Compliance Hub

What is Money Laundering Act?

Site Logo
Tookitaki
16 Mar 2021
5 min
read

What is the Money Laundering Act?

What is the Money Laundering Act? Money laundering refers to the act of sneaking “dirty” money obtained through criminal activities through seemingly legitimate channels, as a way of disguising the true source of the funds. Money laundering has a few key sources, including organized crime, white-collar offences, terrorist activities, and drug smuggling.

Since money laundering poses a massive risk to the global economy, such as draining a large number of funds in addition to funding criminal activities, combatting it is essential to the international community. That’s the reason why there are several national, international, and regional bodies that monitor and regulate money laundering.

These organizations, such as the Financial Action Task Force (FATF) and the United Nations Office on Drugs and Crime (UNODC), draft and implement AML US laws such as money laundering acts and guidelines that financial institutions must comply with. These refer to a set of processes and procedures institutions must implement in order to prevent and promptly catch financial crime or any illegal fiscal activity. If companies do not follow these regulations, they must pay hefty non-compliance fines.

Money Laundering Act (1986)

What is the Money Laundering Act? Globally, numerous laws and legislations have been passed with the intention to prevent money laundering and punish the perpetrators of this illegal activity. Initially, these laws were drafted to combat the mafia and other elements of organized crime, though, over time, the focus shifted to curbing drug smuggling and, later, anti-terrorism.

In the USA, the first such law was The Bank Secrecy Act (BSA), which was passed in the 1970s. The Act refers to a collection of laws that require financial institutions to report suspicious transactions to the USA Department of Treasury. Financial institutions could either have reason to believe that the money is related to criminal activity through their client risk assessments, or could track clients who appear to avoid BSA reporting requirements.

What is the Money Laundering Act? The Money Laundering Act (1986) restricts people from engaging in financial transactions with funds obtained through criminal activities. This includes the transferring of money from one private individual to another. The Money Laundering Act (1986) was designed to make the hiding and reinvestment of illegal profits made from a criminal enterprise and profits into a new federal offence. The Money Laundering Act (1986) targets conduct that occurs after the underlying crime and is not intended to be an alternative means of punishing the crime itself.

The Annunzio-Wylie Anti-Money Laundering Act (1992) strengthened penalties for non-compliance. Review, training, and examination procedures were boosted through the Money Laundering Suppression Act (1994) and the Money Laundering and Financial Crimes Strategy Act (1998).

Money Laundering Act (1996)

Money Laundering Act (1996): Every country around the world has clearly outlined regulations pertaining to money laundering. Money Laundering Acts, such as the Money Laundering Act (1996), as amended by the Money Laundering Act (1999), and the Money Laundering Act (2001) are good examples.

What is the Money Laundering Act? Money Laundering Act (1996) was an Act to criminalise money laundering. The motive of the Money Laundering Act (1996) was to require financial institutions/firms to maintain identification procedures and record-keeping procedures. The purpose of the Money Laundering Act (1996) was also to make orders in relation to proceeds of crime and properties of offenders, to designate money laundering as an extraditable offence.

The digital age provides the perfect environment for money launderers to keep innovating new methods to commit financial crimes. That is why money laundering acts such as Money Laundering Act (1986) and the Money Laundering Act (1996) and govt regulations are subject to constant change and updates. This is especially applicable in 2020, where the AML risk due to the COVID-19 era is both new and challenging.

The PATRIOT Act

The PATRIOT Act resulted from the 9/11 terrorist attacks in the USA. This Act seeks to strengthen and expand the regulation of financial transactions and the financial market as a whole. Title II of the PATRIOT Act is referred to as the International Money Laundering Abatement and Anti-Terrorist Financing Act (2001). It focuses almost exclusively on money laundering and related issues.

The PATRIOT Act explains in detail some of the questions regarding the BSA’s applicability, as well as introducing fresh requirements. Financial institutions can often try and be “willfully blind” to illegal financial transactions taking place right within their organizations. So, the PATRIOT Act requires the creation and implementation of new and improved anti-money laundering programs that hold financial institutions accountable if money laundering does occur. The Act further mandates companies to appoint a compliance officer, draft an internal compliance policy, regularly train staff, and conduct independent audits.

It also necessitates that financial institutions implement proper Know Your Customer (KYC) procedures to verify the identity of their clients and also check them against lists of known terrorists and other criminals. These are called “forthcoming requirements,” and strengthen the BSA’s need for KC.

The PATRIOT Act and the Bank Secrecy Act provide a layer of protection to the USA’s economy and financial institutions against money laundering and other financial crimes. These laws encompass the procedure to recognize suspicious activity, flag off concerned authorities, and trigger the necessary legal action required to charge the criminals. These laws have the power to have suspicious financial institutions investigated by the Federal Reserve and the Office of the Comptroller of Currency.

There is one loophole, however. The definition of “financial institution” under both of these Acts excludes investment advisors and transfer agents. Hence, these companies are treading a thin, unregulated line of profits at the moment. Generally, though, these companies must be registered under the Investment Company Act (1940) to carry out operations.

Prevention of Money Laundering Act & Other National Money Laundering Regulations

Prevention of Money Laundering Act (2002): As mentioned earlier, all countries across the globe have regulations and laws pertaining to money laundering. India passed the Prevention of Money Laundering Act in 2002. The purpose of Prevention of Money Laundering Act: (2002) was to curb the rampant money laundering and corruption being carried out within the country. Prevention of Money Laundering Act: (2002) was enacted by the National Defense Army (NDA) government to fight against money-laundering and terrorist financing. The aim of the Prevention of Money Laundering Act was also to provide for confiscation of property derived from money-laundering. Effective from July 1, 2005, the PMLA and the Rules notified there under came into force.

They also have internal bodies to govern the finance industry, such as the Monetary Authority of Singapore, the Australian Transaction Reports and Analysis Center, the Honk Kong Monetary Authority, and the The Financial Conduct Authority (FCA) in the UK, all of which pass acts and laws regarding financial crimes within their respective jurisdiction

The European Union, too, passed two directives, EU 5AMLD and 6AMLD, in an attempt to harmonize transactions between all the member states. All of these organizations aim to regulate, track, monitor, and prevent money laundering, terrorist financing, or any other illegal activity involving money in their regions.

These bodies change and update regulations often. Indeed, the financial market and industry are in flux due to the advent of technology. So, they need to be on their toes and think one step ahead of criminals in order to outsmart them and prevent financial crimes from being committed. By doing so, they begin the process of drafting and designing AML compliance regulations and policies.

Read here to know more about the job role of an MLRO.

By submitting the form, you agree that your personal data will be processed to provide the requested content (and for the purposes you agreed to above) in accordance with the Privacy Notice

success icon

We’ve received your details and our team will be in touch shortly.

In the meantime, explore how Tookitaki is transforming financial crime prevention.
Learn More About Us
Oops! Something went wrong while submitting the form.

Ready to Streamline Your Anti-Financial Crime Compliance?

Our Thought Leadership Guides

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

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.

Talk to an Expert

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.

ChatGPT Image Sep 17, 2025, 04_29_19 PM

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
Blogs
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.

Talk to an Expert

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.

ChatGPT Image Sep 17, 2025, 04_00_31 PM

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.

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

Talk to an Expert

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.

ChatGPT Image Sep 17, 2025, 11_47_45 AM

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