Fraudsters think fast, but machine learning thinks faster.

Malaysia’s Growing Fraud Challenge

Fraud has become one of the biggest threats facing Malaysia’s banking sector. The rise of instant payments, QR codes, and cross-border remittances has created new opportunities for consumers — and for criminals.

Money mule networks are expanding, account takeover fraud is becoming more common, and investment scams continue to claim victims across the country. Bank Negara Malaysia (BNM) has increased its scrutiny, aligning the country more closely with global standards set by the Financial Action Task Force (FATF).

In this climate, banks need smarter systems. Traditional fraud detection methods are no longer enough. To stay ahead, Malaysian banks are turning to fraud detection using machine learning as their next line of defence.

Why Traditional Fraud Detection Falls Short

For decades, banks relied on rule-based fraud detection systems. These systems flag suspicious activity based on pre-defined rules, such as:

• Transactions above a certain amount
• Transfers to high-risk jurisdictions
• Multiple failed login attempts

While useful, rule-based systems have clear limitations:

• They are static: Criminals quickly learn how to work around rules.
• They create false positives: Too many legitimate transactions are flagged, overwhelming compliance teams.
• They are reactive: Rules are only updated after a new fraud pattern is discovered.
• They lack adaptability: In a fast-changing environment, rigid systems cannot keep pace.

The result is compliance fatigue, higher costs, and gaps that criminals exploit.

How Machine Learning Transforms Fraud Detection

Machine learning (ML) changes the game by allowing systems to learn from data and adapt over time. Instead of relying on static rules, ML models identify patterns and anomalies that may signal fraud.

How ML Works in Banking Fraud Detection

1. Data Collection
   ML models analyse vast amounts of data, including transaction history, customer behaviour, device information, and geolocation.
2. Feature Engineering
   Key attributes are extracted, such as transaction frequency, average values, and unusual login behaviour.
3. Model Training
   Algorithms are trained on historical data, distinguishing between legitimate and fraudulent activity.
4. Real-Time Detection
   As transactions occur, ML models assign risk scores and flag suspicious cases instantly.
5. Continuous Learning
   Models evolve by incorporating feedback from confirmed fraud cases, improving accuracy over time.

Supervised vs Unsupervised Learning

• Supervised learning: Models are trained using labelled data (fraud vs non-fraud).
• Unsupervised learning: Models identify unusual patterns without prior labelling, useful for detecting new fraud types.

This adaptability is critical in Malaysia, where fraud typologies evolve quickly.

Key Benefits of Fraud Detection Using Machine Learning

The advantages of ML-driven fraud detection are clear:

1. Real-Time Detection

Transactions are analysed instantly, allowing banks to stop fraud before funds are withdrawn or transferred abroad.

2. Adaptive Learning

ML models continuously improve, detecting new scam typologies that rules alone would miss.

3. Improved Accuracy

By reducing false positives, banks save time and resources while improving customer experience.

4. Scalability

Machine learning can handle millions of transactions daily, essential in a high-volume market like Malaysia.

5. Holistic View of Risk

ML integrates multiple data points to create a comprehensive risk profile, spotting complex fraud networks.

Fraud Detection in Malaysia’s Banking Sector

Malaysia faces unique pressures that make ML adoption urgent:

• Instant payments and QR adoption: DuitNow QR has become a national standard, but speed increases vulnerability.
• Cross-border laundering risks: Remittance corridors expose banks to international mule networks.
• Sophisticated scams: Criminals are using social engineering and even deepfakes to deceive customers.
• BNM expectations: Regulators want financial institutions to adopt proactive, risk-based monitoring.

In short, fraud detection using machine learning is no longer optional. It is a strategic necessity for Malaysia’s banks.

Step-by-Step: How Banks Can Implement ML-Driven Fraud Detection

For Malaysian banks considering machine learning adoption, the path is practical and achievable:

Step 1: Define the Risk Landscape

Identify the most pressing fraud threats, such as mule accounts, phishing, or account takeover, and align with BNM priorities.

Step 2: Integrate Data Sources

Consolidate transaction, customer, device, and behavioural data into a single framework. ML models thrive on diverse datasets.

Step 3: Deploy Machine Learning Models

Use supervised models for known fraud patterns and unsupervised models for detecting new anomalies.

Step 4: Create Feedback Loops

Feed confirmed fraud cases back into the system to improve accuracy and reduce false positives.

Step 5: Ensure Explainability

Adopt systems that provide clear reasons for alerts. Regulators must understand how decisions are made.

Tookitaki’s FinCense: Machine Learning in Action

This is where Tookitaki’s FinCense makes a difference. Built as the trust layer to fight financial crime, FinCense is an advanced compliance platform powered by AI and machine learning.

Agentic AI Workflows

FinCense uses intelligent AI agents that automate alert triage, generate investigation narratives, and recommend next steps. Compliance teams save hours on each case.

Federated Learning with the AFC Ecosystem

Through the AFC Ecosystem, FinCense benefits from shared intelligence contributed by hundreds of institutions. Malaysian banks gain early visibility into fraud typologies emerging in ASEAN.

Explainable AI

Unlike black-box systems, FinCense provides full transparency. Every flagged transaction includes a clear rationale, making regulator engagement smoother.

End-to-End Fraud and AML Integration

FinCense unifies fraud detection and AML monitoring, offering a single view of risk. This reduces duplication and strengthens overall defences.

ASEAN Market Fit

Scenarios and typologies are tailored to Malaysia’s realities, from QR code misuse to remittance layering.

Scenario Walkthrough: Account Takeover Fraud

Imagine a Malaysian customer’s online banking credentials are stolen through phishing. Fraudsters attempt multiple transfers to mule accounts.

With traditional systems:

• The activity may only be flagged after large sums are lost.
• Manual review delays the response.

With FinCense’s ML-powered detection:

• Unusual login behaviour is flagged immediately.
• Transaction velocity analysis highlights the abnormal transfers.
• Federated learning recognises the mule pattern from other ASEAN cases.
• Agentic AI prioritises the alert, generates a narrative, and recommends blocking the transaction.

Result: The fraud attempt is stopped before funds leave the bank.

Impact on Banks and Customers

The benefits of fraud detection using machine learning extend across the ecosystem:

• Banks reduce fraud losses and compliance costs.
• Customers gain confidence in digital banking, encouraging adoption.
• Regulators see stronger risk management and timely reporting.
• The economy benefits from increased trust in financial services.

The Road Ahead for ML in Fraud Detection

Looking forward, machine learning will play an even larger role in banking fraud prevention:

• Integration with open banking data will provide richer insights.
• AI-powered scams will push banks to deploy equally intelligent defences.
• Collaboration across borders will become critical, especially in ASEAN.
• Hybrid AI-human models will balance efficiency with oversight.

Malaysia has the chance to position itself as a regional leader in adopting ML for financial crime prevention.

Conclusion

Fraud detection using machine learning in banking is no longer a futuristic concept. It is the practical, powerful response Malaysia’s banks need today. Traditional rule-based systems cannot keep up with evolving scams, instant payments, and cross-border laundering risks.

With Tookitaki’s FinCense, Malaysian banks gain an industry-leading trust layer that combines machine learning, explainability, and regional intelligence. The future of fraud prevention is here, and it starts with embracing smarter, adaptive technology.

Federated learning is transforming AML by enabling banks to share intelligence without sharing sensitive data.

Introduction

Financial crime is becoming more sophisticated every year. In Australia, criminals exploit the New Payments Platform (NPP), cross-border corridors, and emerging technologies to launder billions of dollars. Banks and fintechs are under immense pressure from AUSTRAC to detect and report suspicious activity in real time.

Yet no single institution has the complete picture. Criminals spread activity across multiple banks and channels, making it difficult to detect patterns when working in isolation. This is where federated learning in AML comes in. It allows institutions to collaborate on intelligence without exposing customer data, creating a collective shield against money laundering.

What is Federated Learning in AML?

Federated learning is an artificial intelligence technique where multiple parties train a shared model without sharing their raw data. Each institution trains the model locally, and only the model updates — not the underlying data — are shared.

In AML, this means:

• Banks contribute insights into suspicious patterns.
• Sensitive customer data remains within each institution.
• A shared model learns from multiple perspectives, strengthening detection.

It is compliance collaboration without compromising privacy.

Why Australia Needs Federated Learning

1. Fragmented Data

Each bank only sees part of the financial ecosystem. Criminals exploit these gaps by spreading transactions across multiple institutions.

2. Rising Compliance Costs

Institutions are spending billions annually on AML compliance. Shared learning reduces duplication of effort.

3. AUSTRAC’s Push for Innovation

AUSTRAC encourages industry collaboration to strengthen financial crime prevention. Federated learning aligns perfectly with this goal.

4. Real-Time Payment Risks

With NPP and PayTo, money moves instantly. Federated learning enables faster identification of emerging fraud typologies.

5. Protecting Privacy

Australia’s data protection regulations make raw data sharing complex. Federated learning solves this by keeping sensitive data local.

How Federated Learning Works in AML

1. Local Training
   Each institution trains an AI model on its transaction and customer data.
2. Model Updates Shared
   Only the learned patterns (model weights) are sent to a central aggregator.
3. Global Model Improved
   The aggregator combines updates from all banks into a stronger model.
4. Distribution Back to Banks
   The improved model is sent back to each bank for use in detection.

This cycle repeats, continually improving AML detection across the industry.

Use Cases of Federated Learning in AML

1. Mule Account Detection
   Identifies networks of mule accounts across different banks.
2. Cross-Border Laundering
   Tracks layering activity spread across institutions and jurisdictions.
3. Fraud Typology Sharing
   Allows banks to learn from each other’s fraud cases without sharing customer data.
4. Sanctions Screening Enhancement
   Improves detection of high-risk entities that use aliases or complex networks.
5. Customer Risk Profiling
   Builds more accurate risk scores by learning from industry-wide patterns.

Benefits of Federated Learning in AML

• Collective Intelligence: Stronger models built from multiple perspectives.
• Privacy Protection: Raw customer data never leaves the institution.
• Faster Adaptation: New fraud typologies shared quickly across banks.
• Cost Efficiency: Reduces duplication of AML technology spend.
• Regulatory Alignment: Demonstrates proactive industry collaboration.

Challenges of Federated Learning

• Data Quality: Poor-quality local data reduces model accuracy.
• Technical Complexity: Requires strong IT infrastructure for secure collaboration.
• Coordination Barriers: Banks must align on frameworks and standards.
• Explainability: AI models must remain transparent for AUSTRAC compliance.
• Adoption Costs: Initial investment can be high for smaller institutions.

Case Example: Community-Owned Banks Driving Innovation

Community-owned banks like Regional Australia Bank and Beyond Bank are early adopters of collaborative compliance models. By leveraging advanced platforms, they can access federated intelligence that strengthens their detection capabilities without requiring massive in-house teams.

Their success shows that federated learning is not only for Tier-1 institutions. Smaller banks can benefit just as much from this collaborative approach.

Spotlight: Tookitaki’s AFC Ecosystem and FinCense

Tookitaki has pioneered federated learning in AML through its AFC Ecosystem and FinCense platform.

• AFC Ecosystem: A global community of compliance experts contributing real-world scenarios and typologies.
• Federated Learning Engine: Allows banks to benefit from collective intelligence without sharing raw data.
• Real-Time Monitoring: Detects suspicious activity across NPP, PayTo, remittance corridors, and crypto.
• FinMate AI Copilot: Assists investigators with summarised alerts and regulator-ready reports.
• AUSTRAC-Ready: Generates SMRs, TTRs, and IFTIs with full audit trails.
• Cross-Channel Coverage: Unifies detection across banking, wallets, cards, remittances, and crypto.

By combining federated learning with Agentic AI, FinCense delivers industry-leading AML capabilities tailored for the Australian market.

Best Practices for Adopting Federated Learning in AML

1. Start with Partnerships: Collaborate with trusted peers to test federated models.
2. Focus on Data Quality: Ensure local models are trained on clean, structured data.
3. Adopt Explainable AI: Maintain regulator confidence by making outputs transparent.
4. Engage Regulators Early: Keep AUSTRAC informed of federated learning initiatives.
5. Invest in Infrastructure: Secure, scalable platforms are essential for success.

The Future of Federated Learning in AML

1. Industry-Wide Collaboration: More banks will join federated networks to share intelligence.
2. Real-Time Typology Sharing: Federated systems will distribute new fraud scenarios instantly.
3. Cross-Sector Expansion: Insurers, payment firms, and fintechs will join federated AML networks.
4. Global Interoperability: Federated learning models will connect across borders.
5. AI-First Investigations: AI copilots will use federated intelligence to guide case investigations.

Conclusion

Federated learning in AML represents a breakthrough in the fight against financial crime. By combining intelligence from multiple banks without exposing customer data, it creates a collective defence that criminals cannot easily evade.

In Australia, where AUSTRAC demands stronger monitoring and fraudsters exploit instant payments, federated learning provides a powerful solution. Community-owned banks like Regional Australia Bank and Beyond Bank demonstrate that collaboration is possible for institutions of all sizes.

Platforms like Tookitaki’s FinCense are making federated learning a reality, turning compliance from a siloed burden into a shared advantage.

Pro tip: The future of AML will be built on collaboration. Federated learning is the foundation that makes industry-wide intelligence sharing possible.

In the fight against financial crime, detection is only half the battle. The real work starts with the investigation.

Singapore’s financial institutions are facing unprecedented scrutiny when it comes to anti-money laundering (AML) compliance. As regulators raise the bar and criminals get smarter, the ability to investigate suspicious transactions swiftly and accurately is now a non-negotiable requirement. This is where AML investigation software plays a critical role.

In this blog, we explore why AML investigation software matters more than ever in Singapore, what features banks should look for, and how next-generation tools are transforming compliance teams from reactive units into proactive intelligence hubs.

Why Investigation Capabilities Matter in AML Compliance

When a transaction monitoring system flags an alert, it kicks off an entire chain of actions. Analysts must determine whether it's a false positive or a genuine case of money laundering. This requires gathering context, cross-referencing multiple systems, documenting findings, and preparing reports for auditors or regulators.

Doing all of this manually is not only time-consuming, but also increases the risk of human error and compliance gaps. For banks operating in Singapore's high-stakes environment, where MAS expects prompt and well-documented responses, this is a risk few can afford.

Key Challenges Faced by AML Investigators in Singapore

1. Alert Overload

Analysts are often overwhelmed by a high volume of alerts, many of which turn out to be false positives. This slows down investigations and increases backlogs.

2. Fragmented Data Sources

Information needed for a single investigation is typically spread across customer databases, transaction logs, sanctions lists, and case notes, making it difficult to form a complete picture quickly.

3. Manual Documentation

Writing investigation summaries and preparing Suspicious Transaction Reports (STRs) can take hours, reducing the time available for deeper analysis.

4. Audit and Regulatory Pressure

MAS and other regulators expect detailed, traceable justifications for every action taken. Missing documentation or inconsistent processes can lead to penalties.

What AML Investigation Software Does

AML investigation software is designed to streamline, standardise, and enhance the process of investigating suspicious activities. It bridges the gap between alert and action.

Core Functions Include:

• Case creation and automated alert ingestion
• Intelligent data aggregation from multiple systems
• Risk scoring and prioritisation
• Investigation checklists and audit trails
• Natural language summaries for STR filing
• Collaborative case review and escalation tools

Must-Have Features in AML Investigation Software

When evaluating solutions, Singaporean banks should look for these critical capabilities:

1. Smart Alert Triage

The system should help investigators prioritise high-risk alerts by assigning risk scores based on factors such as transaction patterns, customer profile, and historical activity.

2. Contextual Data Aggregation

A strong tool pulls in data from across the bank — including core banking systems, transaction logs, KYC platforms, and screening tools — to provide investigators with a consolidated view.

3. Natural Language Summarisation

Leading software uses AI to generate readable, regulator-friendly narratives that summarise key findings, reducing manual work and improving consistency.

4. Audit-Ready Case Management

Every step taken during an investigation should be logged and traceable, including decision-making, reviewer notes, and attached evidence.

5. Integration with STR Reporting Systems

The software should support direct integration with platforms such as GoAML, used in Singapore for suspicious transaction reporting.

How Tookitaki's FinCense Platform Elevates AML Investigations

Tookitaki’s FinCense platform is designed with Singapore’s regulatory expectations in mind and includes a specialised Smart Disposition Engine for AML investigations.

Key Features:

• AI Copilot (FinMate)
  Acts as an intelligent assistant that helps compliance teams assess red flags, suggest investigative steps, and provide context for alerts.
• Smart Narration Engine
  Automatically generates STR-ready summaries, saving hours of manual writing while ensuring consistency and auditability.
• Unified View of Risk
  Investigators can see customer profiles, transaction history, typologies triggered, and sanction screening results in one interface.
• Scenario-Based Insight
  Through integration with the AFC Ecosystem, the system maps alerts to real-world money laundering typologies relevant to the region.
• Workflow Customisation
  Investigation steps, user roles, and escalation logic can be tailored to the bank’s internal policies and team structure.

Benefits for Compliance Teams

By implementing AML investigation software like FinCense, banks in Singapore can achieve:

• Up to 50 percent reduction in investigation time
• Enhanced quality and consistency of STRs
• Faster closure of true positives
• Lower regulatory risk and better audit outcomes
• Improved collaboration across compliance, risk, and operations

Checklist: Is Your Investigation Process Ready for 2025?

Ask these questions to evaluate your current system:

• Are investigators manually pulling data from multiple systems?
• Is there a standard template for documenting cases?
• How long does it take to prepare an STR?
• Can you trace every decision made during an investigation?
• Are your analysts spending more time writing than investigating?

If any of these answers raise red flags, it may be time to upgrade.

Conclusion: Better Tools Build Stronger Compliance

AML investigation software is no longer a nice-to-have. It is a strategic enabler for banks to stay ahead of financial crime while meeting the rising expectations of regulators, auditors, and customers.

In Singapore's rapidly evolving compliance landscape, banks that invest in smart, AI-powered investigation tools will not only keep up. They will lead the way.

Ready to take your AML investigations to the next level? The future is intelligent, integrated, and investigator-first.