Singapore’s banks are in a race, not just against time, but against tech-savvy fraudsters.

In today’s digital-first banking world, fraud no longer looks like it used to. It doesn’t arrive as forged cheques or shady visits to the branch. It slips in quietly through real-time transfers, fake identities, and unsuspecting mule accounts.

As financial crime becomes more sophisticated, traditional rule-based systems struggle to keep up. And that’s where next-generation bank transaction fraud detection comes in.

This blog explores how Singapore’s banks can shift from reactive to real-time fraud prevention using smarter tools, scenario-based intelligence, and a community-led approach.

The Growing Threat: Real-Time, Real-Risk

Instant payment systems like FAST and PayNow have transformed convenience for consumers. But they’ve also created perfect conditions for fraud:

• Funds move instantly, leaving little time to intervene.
• Fraud rings test systems for weaknesses.
• Mules and synthetic identities blend in with legitimate users.

In Singapore, the number of scam cases surged past 50,000 in 2025 alone. Many of these begin with social engineering and end with rapid fund movements that outpace traditional detection tools.

What Is Bank Transaction Fraud Detection?

Bank transaction fraud detection refers to the use of software and intelligence systems to:

• Analyse transaction patterns in real-time
• Identify suspicious behaviours (like rapid movement of funds, unusual login locations, or account hopping)
• Trigger alerts before fraudulent funds leave the system

But not all fraud detection tools are created equal.

Beyond Rules: Why Behavioural Intelligence Matters

Most legacy systems rely heavily on static rules:

• More than X amount = Alert
• Transfer to high-risk country = Alert
• Login from new device = Alert

While helpful, these rules often generate high false positives and fail to detect fraud that evolves over time.

Modern fraud detection uses behavioural analytics to build dynamic profiles:

• What’s normal for this customer?
• How do their patterns compare to their peer group?
• Is this transaction typical for this day, time, device, or network?

This intelligence-led approach helps Singapore’s banks catch subtle deviations that indicate fraud without overloading investigators.

Common Transaction Fraud Tactics in Singapore

Here are some fraud tactics that banks should watch for:

1. Account Takeover (ATO):

Fraudsters use stolen credentials to log in and drain accounts via multiple small transactions.

2. Business Email Compromise (BEC):

Corporate accounts are manipulated into wiring money to fraudulent beneficiaries posing as vendors.

3. Romance & Investment Scams:

Victims willingly send money to fraudsters under false emotional or financial pretences.

4. Mule Networks:

Illicit funds are routed through a series of personal or dormant accounts to obscure the origin.

5. ATM Cash-Outs:

Rapid withdrawals across multiple locations following fraudulent deposits.

Each scenario requires context-aware detection—something traditional rules alone can’t deliver.

How Singapore’s Banks Are Adapting

Forward-thinking institutions are shifting to:

• Real-time monitoring: Systems scan every transaction as it happens.
• Scenario-based detection: Intelligence is built around real fraud typologies.
• Federated learning: Institutions share anonymised risk insights to detect emerging threats.
• AI and ML models: These continuously learn from past patterns to improve accuracy.

This new generation of tools prioritises precision, speed, and adaptability.

The Tookitaki Approach: Smarter Detection, Stronger Defences

Tookitaki’s FinCense platform is redefining how fraud is detected across APAC. Here’s how it supports Singaporean banks:

✅ Real-time Detection

Every transaction is analysed instantly using a combination of AI models, red flag indicators, and peer profiling.

✅ Community-Driven Typologies

Through the AFC Ecosystem, banks access and contribute to real-world fraud scenarios—from mule accounts to utility scam layering techniques.

✅ Federated Intelligence

Instead of relying only on internal data, banks using FinCense tap into anonymised, collective intelligence without compromising data privacy.

✅ Precision Tuning

Simulation features allow teams to test new detection rules and fine-tune thresholds to reduce false positives.

✅ Seamless Case Integration

When a suspicious pattern is flagged, it’s directly pushed into the case management system with contextual details for fast triage.

This ecosystem-powered approach offers banks a smarter, faster path to fraud prevention.

What to Look for in a Transaction Fraud Detection Solution

When evaluating solutions, Singaporean banks should ask:

• Does the tool operate in real-time across all payment channels?
• Can it adapt to new typologies without full retraining?
• Does it reduce false positives while improving true positive rates?
• Can it integrate into your existing compliance stack?
• Is the vendor proactive in fraud intelligence updates?

Red Flags That Signal a Need to Upgrade

If you’re noticing any of the following, it may be time to rethink your detection systems:

• Your fraud losses are rising despite existing controls.
• Investigators are buried under low-value alerts.
• You’re slow to detect new scams until after damage is done.
• Your system relies only on historical transaction patterns.

Future Outlook: From Reactive to Proactive Fraud Defence

The future of bank transaction fraud detection lies in:

• Proactive threat hunting using AI models
• Crowdsourced intelligence from ecosystems like AFC
• Shared risk libraries updated in real-time
• Cross-border fraud detection powered by network-level insights

As Singapore continues its Smart Nation push and expands its digital economy, the ability to protect payments will define institutional trust.

Conclusion: A Smarter Way Forward

Fraud is fast. Detection must be faster. And smarter.

By moving beyond traditional rule sets and embracing intelligent, collaborative fraud detection systems, banks in Singapore can stay ahead of evolving threats while keeping customer trust intact.

Transaction fraud isn’t just a compliance issue—it’s a business continuity one.

n financial compliance, how you start often defines how well you succeed.

As financial institutions across Singapore continue to digitise, one of the most critical stages in the customer lifecycle is also one of the most overlooked: onboarding. In a world of rising financial crime, increasingly complex regulatory expectations, and growing customer expectations for speed and simplicity—getting onboarding right is a compliance and business imperative.

AML onboarding software helps institutions walk this tightrope, balancing user experience with regulatory rigour. This blog explores what AML onboarding software is, why it matters in Singapore, and what features to look for when choosing the right solution.

Why Onboarding is a High-Risk Stage for Financial Crime

The onboarding phase is where risk enters the institution. Criminals often use fake identities, straw accounts, or mule accounts to gain access to the financial system. If these bad actors slip through during onboarding, they become much harder to detect downstream.

At the same time, overly rigid processes can lead to drop-offs or customer dissatisfaction—especially in a competitive market like Singapore where fintech players offer quick and seamless onboarding experiences.

This is where AML onboarding software plays a key role.

What is AML Onboarding Software?

AML onboarding software is designed to automate and enhance the customer due diligence (CDD) and Know Your Customer (KYC) processes during the initial stages of client engagement. It combines data collection, risk scoring, screening, and workflow automation to help financial institutions:

• Verify identities
• Assess customer risk
• Detect suspicious behaviour early
• Comply with MAS and FATF regulations
• Ensure auditability and reporting readiness

This software acts as a digital gatekeeper, helping teams detect red flags before a single transaction takes place.

Key Features of an Effective AML Onboarding Solution

Here’s what the best AML onboarding platforms bring to the table:

1. Dynamic Risk Profiling

Customers are assigned risk scores based on multiple factors—geographic exposure, occupation, product usage, and more. This helps tailor ongoing due diligence requirements.

2. Seamless Integration with Screening Tools

The onboarding software should be able to screen applicants in real-time against sanctions lists, politically exposed person (PEP) lists, and adverse media.

3. Intelligent Document Verification

Advanced systems offer biometric matching, liveness detection, and AI-based document parsing to reduce fraud and manual work.

4. Straight-Through Processing

Low-risk applicants should move through the system quickly with minimal friction, while high-risk cases are routed for enhanced due diligence.

5. Centralised Audit Trails

Every decision—approval, escalation, or rejection—should be logged for compliance and future investigations.

6. Local Regulatory Alignment

In Singapore, onboarding systems must comply with MAS AML Notices (e.g., Notice 626, PSN01), including requirements for non-face-to-face verification, ID recordkeeping, and high-risk country checks.

Common Onboarding Pitfalls to Avoid

Even the most promising compliance programmes can be derailed by poor onboarding. Here are a few common traps:

• Over-reliance on manual checks leading to delays
• Lack of integration between risk scoring and screening tools
• No visibility into onboarding drop-off points
• Inability to adapt due diligence levels based on real-time risk

The right AML onboarding software helps mitigate these issues from day one.

Use Case: Strengthening Digital Onboarding in a Singaporean Digital Bank

A mid-sized digital bank in Singapore faced challenges in balancing fast customer onboarding with the risk of synthetic identities and mule accounts. They implemented an AML onboarding solution that offered:

• Real-time screening against global watchlists
• Adaptive risk scoring based on customer behaviour
• Biometric ID checks for non-face-to-face verification
• Integration with their transaction monitoring system

The outcome? A 40% reduction in onboarding time, 60% fewer false positives during initial checks, and stronger regulatory audit readiness.

How Tookitaki Enhances the AML Onboarding Lifecycle

Tookitaki’s FinCense platform powers seamless onboarding with intelligent compliance baked in from the start.

While not a KYC identity verification tool, FinCense supports onboarding teams by:

• Providing a dynamic risk profile that connects to transaction behaviour
• Ingesting typologies and red flags from the AFC Ecosystem to detect unusual patterns early
• Enabling real-time alerting if onboarding-linked accounts behave abnormally in the first days of activity
• Strengthening case management with cross-functional visibility across onboarding and monitoring

This approach ensures that high-risk profiles are not only flagged early but also monitored in context post-onboarding.

Best Practices When Selecting AML Onboarding Software

1. Choose a vendor that offers local support and understands MAS regulatory requirements.
2. Prioritise explainability—your team should understand why a customer was flagged.
3. Ensure seamless integration with other AML systems like transaction monitoring, case management, and reporting.
4. Look for scalability so the system can grow with your business and adapt to new typologies.

Future Outlook: The Onboarding Battleground

As Singapore continues its push for digitalisation, from e-wallets to neobanks, the onboarding experience is becoming a competitive differentiator. Yet compliance cannot be compromised.

The future of AML onboarding lies in:

• Greater use of AI to detect synthetic identities
• Network-level intelligence to prevent mule account onboarding
• Real-time fraud and AML orchestration from day one

Institutions that invest in smart onboarding software today will be better equipped to fight financial crime tomorrow.

Conclusion: First Impressions That Last

Onboarding is no longer just a formality—it’s your first line of defence. With the right AML onboarding software, Singapore’s financial institutions can deliver frictionless user experiences while staying fully compliant.

It’s not about choosing between speed and security—it’s about choosing both.

Anti money laundering and compliance are often spoken as one idea, but treating them as the same function is one of the most common mistakes financial institutions make.

Introduction

In boardrooms, audit meetings, and regulatory discussions, the terms anti money laundering and compliance are often used interchangeably. AML compliance. Compliance controls. Regulatory AML. The language blends together so naturally that the distinction is rarely questioned.

Yet inside financial institutions, AML and compliance play different roles, fail in different ways, and require different capabilities to function well.

Understanding the difference between anti money laundering and compliance is not a matter of semantics. It is fundamental to how banks manage financial crime risk, design controls, allocate resources, and respond to regulators. When the two are treated as the same thing, gaps appear. When they are understood as complementary but distinct, institutions gain clarity and control.

This blog breaks down what anti money laundering and compliance each actually mean in practice, where they intersect, where they diverge, and why mature institutions design for both rather than collapsing them into one concept.

Why AML and Compliance Are So Often Confused

There are several reasons why AML and compliance are routinely blended together.

First, anti money laundering obligations are enforced through regulatory compliance. Banks must comply with AML laws, guidance, and supervisory expectations. This naturally links AML activity to the compliance function.

Second, AML teams often sit within compliance departments. Organisational charts reinforce the idea that AML is simply a subset of compliance.

Third, regulatory language frequently refers to AML compliance rather than distinguishing between detection, prevention, governance, and oversight.

While understandable, this conflation creates blind spots.

What Anti Money Laundering Actually Does

Anti money laundering is fundamentally about detecting and disrupting illicit financial activity.

In practice, AML focuses on:

• Identifying suspicious behaviour
• Detecting laundering typologies
• Understanding how illicit funds move
• Investigating unusual activity
• Escalating and reporting genuine risk

AML is operational by nature. It deals with transactions, behaviour, patterns, and decisions made under uncertainty.

An AML function asks questions such as:

• Does this activity make sense given what we know about the customer
• Is this behaviour consistent with known laundering techniques
• Is there a reasonable suspicion that funds are linked to crime

AML is about risk discovery and response.

What Compliance Actually Does

Compliance serves a different purpose.

Compliance is about ensuring the institution operates within regulatory expectations and can demonstrate that fact when required.

In practice, compliance focuses on:

• Policies and procedures
• Governance frameworks
• Control design and documentation
• Oversight and assurance
• Regulatory engagement
• Evidence and auditability

A compliance function asks questions such as:

• Do we have appropriate controls in place
• Are those controls documented and approved
• Are they being followed consistently
• Can we demonstrate this to regulators

Compliance is about control assurance and accountability.

The Core Difference in One Sentence

Anti money laundering is about finding and responding to financial crime risk.
Compliance is about proving that the institution’s controls are appropriate and effective.

They are related, but they are not the same.

Where AML and Compliance Intersect

AML and compliance intersect constantly, which is why alignment matters.

Regulatory obligations

AML laws create compliance requirements. Institutions must show that their AML controls meet regulatory standards.

Suspicious matter reporting

AML teams identify suspicious activity. Compliance frameworks ensure reporting is timely, accurate, and auditable.

Risk based approaches

AML identifies risk. Compliance ensures controls are proportionate to that risk and documented accordingly.

Governance

AML outcomes inform governance discussions. Compliance provides the structure through which governance operates.

When AML and compliance work in harmony, institutions gain both detection strength and regulatory confidence.

Where AML and Compliance Commonly Drift Apart

Problems arise when the distinction between AML and compliance is ignored.

Compliance without effective AML

Some institutions focus heavily on policies, checklists, and documentation while underlying detection quality remains weak. On paper, controls exist. In practice, risk goes unnoticed.

AML without compliance discipline

Other institutions detect risk effectively but struggle to explain decisions, maintain consistency, or satisfy regulatory scrutiny.

Box ticking culture

When AML is treated purely as a compliance obligation, teams focus on satisfying requirements rather than understanding risk.

Operational fatigue

AML analysts overloaded with false positives may meet procedural compliance requirements while missing genuine threats.

These gaps often only surface during regulatory reviews or post incident investigations.

How Misalignment Shows Up in Real Institutions

Misalignment between anti money laundering and compliance often reveals itself through familiar symptoms.

• High alert volumes with low quality outcomes
• Inconsistent investigation decisions
• Difficulty explaining why alerts were triggered
• Weak linkage between risk assessments and controls
• Regulatory findings that reference process failures rather than intent

These issues are rarely caused by lack of effort. They are structural problems.

What Mature Institutions Do Differently

Institutions with strong AML and compliance outcomes treat them as distinct but interconnected capabilities.

Clear role definition

AML teams focus on detection, investigation, and typology understanding. Compliance teams focus on governance, assurance, and regulatory engagement.

Shared language

Risk concepts, thresholds, and rationales are aligned so that AML decisions can be explained within compliance frameworks.

Feedback loops

Compliance findings inform AML improvements. AML insights inform compliance control design.

Technology alignment

Systems support both operational detection and compliance oversight without forcing one to compromise the other.

This balance is difficult to achieve, but essential.

The Role of Technology in Bridging AML and Compliance

Technology often sits at the centre of the AML and compliance relationship.

Poorly designed systems create friction. Strong platforms create alignment.

Effective AML technology helps by:

• Providing explainable detection logic
• Maintaining clear audit trails
• Supporting consistent investigations
• Enabling oversight without slowing operations
• Translating operational decisions into compliance evidence

Technology does not eliminate the need for judgement, but it determines how visible and defensible that judgement becomes.

Why Regulators Care About the Difference

Regulators are not only interested in whether suspicious matters are reported. They are interested in how institutions arrive at decisions.

Regulatory expectations increasingly focus on:

• Risk based reasoning
• Control effectiveness
• Consistency of outcomes
• Governance accountability

When AML and compliance are blurred together, institutions struggle to articulate this reasoning clearly.

Australia Specific Considerations

In Australia, expectations around anti money laundering and compliance continue to evolve.

Institutions are expected to:

• Understand emerging typologies such as scam driven laundering
• Apply proportional controls based on real risk
• Demonstrate clear governance over AML systems
• Maintain strong documentation and oversight

This environment makes alignment between AML and compliance more important than ever.

For community owned institutions such as Regional Australia Bank, the challenge is achieving this alignment with lean teams and limited tolerance for inefficiency.

Common Mistakes to Avoid

Several mistakes repeatedly undermine AML and compliance effectiveness.

Treating AML as paperwork

This weakens detection and creates false confidence.

Treating compliance as an obstacle

This leads to poor documentation and regulatory exposure.

Over engineering controls

Excessive complexity increases failure points.

Ignoring operational feedback

Analyst experience often highlights control weaknesses before audits do.

Avoiding these mistakes requires deliberate design.

How Institutions Can Align AML and Compliance More Effectively

Alignment does not require restructuring overnight. It requires focus.

Start with shared risk understanding

Ensure AML risk assessments genuinely inform compliance controls.

Design controls around real behaviour

Avoid theoretical frameworks disconnected from operational reality.

Prioritise explainability

Decisions should be understandable to analysts, auditors, and regulators alike.

Use technology as an enabler

Systems should connect detection, investigation, and oversight seamlessly.

Review continuously

Alignment is not static. It evolves as risk evolves.

Where Tookitaki Fits in This Conversation

Tookitaki approaches anti money laundering and compliance as complementary capabilities that must work together.

Through its FinCense platform, institutions can:

• Detect behaviour driven risk more effectively
• Maintain clear and explainable decision logic
• Support consistent investigations
• Generate audit ready evidence
• Align operational AML outcomes with compliance expectations

This helps institutions strengthen both detection quality and regulatory defensibility without forcing one to dominate the other.

The Future of Anti Money Laundering and Compliance

The future points toward greater integration, not greater confusion.

Key trends include:

• More intelligence led AML detection
• Stronger emphasis on accountability and explainability
• Technology that supports both operations and oversight
• Closer collaboration between AML and compliance teams

Institutions that recognise the difference between anti money laundering and compliance, and design accordingly, will be better positioned to manage risk and regulatory change.

Conclusion

Anti money laundering and compliance are deeply connected, but they are not the same thing. One discovers risk. The other ensures accountability. One is operational. The other is structural.

When institutions blur the distinction, they weaken both. When they respect it, align it, and design for it, they create stronger controls, clearer decisions, and greater regulatory confidence.

In an increasingly complex financial crime landscape, understanding this difference is no longer optional. It is foundational to sustainable, effective risk management.