Compliance Hub

Fraud Detection Using Machine Learning in Banking

Tookitaki

10 min

read

The financial landscape is evolving rapidly. With this evolution comes an increase in financial crimes, particularly fraud.

Financial institutions are constantly seeking ways to enhance their fraud detection and prevention mechanisms. Traditional methods, while effective to some extent, often fall short in the face of sophisticated fraudulent schemes.

Enter machine learning. This technology has emerged as a game-changer in the banking sector, particularly in fraud detection.

Machine learning algorithms can sift through vast volumes of transaction data, identifying patterns and anomalies indicative of fraudulent activities. This ability to learn from historical data and predict future frauds is revolutionising the way financial institutions approach fraud detection.

An illustration of machine learning algorithms analyzing transaction data

However, the implementation of machine learning in fraud detection is not without its challenges. Distinguishing between legitimate transactions and suspicious activity, ensuring data privacy, and maintaining regulatory compliance are just a few of the hurdles to overcome.

This article aims to provide a comprehensive overview of fraud detection using machine learning in banking. It will delve into the evolution of fraud detection, the role of machine learning, its implementation, and the challenges faced.

By the end, financial crime investigators and other professionals in the banking sector will gain valuable insights into this cutting-edge technology and its potential in enhancing their fraud detection strategies.

The Evolution of Fraud Detection in Banking

The banking sector has always been a prime target for fraudsters. Over the years, the methods used to commit fraud have evolved, becoming more complex and sophisticated.

In response, financial institutions have had to adapt their fraud detection systems. Traditional fraud detection methods relied heavily on rule-based systems and manual investigations. These systems were designed to flag transactions that met certain predefined criteria indicative of fraud.

However, as the volume of transactions increased with the advent of digital banking, these traditional systems began to show their limitations. They struggled to process the vast amounts of transaction data, leading to delays in fraud detection and prevention.

Moreover, rule-based systems were often unable to detect new types of fraud that did not fit into their predefined rules. This led to a high number of false negatives, where fraudulent transactions went undetected.

The need for a more effective solution led to the exploration of machine learning for fraud detection.

Traditional Fraud Detection vs. Machine Learning Approaches

Traditional fraud detection systems, while useful, often lacked the ability to adapt to new fraud patterns. They were rigid, relying on predefined rules that could not capture the complexity of evolving fraudulent activities.

Machine learning, on the other hand, offers a more dynamic approach. It uses algorithms that learn from historical transaction data, identifying patterns and anomalies that may indicate fraud. This ability to learn and adapt makes machine learning a powerful tool in detecting and predicting future frauds.

Moreover, machine learning can handle large volumes of data, making it ideal for the digital banking environment where millions of transactions occur daily.

Limitations of Conventional Systems in the Digital Age

In the digital age, the volume, velocity, and variety of transaction data have increased exponentially. Traditional fraud detection systems, designed for a less complex era, struggle to keep up.

These systems often generate a high number of false positives, flagging legitimate transactions as suspicious. This not only leads to unnecessary investigations but can also result in a poor customer experience.

Furthermore, conventional systems are reactive, often detecting fraud after it has occurred. In contrast, machine learning allows for proactive fraud detection, identifying potential fraud before it happens. This shift from a reactive to a proactive approach is crucial in minimising financial loss and protecting customer trust.

Machine Learning: A Game Changer in Fraud Detection

Machine learning has emerged as a game changer in the field of fraud detection. Its ability to learn from data and adapt to new patterns makes it a powerful tool in the fight against financial fraud.

Machine learning algorithms can analyze vast amounts of transaction data in real-time. They can identify complex patterns and subtle correlations that may indicate fraudulent activity. This level of analysis is beyond the capabilities of traditional rule-based systems.

Moreover, machine learning can predict future frauds based on historical data. This predictive capability allows financial institutions to take proactive measures to prevent fraud, rather than reacting after the fact.

Machine learning also reduces the number of false positives. It can distinguish between legitimate transactions and suspicious activity with a high degree of accuracy. This not only saves resources but also improves the customer experience.

However, implementing machine learning in fraud detection is not without its challenges. It requires high-quality data, continuous model training, and a deep understanding of the underlying algorithms.

Understanding Machine Learning Algorithms in Banking

Machine learning algorithms can be broadly classified into supervised and unsupervised learning models. Supervised learning models are trained on labeled data, where the outcome of each transaction (fraudulent or legitimate) is known. These models learn to predict the outcome of new transactions based on this training.

Unsupervised learning models, on the other hand, do not require labeled data. They identify patterns and anomalies in the data, which can indicate potential fraud. These models are particularly useful in detecting new types of fraud that do not fit into known patterns.

Both supervised and unsupervised learning models have their strengths and weaknesses. The choice of model depends on the specific requirements of the financial institution and the nature of the data available.

Regardless of the type of model used, the effectiveness of machine learning in fraud detection depends largely on the quality of the data and the accuracy of the model training.

Real-Time Transaction Monitoring with Machine Learning

One of the key advantages of machine learning is its ability to process and analyse large volumes of data in real-time. This is particularly important in the context of digital banking, where transactions occur around the clock and across different channels.

Real-time transaction monitoring allows financial institutions to detect and prevent fraud as it happens. Machine learning algorithms can analyse each transaction as it occurs, flagging any suspicious activity for immediate investigation.

This real-time analysis is not limited to the transaction itself. Machine learning models can also analyze the context of the transaction, such as the customer's typical behavior, the time and location of the transaction, and other relevant factors.

This comprehensive analysis allows for more accurate fraud detection, reducing both false positives and false negatives. It also enables financial institutions to respond quickly to potential fraud, minimising financial loss and protecting customer trust.

Implementing Machine Learning Models for Fraud Detection

Implementing machine learning models for fraud detection requires a strategic approach. It's not just about choosing the right algorithms, but also about understanding the data and the business context.

The first step is to define the problem clearly. What type of fraud are you trying to detect? What are the characteristics of fraudulent transactions? What data is available for analysis? These questions will guide the choice of machine learning model and the design of the training process.

Next, the data needs to be prepared for analysis. This involves cleaning the data, handling missing values, and transforming variables as needed. The quality of the data is crucial for the performance of the machine learning model.

Once the data is ready, the machine learning model can be trained. This involves feeding the model with the training data and allowing it to learn from it. The model's performance should be evaluated and fine-tuned as necessary.

Finally, the model needs to be integrated into the existing fraud detection system. This requires careful planning and testing to ensure that the model works as expected and does not disrupt the existing processes.

Supervised vs. Unsupervised Learning in Fraud Detection

In the context of fraud detection, both supervised and unsupervised learning models have their uses. The choice between the two depends on the nature of the problem and the data available.

Supervised learning models are useful when there is a large amount of labeled data available. These models can learn from past examples of fraud and apply this knowledge to detect future frauds. However, they may not be as effective in detecting new types of fraud that do not fit into known patterns.

Unsupervised learning models, on the other hand, do not require labeled data. They can identify patterns and anomalies in the data, which can indicate potential fraud. These models are particularly useful in detecting new types of fraud that do not fit into known patterns.

Regardless of the type of model used, the effectiveness of machine learning in fraud detection depends largely on the quality of the data and the accuracy of the model training.

The Role of Data Quality and Model Training

Data quality plays a crucial role in the effectiveness of machine learning models for fraud detection. High-quality data allows the model to learn accurately and make reliable predictions.

Data quality involves several aspects, including accuracy, completeness, consistency, and timeliness. The data should accurately represent the transactions, be complete with no missing values, be consistent across different sources, and be up-to-date.

Model training is another critical factor in the success of machine learning for fraud detection. The model needs to be trained on a representative sample of the data, with a good balance between fraudulent and legitimate transactions.

The model's performance should be evaluated and fine-tuned as necessary. This involves adjusting the model's parameters, retraining the model, and validating its performance on a separate test set.

Continuous monitoring and updating of the model is also essential to ensure that it remains effective as new patterns of fraud emerge.

Challenges in Machine Learning-Based Fraud Detection

Despite the potential of machine learning in fraud detection, there are several challenges that financial institutions need to address. One of the main challenges is the complexity of financial transactions.

Financial transactions involve numerous variables and can follow complex patterns. This complexity can make it difficult for machine learning models to accurately identify fraudulent transactions.

Another challenge is the imbalance in the data. Fraudulent transactions are relatively rare compared to legitimate transactions. This imbalance can lead to models that are biased towards predicting transactions as legitimate, resulting in a high number of false negatives.

The dynamic nature of fraud is another challenge. Fraudsters continuously adapt their tactics to evade detection. This means that machine learning models need to be regularly updated to keep up with new patterns of fraud.

Finally, there are challenges related to data privacy and security. Financial transactions involve sensitive personal information. Financial institutions need to ensure that this data is handled securely and that privacy is maintained.

Distinguishing Legitimate Transactions from Fraudulent Activity

Distinguishing between legitimate transactions and fraudulent activity such as credit card fraud is a key challenge in fraud detection. This is particularly difficult because fraudulent transactions often mimic legitimate ones.

Machine learning models can help to address this challenge by identifying patterns and anomalies in the data. However, these models need to be trained on high-quality data and need to be regularly updated to keep up with changing patterns of fraud.

False positives are another concern. These occur when legitimate transactions are incorrectly flagged as fraudulent. This can lead to unnecessary investigations and can disrupt the customer experience. Strategies to minimise false positives include refining the model's parameters and incorporating feedback from fraud investigators.

Ethical and Privacy Considerations in Data Usage

The use of machine learning in fraud detection raises several ethical and privacy considerations. One of the main concerns is the use of personal transaction data.

Financial institutions need to ensure that they are complying with data protection regulations. This includes obtaining the necessary consents for data usage and ensuring that data is stored securely.

There is also a need for transparency in the use of machine learning. Customers should be informed about how their data is being used and how decisions are being made. This can help to build trust and can also provide customers with the opportunity to correct any inaccuracies in their data.

Finally, there are ethical considerations related to the potential for bias in machine learning models. Financial institutions need to ensure that their models are fair and do not discriminate against certain groups of customers. This requires careful design and testing of the models, as well as ongoing monitoring of their performance.

Financial Institutions Winning the Fight Against Fraud

Financial institutions are increasingly turning to machine learning to combat fraud. This is not just limited to large multinational banks. Smaller banks and credit unions are also adopting these technologies, often in partnership with fintech companies.

One example is the Royal Bank of Scotland, which uses machine learning to analyze customer behaviour and identify unusual patterns. This has helped the bank to detect and prevent fraud, improving customer trust and reducing financial loss.

Another example is Danske Bank, which uses machine learning to detect money laundering. The bank's machine learning model analyses transaction data and flags suspicious activity for further investigation. This has helped the bank to comply with anti-money laundering regulations and has also reduced the cost of investigations.

These examples show that machine learning is not just a tool for the future. It is already being used today, helping financial institutions to win the fight against fraud.

The Future of Fraud Detection in Banking

The future of fraud detection in banking is promising, with machine learning playing a central role. As technology continues to evolve, so too will the methods used to detect and prevent fraud.

Machine learning models will become more sophisticated, capable of analysing larger volumes of data and identifying more complex patterns of fraudulent activity. This will enable financial institutions to detect fraud more quickly and accurately, reducing financial loss and improving customer trust.

At the same time, the integration of machine learning with other technologies, such as artificial intelligence and blockchain, will enhance fraud detection capabilities. These technologies will provide additional layers of security, making it even harder for fraudsters to succeed.

The future will also see greater collaboration between financial institutions, fintech companies, and law enforcement agencies. By sharing data and insights, these organizations can work together to combat financial fraud more effectively.

Emerging Trends and Technologies

Several emerging trends and technologies are set to shape the future of fraud detection in banking. One of these is deep learning, a subset of machine learning that uses neural networks to analyse data. Deep learning can identify complex patterns and correlations in data, making it a powerful tool for detecting fraud.

Another trend is the use of behavioural biometrics, which analyses the unique ways in which individuals interact with their devices. This can help to identify fraudulent activity, as fraudsters will interact with devices in different ways to legitimate users.

Finally, the use of consortium data and shared intelligence will become more common. By pooling data from multiple sources, financial institutions can build more accurate and robust machine learning models for fraud detection.

Preparing for the Next Wave of Financial Crimes

As technology evolves, so too do the methods used by fraudsters. Financial institutions must therefore be proactive in preparing for the next wave of financial crimes. This involves staying up-to-date with the latest trends and technologies in fraud detection, and continuously updating and refining machine learning models.

Financial crime investigators will also need to develop new skills and expertise. This includes understanding how machine learning works, and how it can be applied to detect and prevent fraud. Training and professional development will therefore be crucial.

Finally, financial institutions will need to adopt a multi-layered security approach. This involves using a range of technologies and methods to detect and prevent fraud, with machine learning being just one part of the solution. By doing so, they can ensure that they are well-prepared to combat the ever-evolving threat of financial fraud.

Conclusion: Embracing Machine Learning for a Safer Banking Environment

In conclusion, as financial institutions strive to stay ahead of increasingly sophisticated fraud tactics, adopting advanced solutions like Tookitaki's FinCense becomes imperative.

With its real-time fraud prevention capabilities, FinCense empowers banks and fintechs to screen customers and transactions with remarkable 90% accuracy, ensuring robust protection against fraudulent activities. Its comprehensive risk coverage, powered by cutting-edge AI and machine learning, addresses all potential risk scenarios, providing a holistic approach to fraud detection.

Moreover, FinCense's seamless integration with existing systems enhances operational efficiency, allowing compliance teams to concentrate on the most significant threats. By choosing Tookitaki's FinCense, financial institutions can safeguard their operations and foster a secure environment for their customers, paving the way for a future where fraud is effectively mitigated.

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Compliance Hubs

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

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.