Compliance Hub

Hidden Risks in Anti-Money Laundering Compliance: What Banks Miss Most

Tookitaki

10 min

read

Despite investing billions in anti-money laundering systems, banks continue to face record fines for compliance failures, reaching $5 billion in 2022 alone. While most financial institutions have basic AML frameworks in place, dangerous blind spots lurk beneath the surface of their compliance programs.

These hidden risks extend far beyond simple system glitches or process gaps. From outdated legacy systems failing to detect sophisticated money laundering patterns to critical weaknesses in customer due diligence, banks face multiple vulnerabilities that often go unnoticed until it's too late.

This article examines the most significant yet frequently overlooked risks in AML compliance, including technological limitations, customer due diligence gaps, transaction monitoring weaknesses, and regulatory interpretation challenges. Understanding these hidden risks is crucial for financial institutions to strengthen their defences against evolving money laundering threats and avoid costly compliance failures.

Hidden Risks in AntiMoney Laundering Compliance What Banks Miss Most-2

Technological Blind Spots in AML Systems

Financial institutions increasingly find themselves caught between outdated technology infrastructure and sophisticated money laundering techniques. Traditional approaches to anti-money laundering detection are becoming less effective as criminals adapt their methods. This technological gap creates significant blind spots in even the most well-funded AML programs.

Legacy System Integration Failures

The financial sector's reliance on outdated core systems creates fundamental vulnerabilities in AML frameworks. Financial institutions face substantial challenges when attempting to integrate modern detection tools with existing infrastructure. The costs and complexities involved in replacing legacy systems often prevent banks from fully utilizing innovative AML approaches. Consequently, many institutions continue operating with fragmented systems that fail to communicate effectively.

When legacy platforms cannot properly interface with newer monitoring solutions, critical transaction data falls through the cracks. This fragmentation creates dangerous monitoring gaps, as evidenced by cases where incorrect implementation of detection rules resulted in failures to generate alerts on suspicious transactions over extended periods. Such integration failures demonstrate how even properly designed AML systems can fail when implementation and integration are flawed.

Data Quality Issues in Transaction Monitoring

AML controls depend heavily on unstructured data elements like customer names and addresses that pass through numerous banking systems before reaching monitoring tools. Poor data quality manifests in various forms:

Incorrect spellings, dummy dates of birth, and incomplete addresses
Disparate data sources creating fragmented customer views
Inconsistent formatting across systems
Lack of data integrity controls

Banks have invested tens of millions of dollars addressing these data quality issues, yet problems persist. When transaction monitoring systems receive compromised data, they inevitably produce compromised results. The Hong Kong Monetary Authority has emphasized that "the integrity and robustness of a transaction monitoring system is vital in the ongoing fight against financial crime".

Algorithm Limitations in Pattern Detection

Conventional rule-based transaction monitoring solutions generate significant false positive alerts while missing sophisticated criminal behaviours. These systems typically lack the ability to:

Support scenarios with dynamic parameters based on customer profiles
Adapt to changing money laundering risks
Identify new transaction patterns
Detect emerging threats

Furthermore, traditional monitoring approaches rely on periodic reviews and manual reporting, making real-time detection nearly impossible. Static systems only identify what they were originally programmed to find, creating a reactive rather than proactive approach. Some financial institutions have begun adopting AI and machine learning to address these limitations, using these technologies to analyze large transaction volumes and identify behavioural patterns indicating potential risks.

API Connection Vulnerabilities

As banks expand their digital ecosystems, API vulnerabilities create new AML blind spots. The research identified that 95% of organizations experienced API security incidents within a 12-month period, with malicious API traffic growing by 681%. These vulnerabilities can allow threat actors to:

Gain administrative access to banking systems
Access users' banking details and financial transactions
Leak personal data
Perform unauthorized fund transfers

In one notable case, researchers discovered a Server-Side Request Forgery flaw in a U.S.-based fintech platform that could have compromised millions of users' accounts. Additionally, attacks against internal APIs of financial institutions increased by 613% between the first and second halves of one year, highlighting this growing threat vector.

Customer Due Diligence Gaps Beyond KYC

Even with robust Know Your Customer procedures in place, financial institutions frequently struggle with deeper customer due diligence gaps that expose them to significant money laundering risks. These vulnerabilities extend far beyond initial customer identification and verification, creating blind spots in ongoing risk management processes.

Beneficial Ownership Verification Challenges

Corporate vehicles remain primary tools for disguising illicit financial flows, primarily because beneficial ownership information is often inadequate, inaccurate, or outdated. Money launderers typically obscure ownership through shell companies, complex multi-layered structures, bearer shares, and nominee arrangements. The Financial Action Task Force (FATF) specifically notes how criminals deliberately split company formation, asset ownership, professional intermediaries, and bank accounts across different countries to evade regulations.

Verification presents a substantial hurdle as many beneficial ownership registries rely on self-declaration without proper authentication mechanisms. Although regulations like the Customer Due Diligence (CDD) Rule require financial institutions to identify individuals holding at least 25% of an investment entity, several implementation challenges persist:

Complex ownership chains involving entities across multiple jurisdictions
Difficulty distinguishing between legal and beneficial ownership
Insufficient documentation to support ownership claims
Limited access to reliable cross-border ownership information

Such verification failures explain why artificial corporate structures continue facilitating financial crimes, particularly in cross-border contexts.

Ongoing Monitoring Weaknesses

Static, periodic reviews have proven inadequate for detecting evolving risk profiles. Many institutions conduct customer risk assessments as one-time exercises during onboarding rather than ongoing processes. This approach fails to capture changing customer behaviours and risk levels that emerge throughout the relationship lifecycle.

The Hong Kong Monetary Authority emphasizes that "risk levels are not static and can change over time based on customer behaviour, market conditions, or regulatory developments". However, most financial institutions lack the infrastructure to implement truly perpetual KYC solutions where customers are screened in real-time or near real-time based on trigger events.

Common ongoing monitoring deficiencies include:

Delayed reactions to significant customer profile changes, especially regarding beneficial ownership structures that evolve over time. Financial institutions frequently fail to detect when low-risk customers transition to higher-risk categories through changed circumstances or behaviours. Moreover, banks often lack effective systems to identify suspicious patterns that develop gradually across multiple accounts or entities.

Cross-Border Customer Risk Assessment Failures

International banking operations create particularly challenging due diligence environments. According to the Bank for International Settlements, banks engaging in cross-border activities face "increased legal risk" specifically because they may fail to comply with different national laws and regulations. Such failures occur through both inadvertent misinterpretation and deliberate avoidance.

Cross-border risk assessment challenges stem from fundamental structural issues. First, significant differences exist between jurisdictions regarding bank licensing, supervisory requirements, and customer protection frameworks. Second, data protection regulations frequently complicate information sharing across borders, hampering holistic customer risk assessment. Finally, cultural and linguistic differences lead to misunderstandings and misalignments between financial institutions and regulatory authorities.

These jurisdictional complexities create perfect conditions for regulatory arbitrage. Money launderers specifically target jurisdictions with weaker beneficial ownership transparency requirements, exploiting gaps between regulatory regimes. Correspondent banking relationships exacerbate these challenges as domestic banks must often rely on foreign banks' AML capabilities, which may not meet their own compliance standards.

Banks that fail to develop specialized cross-border due diligence frameworks remain vulnerable to sophisticated laundering schemes that deliberately operate across multiple regulatory environments.

Transaction Monitoring Weaknesses

Transaction monitoring forms the backbone of modern anti-money laundering defence systems, yet financial institutions consistently struggle with fundamental weaknesses that undermine their effectiveness. Even well-designed systems often fail to detect suspicious activities due to configuration issues, management challenges, and technological limitations.

Alert Threshold Configuration Errors

Setting appropriate thresholds represents a critical challenge in transaction monitoring. The Hong Kong Monetary Authority found instances where banks set thresholds for premium and private banking segments at levels five times higher than customers' expected assets under management, severely limiting detection capabilities. In another case, a bank's pass-through payment scenario failed to flag a major transaction where $38.91 million flowed in and out within three days.

Incorrect segmentation further compounds threshold configuration problems. Banks that fail to properly segment their customer base undermine the risk-based approach by not monitoring clients for the specific risks they pose or are exposed to. Subsequently, clients allocated to incorrect segments generate unnecessary alerts while genuine suspicious activities go undetected. Indeed, poor segmentation leads to thresholds being set for broad populations rather than tailored to narrower ranges of similar customer behaviour.

False Positive Management Problems

The banking industry faces an overwhelming challenge with false positive rates in AML transaction monitoring systems reaching as high as 90%. Studies show that industry-wide, up to 95% of alerts generated by traditional monitoring systems are false positives. This flood of false alerts creates significant operational inefficiencies:

Wasted resources investigating legitimate transactions
Substantial costs in terms of manpower and time
Alert backlogs leading to delayed identification of actual suspicious activity
Potential for genuine threats to be overlooked amid the noise

Importantly, false positives not only burden compliance teams but can also lead to innocent customers being treated as suspicious, resulting in negative customer experiences and potential customer loss.

Scenario Coverage Limitations

Many transaction monitoring scenarios are implemented merely because they are available in vendor solutions rather than based on specific risk analysis. As a result, institutions face a disconnect between their AML risk assessments and transaction monitoring processes, leading to under-monitoring in some areas and over-monitoring in others.

Furthermore, static rule-based systems operate within predefined thresholds and struggle to identify complex, evolving money laundering patterns. These systems primarily detect what they were originally programmed to find, creating a reactive rather than proactive approach to detecting suspicious activity.

Real-Time Monitoring Gaps for Digital Payments

Digital payment systems create unique vulnerabilities through the very features that make them appealing: speed, convenience, and anonymity. Traditional transaction monitoring approaches rely on periodic reviews and manual reporting, making real-time detection nearly impossible.

For effective anti-money laundering compliance in digital payments, continuous monitoring through automation is crucial. Without robust real-time processing capabilities, financial institutions cannot promptly identify and flag suspicious activities in digital transactions. This timing gap allows sophisticated criminals to exploit the delay between transaction execution and detection, particularly in cross-border scenarios where speed is a critical factor.

Regulatory Interpretation Misalignments

Banks frequently navigate a labyrinth of regulatory frameworks that vary significantly across borders, creating fundamental misalignments in anti-money laundering compliance. These inconsistencies often remain unaddressed until exposed through costly enforcement actions.

Jurisdictional Requirement Conflicts

The convergence of AML transparency objectives and data privacy constraints creates significant operational challenges for global financial institutions. In the United States, personal information is typically considered the property of the data holder, whereas in the European Union, privacy is a fundamental right with personal information ownership vested in the individual. This creates an inherent tension between regulatory regimes:

US relies on sector-specific privacy regulations without a comprehensive federal privacy law
EU takes a harmonized approach through the General Data Protection Regulation (GDPR)
Different jurisdictions impose varying customer due diligence requirements
Some jurisdictions require self-reporting while others do not

These inconsistencies frequently force institutions to implement group-wide policies applying the most restrictive regime globally, though local laws must still govern reporting and information-sharing procedures.

Evolving Regulatory Guidance Misinterpretation

The Financial Action Task Force (FATF) recommendations remain the global AML standard, nevertheless, implementations vary considerably across jurisdictions. Many financial institutions struggle with interpreting evolving regulatory changes correctly. For instance, the revised FATF Recommendations issued in 2012 raised the bar on regulatory expectations in most jurisdictions. Furthermore, terminology inconsistency compounds confusion - some professionals refer to their compliance responsibilities as "AML/KYC" while FinCEN uses "AML/CFT programs".

Implementation challenges intensify when risk assessments are not regularly updated as banks adjust business models to adapt to market developments. Even recently, the 2024 FinCEN final rule requiring investment advisers to implement AML/CFT programs has created widespread misunderstandings about applicability and implementation requirements.

Enforcement Action Blind Spots

Enforcement patterns reveal systematic blind spots in AML frameworks. In fact, the Hong Kong Monetary Authority's disciplinary actions against four banks demonstrated common control lapses that occurred in ongoing monitoring and enhanced due diligence in high-risk situations. Meanwhile, digital payments and e-commerce continue to be blind spots in AML regimes, with enforcement mechanisms primarily targeting traditional financial services.

The TD Bank settlement of HKD 23.34 billion over AML failures illustrates a concerning regulatory gap - the violations persisted for years before detection. This suggests not just institutional failures, but systemic weaknesses in regulatory monitoring itself.

Resource Allocation and Expertise Deficits

Proper resource distribution remains a critical challenge in anti-money laundering efforts, with financial institutions often miscalculating where to deploy their limited assets. Resource allocation deficiencies frequently undermine otherwise well-designed compliance programs.

Compliance Staff Training Inadequacies

Insufficient training consistently emerges as a primary driver of AML failures. Banks that neglect regular staff education create environments where employees cannot effectively identify suspicious activities or understand their reporting obligations. In one notable enforcement case, inadequate staff training directly contributed to compliance violations as employees lacked an understanding of proper due diligence procedures.

The consequences extend beyond mere regulatory violations. Poorly trained staff cannot apply the "art" of anti-money laundering compliance—the intuitive ability to recognize when something requires deeper investigation. As one compliance expert noted, "Sometimes, good compliance boils down to a suspicion by a trained, experienced compliance officer that something is off".

Budget Distribution Imbalances

Financial institutions frequently allocate resources ineffectively. European banks spend approximately €22,984 daily on KYC programs, yet only 26% goes toward technological solutions that could reduce operating costs and scale with future growth. Instead, most AML budgets fund manual processes that cannot meet increasing compliance demands.

This imbalance creates a troubling pattern: 90% of financial institutions expect compliance operating costs to increase by up to 30% over two years, yet 72% admit compliance technology budgets have remained static. Hence, banks remain caught in cycles of increasing operational expenses without corresponding investments in efficiency.

Technology vs. Human Expertise Trade-offs

Essentially, effective AML systems require both technological capability and human judgment. While advanced solutions can process vast transaction volumes, they cannot replace human expertise. Even with sophisticated technology, "manual review and human input remains very important".

The optimal approach combines "the efficiency and accuracy of digital solutions with the knowledge and analytical skills of human experts". Institutions that overcorrect toward either extreme—excessive reliance on automation or overwhelming manual processes—create significant vulnerabilities in their compliance frameworks.

Conclusion: Strengthening Money Laundering Compliance with Tookitaki

Financial institutions face significant hidden risks in their AML compliance programs, even after investing billions in prevention systems. These vulnerabilities stem from legacy system limitations, data quality issues, algorithm constraints, and regulatory misinterpretations, all of which create dangerous blind spots in financial crime detection.

To combat these challenges effectively, banks must adopt comprehensive, AI-driven AML compliance solutions that go beyond traditional rule-based systems. This is where Tookitaki sets the industry standard.

Tookitaki’s FinCense platform revolutionizes money laundering compliance with:

AI-Powered Transaction Monitoring – Reduces false positives and detects sophisticated laundering patterns in real-time.
Dynamic Risk-Based Approach – Strengthens customer due diligence (CDD) and beneficial ownership verification.
Automated Screening & Regulatory Alignment – Ensures seamless compliance across multiple jurisdictions.
Federated Learning Models – Continuously adapts to new money laundering tactics, keeping financial institutions ahead of evolving risks.

Financial institutions that fail to modernize their AML frameworks risk regulatory penalties, financial losses, and reputational damage. By leveraging Tookitaki’s AI-driven AML compliance solutions, banks can eliminate hidden risks, improve operational efficiency, and stay ahead of financial criminals.

Enhance your AML compliance strategy today with Tookitaki.

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Our Thought Leadership Guides

Compliance Hubs

18 Sep 2025

6 min

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Fraud Detection Using Machine Learning in Banking: Malaysia’s Next Line of Defence

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

Data Collection
ML models analyse vast amounts of data, including transaction history, customer behaviour, device information, and geolocation.
Feature Engineering
Key attributes are extracted, such as transaction frequency, average values, and unusual login behaviour.
Model Training
Algorithms are trained on historical data, distinguishing between legitimate and fraudulent activity.
Real-Time Detection
As transactions occur, ML models assign risk scores and flag suspicious cases instantly.
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.

Fraud Detection Using Machine Learning in Banking: Malaysia’s Next Line of Defence

Compliance Hubs

18 Sep 2025

6 min

read

Federated Learning in AML: A Smarter Way to Fight Financial Crime in Australia

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

Local Training
Each institution trains an AI model on its transaction and customer data.
Model Updates Shared
Only the learned patterns (model weights) are sent to a central aggregator.
Global Model Improved
The aggregator combines updates from all banks into a stronger model.
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

Mule Account Detection
Identifies networks of mule accounts across different banks.
Cross-Border Laundering
Tracks layering activity spread across institutions and jurisdictions.
Fraud Typology Sharing
Allows banks to learn from each other’s fraud cases without sharing customer data.
Sanctions Screening Enhancement
Improves detection of high-risk entities that use aliases or complex networks.
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

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

The Future of Federated Learning in AML

Industry-Wide Collaboration: More banks will join federated networks to share intelligence.
Real-Time Typology Sharing: Federated systems will distribute new fraud scenarios instantly.
Cross-Sector Expansion: Insurers, payment firms, and fintechs will join federated AML networks.
Global Interoperability: Federated learning models will connect across borders.
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.

Federated Learning in AML: A Smarter Way to Fight Financial Crime in Australia

Compliance Hubs

17 Sep 2025

6 min

read

The Investigator’s Edge: Why AML Investigation Software Is a Must-Have for Singapore’s Banks

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.

The Investigator’s Edge: Why AML Investigation Software Is a Must-Have for Singapore’s Banks

Blogs

18 Sep 2025

6 min

read

Fraud Detection Using Machine Learning in Banking: Malaysia’s Next Line of Defence

Fraudsters think fast, but machine learning thinks faster.

Malaysia’s Growing Fraud Challenge

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.