Ready Compliance and Audit Logs

Every alert, investigation step, and STR submission is fully logged and traceable — helping banks stay prepared for MAS audits and risk assessments.

Case Results: What Singapore Institutions Are Achieving with FinCense

Financial institutions using FinCense report:

• 60 to 70 percent reduction in false positives
• 3x faster average investigation closure time
• Stronger alignment with MAS expectations
• Higher STR accuracy and submission rates
• Improved team morale and reduced compliance fatigue

By combining smart detection with smarter investigation, FinCense improves every part of the AML workflow.

Checklist: Is Your AML Technology Where It Needs to Be?

Ask your team:

• Can your system detect typologies unique to Southeast Asia?
• How many alerts are false positives?
• Can you trace every step of an investigation for audit?
• How long does it take to file an STR?
• Are your detection thresholds adaptive or fixed?
• Is your technology continuously learning and improving?

If your answers raise concerns, it may be time to evaluate a more advanced solution.

Conclusion: Technology Is Now the Strongest Line of Defence

The fight against money laundering has reached a tipping point. Old systems and slow processes can no longer keep up with the scale and speed of financial crime.

In Singapore, where regulatory standards are high and criminal tactics are sophisticated, the need for intelligent, integrated, and locally relevant technology is greater than ever.

Tookitaki’s FinCense shows what money laundering technology should look like in 2025 — agile, explainable, scenario-driven, and backed by community intelligence.

The future of AML is not just about compliance. It’s about building trust, protecting reputation, and staying one step ahead of those who exploit the financial system.

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.