Automated Transaction Monitoring in Singapore: Smarter, Faster, and Built for Today’s Risks
Manual checks won’t catch a real-time scam. But automated transaction monitoring just might.
As Singapore’s financial ecosystem continues to embrace digital payments and instant transfers, the window for spotting suspicious activity is shrinking. Criminals are getting faster, and compliance teams are under pressure to keep up. That’s where automated transaction monitoring steps in — replacing slow, manual processes with real-time intelligence and AI-powered detection.
In this blog, we’ll break down how automated transaction monitoring works, why it’s essential for banks and fintechs in Singapore, and how modern platforms are transforming AML operations from reactive to proactive.

What Is Automated Transaction Monitoring?
Automated transaction monitoring refers to technology systems that analyse customer transactions in real time or near real time to detect signs of money laundering, fraud, or other suspicious activity. These systems work by applying pre-set rules, typologies, or machine learning models to transaction data, triggering alerts when unusual or high-risk patterns are found.
Key use cases:
- Monitoring for structuring and layering
- Detecting transactions with sanctioned jurisdictions
- Identifying mule account flows
- Tracking cross-border movement of illicit funds
- Flagging high-risk behavioural deviations
Why Singapore Needs Automated Monitoring More Than Ever
Singapore’s high-speed payments infrastructure — including PayNow, FAST, and widespread mobile banking — has made it easier than ever for funds to move quickly. This is great for users, but it also creates challenges for compliance teams trying to spot laundering in motion.
Current pressures include:
- Real-time payment schemes that leave no room for slow investigations
- Layering of illicit funds through fintech platforms and e-wallets
- Use of shell companies and nominee directors to hide ownership
- Cross-border mules linked to scams and cyber-enabled fraud
- Regulatory push for faster STR filing and risk-based escalation
Automated transaction monitoring is now essential to meet both operational and regulatory expectations.
How Automated Transaction Monitoring Works
1. Data Ingestion
The system pulls transaction data from core banking systems, payment gateways, and other sources. This may include amount, time, device, channel, location, and more.
2. Rule or Scenario Application
Predefined rules or typologies are applied. For example:
- Flag all transactions above SGD 10,000 from high-risk countries
- Flag multiple small transactions structured to avoid reporting limits
- Alert on sudden account activity after months of dormancy
3. AI/ML Scoring (Optional)
Advanced systems apply machine learning to assess the overall risk of the transaction or customer in real time.
4. Alert Generation
If a transaction matches a risk scenario or exceeds thresholds, the system creates an alert, which flows into case management.
5. Investigation and Action
Analysts review alerts, investigate patterns, and decide on next steps — escalate, file STR, or close as a false positive.
Benefits of Automated Transaction Monitoring
✅ Real-Time Risk Detection
Identify and block suspicious transfers before they’re completed.
✅ Faster Alert Handling
Eliminates the need for manual reviews of every transaction, freeing up analyst time.
✅ Reduced False Positives
Modern systems learn from past decisions to avoid triggering unnecessary alerts.
✅ Compliance Confidence
Supports MAS expectations for timeliness, accuracy, and explainability.
✅ Scalability
Can handle growing transaction volumes without increasing headcount.
Must-Have Features for Singapore-Based Institutions
To be effective in the Singapore market, an automated transaction monitoring system should include:
1. Real-Time Monitoring Engine
Delays mean missed threats. Look for solutions that can process and flag transactions within seconds across digital and physical channels.
2. Dynamic Risk Scoring
Every transaction should be assessed in context, using:
- Historical behaviour
- Customer profile
- External data (e.g., sanctions, adverse media)
3. Scenario-Based Detection
Beyond simple thresholds, the system should support typologies based on real-world money laundering methods in Singapore and Southeast Asia.
Common examples:
- Pass-through layering via utility platforms
- QR code-enabled scam payments
- Cross-border fund transfers to newly created shell firms
4. AI and Machine Learning
Advanced systems use AI to:
- Identify previously unknown risk patterns
- Score alerts by urgency and likelihood
- Continuously improve detection quality
5. Investigation Workflows
Once an alert is raised, analysts should be able to:
- View customer and transaction history
- Add notes and attachments
- Escalate or close the alert with audit logs
6. GoAML-Compatible Reporting
For STR filing, the system should:
- Auto-generate STRs based on alert data
- Track internal approvals
- Submit directly to MAS GoAML or export in supported formats
7. Simulation and Tuning
Before pushing new rules live, simulation tools help test how many alerts will be triggered, allowing teams to optimise thresholds.
8. Explainable Outputs
Alerts should include clear reasoning so investigators and auditors can understand why they were triggered.

Challenges with Manual or Legacy Monitoring
Many institutions still rely on outdated or semi-automated systems. These setups often:
- Generate high volumes of false positives
- Cannot detect new laundering typologies
- Delay STR filings due to manual investigation backlogs
- Lack scalability as transaction volume increases
- Struggle with audit readiness and explainability
In a regulatory environment like Singapore’s, these gaps lead to increased risk exposure and operational inefficiencies.
How Tookitaki’s FinCense Platform Enables Automated Transaction Monitoring
Tookitaki’s FinCense is a modern AML solution designed for Singapore’s evolving needs. Its automated transaction monitoring engine combines AI, scenario-based logic, and regional intelligence to deliver precision and speed.
Here’s how it works:
1. Typology-Based Detection with AFC Ecosystem Integration
FinCense leverages over 200 AML typologies contributed by experts across Asia through the AFC Ecosystem.
This helps institutions detect threats like:
- Scam proceeds routed via mules
- Crypto-linked layering attempts
- Synthetic identity fraud patterns
2. Modular AI Agents
FinCense uses an Agentic AI framework with specialised agents for:
- Alert generation
- Prioritisation
- Investigation
- STR filing
Each agent is optimised for accuracy, performance, and transparency.
3. Smart Investigation Tools
FinMate, the AI copilot, supports analysts by:
- Summarising risk factors
- Highlighting key transactions
- Suggesting likely typologies
- Drafting STR summaries in plain language
4. MAS-Ready Compliance Features
FinCense includes:
- GoAML-compatible STR submission
- Audit trails for every alert and decision
- Model testing and validation tools
- Explainable AI that aligns with MAS Veritas principles
5. Simulation and Performance Monitoring
Before changes go live, FinCense allows teams to simulate rule impact, reduce noise, and optimise thresholds — all in a controlled environment.
Success Metrics from Institutions Using FinCense
Banks and fintechs in Singapore using FinCense have seen:
- 65 percent reduction in false positives
- 3x faster investigation workflows
- Improved regulatory audit outcomes
- Stronger typology coverage and detection precision
- Happier, less overworked compliance teams
Checklist: Is Your Transaction Monitoring System Keeping Up?
Ask your team:
- Are you detecting suspicious activity in real time?
- Can your system adapt quickly to new laundering methods?
- Are your alerts prioritised by risk or reviewed manually?
- Do analysts have investigation tools at their fingertips?
- Is your platform audit-ready and MAS-compliant?
- Are STRs automated or still manually compiled?
If you're unsure about two or more of these, it may be time for an upgrade.
Conclusion: Automation Is Not the Future — It’s the Minimum
In Singapore’s high-speed financial environment, automated transaction monitoring is no longer a nice-to-have. It’s the bare minimum for staying compliant, competitive, and customer-trusted.
Solutions like Tookitaki’s FinCense deliver more than automation. They provide intelligence, adaptability, and explainability — all backed by a community of experts contributing real-world insights into the AFC Ecosystem.
If your compliance team is drowning in manual reviews and outdated alerts, now is the time to let automation take the lead.
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Top AML Scenarios in ASEAN

The Role of AML Software in Compliance

The Role of AML Software in Compliance


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AML Vendors in Australia: How to Choose the Right Partner in a Rapidly Evolving Compliance Landscape
The AML vendor market in Australia is crowded, complex, and changing fast. Choosing the right partner is now one of the most important decisions a bank will make.
Introduction: A New Era of AML Choices
A decade ago, AML technology buying was simple. Banks picked one of a few rule-based systems, integrated it into their core banking environment, and updated thresholds once a year. Today, the landscape looks very different.
Artificial intelligence, instant payments, cross-border digital crime, APRA’s renewed focus on resilience, and AUSTRAC’s expectations for explainability are reshaping how banks evaluate AML vendors.
The challenge is no longer finding a system that “works”.
It is choosing a partner who can evolve with you.
This blog takes a fresh, practical, and Australian-specific look at the AML vendor ecosystem, what has changed, and what institutions should consider before committing to a solution.

Part 1: Why the AML Vendor Conversation Has Changed
The AML market globally has expanded rapidly, but Australia is experiencing something unique:
a shift from traditional rule-based models to intelligent, adaptive, and real-time compliance ecosystems.
Several forces are driving this change:
1. The Rise of Instant Payments
The New Payments Platform (NPP) introduced unprecedented settlement speed, compressing the investigation window from hours to minutes. Vendors must support real-time analysis, not batch-driven monitoring.
2. APRA’s Renewed Focus on Operational Resilience
Under CPS 230 and CPS 234, vendors are no longer just technology providers.
They are part of a bank’s risk ecosystem.
3. AUSTRAC’s Expectations for Transparency
Explainability is becoming non-negotiable. Vendors must show how their scenarios work, why alerts fire, and how models behave.
4. Evolving Criminal Behaviour
Human trafficking, romance scams, mule networks, synthetic identities.
Typologies evolve weekly.
Banks need vendors who can adapt quickly.
5. Pressure to Lower False Positives
Australian banks carry some of the highest alert volumes relative to population size.
Vendor intelligence matters more than ever.
The result:
Banks are no longer choosing AML software. They are choosing long-term intelligence partners.
Part 2: The Three Types of AML Vendors in Australia
The market can be simplified into three broad categories. Understanding them helps decision-makers avoid mismatches.
1. Legacy Rule-Based Platforms
These systems have existed for 10 to 20 years.
Strengths
- Stable
- Well understood
- Large enterprise deployments
Limitations
- Hard-coded rules
- Minimal adaptation
- High false positives
- Limited intelligence
- High cost of tuning
- Not suitable for real-time payments
Best for
Institutions with low transaction complexity, limited data availability, or a need for basic compliance.
2. Hybrid Vendors (Rules + Limited AI)
These providers add basic machine learning on top of traditional systems.
Strengths
- More flexible than legacy tools
- Some behavioural analytics
- Good for institutions transitioning gradually
Limitations
- Limited explainability
- AI add-ons, not core intelligence
- Still rule-heavy
- Often require large tuning projects
Best for
Mid-sized institutions wanting incremental improvement rather than transformation.
3. Intelligent AML Platforms (Native AI + Federated Insights)
This is the newest category, dominated by vendors who built systems from the ground up to support modern AML.
Strengths
- Built for real-time detection
- Adaptive models
- Explainable AI
- Collaborative intelligence capabilities
- Lower false positives
- Lighter operational load
Limitations
- Requires cultural readiness
- Needs better-quality data inputs
- Deeper organisational alignment
Best for
Banks seeking long-term AML maturity, operational scale, and future-proofing.
Australia is beginning to shift from Category 1 and 2 into Category 3.
Part 3: What Australian Banks Actually Want From AML Vendors in 2025
Interviews and discussions across risk and compliance teams reveal a pattern.
Banks want vendors who can deliver:
1. Real-time capabilities
Batch-based monitoring is no longer enough.
AML must keep pace with instant payments.
2. Explainability
If a model cannot explain itself, AUSTRAC will ask the institution to justify it.
3. Lower alert volumes
Reducing noise is as important as identifying crime.
4. Consistency across channels
Customers interact through apps, branches, wallets, partners, and payments.
AML cannot afford blind spots.
5. Adaptation without code changes
Vendors should deliver new scenarios, typologies, and thresholds without major uplift.
6. Strong support for small and community banks
Institutions like Regional Australia Bank need enterprise-grade intelligence without enterprise complexity.
7. Clear model governance dashboards
Banks want to see how the system performs, evolves, and learns.
8. A vendor who listens
Compliance teams want partners who co-create, not providers who supply static software.
This is why intelligent, collaborative platforms are rapidly becoming the new default.

Part 4: Questions Every Bank Should Ask an AML Vendor
This is the operational value section. It differentiates your blog immediately from generic AML vendor content online.
1. How fast can your models adapt to new typologies?
If the answer is “annual updates”, the vendor is outdated.
2. Do you support Explainable AI?
Regulators will demand transparency.
3. What are your false positive reduction metrics?
If the vendor cannot provide quantifiable improvements, be cautious.
4. How much of the configuration can we control internally?
Banks should not rely on vendor teams for minor updates.
5. Can you support real-time payments and NPP flows?
A modern AML platform must operate at NPP speed.
6. How do you handle federated learning or collective intelligence?
This is the modern competitive edge.
7. What does model drift detection look like?
AML intelligence must stay current.
8. Do analysts get contextual insights, or only alerts?
Context reduces investigation time dramatically.
9. How do you support operational resilience under CPS 230?
This is crucial for APRA-regulated banks.
10. What does onboarding and migration look like?
Banks want smooth transitions, not 18-month replatforming cycles.
Part 5: How Tookitaki Fits Into the AML Vendor Landscape
A Different Kind of AML Vendor
Tookitaki does not position itself as another monitoring system.
It sees AML as a collective intelligence challenge where individual banks cannot keep up with evolving financial crime by fighting alone.
Three capabilities make Tookitaki stand out in Australia:
1. Intelligence that learns from the real world
FinCense is built on a foundation of continuously updated scenario intelligence contributed by a network of global compliance experts.
Banks benefit from new behaviour patterns long before they appear internally.
2. Agentic AI that helps investigators
Instead of just generating alerts, Tookitaki introduces FinMate, a compliance investigation copilot that:
- Surfaces insights
- Suggests investigative paths
- Speeds up decision-making
- Reduces fatigue
- Improves consistency
This turns investigators into intelligence analysts, not data processors.
3. Federated learning that keeps data private
The platform learns from patterns across multiple banks without sharing customer data.
This gives institutions the power of global insight with the privacy of isolated systems.
Why this matters for Australian banks
- Supports real-time monitoring
- Reduces alert volumes
- Strengthens APRA CPS 230 alignment
- Provides explainability for AUSTRAC audits
- Offers a sustainable operational model for small and large banks
It is not just a vendor.
It is the trust layer that helps institutions outpace financial crime.
Part 6: The Future of AML Vendors in Australia
The AML vendor landscape is shifting from “who has the best rules” to “who has the best intelligence”. Here’s what the future looks like:
1. Dynamic intelligence networks
Static rules will fade away.
Networks of shared insights will define modern AML.
2. AI-driven decision support
Analysts will work alongside intelligent copilots, not alone.
3. No-code scenario updates
Banks will update scenarios like mobile apps, not system upgrades.
4. Embedded explainability
Every alert will come with narrative, not guesswork.
5. Real-time everything
Monitoring, detection, response, audit readiness.
6. Collaborative AML ecosystems
Banks will work together, not in silos.
Tookitaki sits at the centre of this shift.
Conclusion
Choosing an AML vendor in Australia is no longer a procurement decision.
It is a strategic one.
Banks today need partners who deliver intelligence, not just infrastructure.
They need transparency for AUSTRAC, resilience for APRA, and scalability for NPP.
They need technology that empowers analysts, not overwhelms them.
As the landscape continues to evolve, institutions that choose adaptable, explainable, and collaborative AML platforms will be future-ready.
The future belongs to vendors who learn faster than criminals.
And the banks who choose them wisely.

Fraud Detection System: How Malaysia Can Stay One Step Ahead of Digital Crime
As Malaysia’s financial system goes digital, fraud detection systems are becoming the silent guardians of consumer trust.
Malaysia’s Expanding Fraud Challenge
Malaysia is experiencing a digital transformation unlike anything seen before. QR payments, e-wallets, instant transfers, digital banks, and cross-border digital commerce have rapidly become part of everyday life.
Innovation has brought convenience, but it has also enabled a wave of sophisticated financial fraud. Criminal networks are using faster payment channels, deep social engineering, and large mule networks to steal and move funds before victims or institutions can react.
The Royal Malaysia Police, Bank Negara Malaysia (BNM), and cybersecurity agencies have consistently flagged the rise in:
- Online investment scams
- E-wallet fraud
- Account takeover attacks
- Romance scams
- Cross-border mule operations
- Deepfake-enabled fraud
- Social engineering targeting retirees and gig workers
Fraud not only causes financial loss but also erodes public trust in digital banking and fintech. As Malaysia accelerates toward a cashless society, the need for intelligent, proactive fraud detection has become a national priority.
This is where the evolution of the fraud detection system becomes central to protecting financial integrity.

What Is a Fraud Detection System?
A fraud detection system is a technology platform that identifies, prevents, and responds to fraudulent financial activity. It analyses millions of transactions, user behaviours, and contextual signals to detect anomalies that indicate fraud.
Modern fraud detection systems protect institutions against:
- Identity theft
- Transaction fraud
- Synthetic identities
- First-party fraud
- Friendly fraud
- Card-not-present attacks
- Social engineering scams
- Mule account activity
- False merchant onboarding
In Malaysia’s dynamic financial ecosystem, the fraud detection system acts as a real-time surveillance layer safeguarding both institutions and consumers.
How a Fraud Detection System Works
A powerful fraud detection system operates through a sequence of intelligent steps.
1. Data Collection
The system gathers data from multiple sources including payment platforms, device information, customer profiles, login behaviour, and transaction history.
2. Behavioural Analysis
Models recognise normal behavioural patterns and build a baseline for each user, device, or merchant.
3. Anomaly Detection
Any deviation from expected behaviour triggers deeper analysis. This includes unusual spending, unknown device access, rapid transactions, or location mismatches.
4. Risk Scoring
Each action or transaction receives a risk score based on probability of fraud.
5. Real-Time Decisioning
The system performs instant checks to accept, challenge, or block the activity.
6. Investigation and Feedback Loop
Alerts are routed to investigators who confirm whether a case is fraud. This feedback retrains machine learning models for higher accuracy.
Fraud detection systems are not static rule engines. They are continuously learning frameworks that adapt to new threats with every case reviewed.
Why Legacy Fraud Systems Fall Short
Despite increased digital adoption, many Malaysian financial institutions still use traditional fraud monitoring tools that struggle to keep pace with modern threats.
Here is where these systems fail:
- Static rule sets cannot detect emerging patterns like deepfake impersonation or mule rings.
- Slow investigation workflows allow fraudulent funds to leave the ecosystem before action can be taken.
- Limited visibility across channels results in blind spots between digital banking, cards, and payment rails.
- High false positives disrupt genuine customers and overwhelm analysts.
- Siloed AML and fraud systems prevent institutions from seeing fraud proceeds that transition into money laundering.
Fraud today is dynamic, distributed, and data driven. Systems built more than a decade ago cannot protect a modern, hyperconnected financial environment.
The Rise of AI-Powered Fraud Detection Systems
Artificial intelligence has transformed fraud detection into a predictive science. AI-powered fraud systems bring a level of intelligence and speed that traditional systems cannot match.
1. Machine Learning for Pattern Recognition
Models learn from millions of past transactions to identify subtle fraud behaviour, even if it has never been seen before.
2. Behavioural Biometrics
AI analyses keystroke patterns, time on page, navigation flow, and device characteristics to distinguish legitimate users from attackers.
3. Real-Time Detection
AI systems analyse risk instantly, giving institutions crucial seconds to block or hold suspicious activity.
4. Lower False Positives
AI reduces unnecessary alerts by understanding context, not just rules.
5. Autonomous Detection and Triage
AI systems prioritise high-risk alerts and automate repetitive tasks, freeing investigators to focus on complex threats.
AI-powered systems do not simply detect fraud. They help institutions anticipate it.
Why Malaysia Needs Next-Generation Fraud Detection
Fraud in Malaysia is no longer isolated to simple scams. Criminal networks have become highly organised, using advanced technologies and exploiting digital loopholes.
Malaysia faces increasing risks from:
- QR laundering through DuitNow
- Instant pay-and-transfer fraud
- Cross-border mule farming
- Scams operated from foreign syndicate hubs
- Cryptocurrency-linked laundering
- Fake merchant setups
- Fast layering to offshore accounts
These patterns require solutions that recognise behaviour, understand typologies, and react in real time. This is why modern fraud detection systems integrated with AI are becoming essential for Malaysian risk teams.
Tookitaki’s FinCense: Malaysia’s Most Advanced Fraud Detection System
At the forefront of AI-driven fraud prevention is Tookitaki’s FinCense, an end-to-end platform built to detect and prevent both fraud and money laundering. It is used by leading banks and fintechs across Asia-Pacific and is increasingly recognised as the trust layer to fight financial crime.
FinCense is built on four pillars that make it uniquely suited to Malaysia’s digital economy.
1. Agentic AI for Faster, Smarter Investigations
FinCense uses intelligent autonomous agents that perform tasks such as alert triage, pattern clustering, narrative generation, and risk explanation.
These agents work around the clock, giving compliance teams:
- Faster case resolution
- Higher accuracy
- Better prioritisation
- Clear decision support
This intelligent layer allows teams to handle high volumes of fraud alerts without burning out or missing critical risks.
2. Federated Intelligence Through the AFC Ecosystem
Fraud patterns often emerge in one market before appearing in another. FinCense connects to the Anti-Financial Crime (AFC) Ecosystem, a collaborative intelligence network of institutions across ASEAN.
Through privacy-preserving federated learning, models benefit from:
- Regional typologies
- New scam patterns
- Real-time cross-border trends
- Behavioural signatures of mule activity
This gives Malaysian institutions early visibility into fraud patterns seen in Singapore, the Philippines, Indonesia, and Thailand.
3. Explainable AI for Trust and Compliance
Regulators expect not just accuracy but clarity. FinCense generates explanations for every flagged event, detailing the data points and logic used in the decision.
This ensures:
- Full transparency
- Audit readiness
- Confidence in automated decisions
- Better regulatory communication
Explainability is essential for AI adoption, and FinCense is designed to meet these expectations.
4. Unified Fraud and AML Detection
Fraud often transitions into money laundering. FinCense unifies fraud detection and AML transaction monitoring into one decisioning platform. This allows teams to:
- Connect fraud events to laundering flows
- Detect mule activity linked to scams
- Analyse both behavioural and transactional trends
- Break criminal networks instead of individual incidents
This unified view creates a powerful defence that legacy siloed systems cannot match.

Real-World Scenario: Detecting Cross-Border Investment Fraud
Consider a popular scam trend. Victims in Malaysia receive calls or WhatsApp messages promising high returns through offshore trading platforms. They deposit funds into mule accounts linked to foreign syndicates.
Here is how FinCense detects and disrupts this:
- The system identifies unusual inbound deposits from unrelated senders.
- Behavioural analysis detects rapid movement of funds between multiple local accounts.
- Federated intelligence matches this behaviour with similar typologies in Singapore and Hong Kong.
- Agentic AI generates a complete case narrative summarising:
- Transaction velocity
- Peer network connections
- Device and login anomalies
- Similar scenarios seen in the region
- The institution blocks the outbound transfer, freezes the account, and prevents losses.
This entire process occurs within minutes, a speed that traditional systems cannot match.
Benefits for Malaysian Financial Institutions
Deploying an AI-powered fraud detection system like FinCense has measurable impact.
- Significant reduction in false positives
- Faster alert resolution times
- Better protection for vulnerable customers
- Higher detection accuracy
- Lower operational costs
- Improved regulator trust
- Better customer experience
Fraud prevention shifts from reactive defence to proactive risk management.
Key Features to Look for in a Modern Fraud Detection System
Financial institutions evaluating fraud systems should prioritise five core capabilities.
1. Intelligence and adaptability
Systems must evolve with new fraud trends and learn continuously.
2. Contextual and behavioural detection
Instead of relying solely on rules, solutions should use behavioural analytics to understand intent.
3. Real-time performance
Fraud moves in seconds. Systems must react instantly.
4. Explainability
Every alert should be transparent and justified for regulatory confidence.
5. Collaborative intelligence
Systems must learn from regional behaviour, not just local data.
FinCense checks all these boxes and provides additional advantages through unified fraud and AML detection.
The Future of Fraud Detection in Malaysia
Malaysia is on a clear path toward a safer digital financial ecosystem. The next phase of fraud detection will be shaped by several emerging trends:
- Open banking data sharing enabling richer identity verification
- Real-time AI models trained on regional intelligence
- Deeper collaboration between banks, fintechs, and regulators
- Human-AI partnerships integrating expertise and computational power
- Unified financial crime platforms merging AML, fraud, and sanctions for complete visibility
Malaysia’s forward-looking regulatory environment positions the country as a leader in intelligent fraud prevention across ASEAN.
Conclusion
Fraud detection is no longer a standalone function. It is the heartbeat of trust in Malaysia’s digital financial future. As criminals innovate faster and exploit new technologies, institutions must adopt tools that can outthink, outpace, and outmanoeuvre sophisticated fraud networks.
Tookitaki’s FinCense stands as the leading fraud detection system built for Malaysia. It blends Agentic AI, federated intelligence, and explainable models to create real-time, transparent, and regionally relevant protection.
By moving from static rules to collaborative intelligence, Malaysia’s financial institutions can stay one step ahead of digital crime and build a safer future for every consumer.

What Is APRA? A Simple Guide to Australia’s Banking Regulator
If you live, work, or bank in Australia, your financial safety is protected by an agency you may not know well: APRA.
Introduction
Most Australians interact with banks every day without ever thinking about the rules and systems that keep the financial sector stable. Behind the scenes, one regulator plays a critical role in ensuring banks are safe, resilient, and well managed: the Australian Prudential Regulation Authority, better known as APRA.
APRA oversees the health of the financial system, ensuring that banks, credit unions, insurers, and superannuation funds operate responsibly. While AUSTRAC focuses on preventing money laundering and financial crime, APRA focuses on stability, governance, risk, and long-term protection.
In a fast-changing financial world, understanding APRA is becoming increasingly important for businesses, compliance teams, fintechs, and even everyday consumers.
This simple guide explains what APRA does, who it regulates, and why its work matters.

What Does APRA Stand For?
APRA stands for the Australian Prudential Regulation Authority.
The term “prudential regulation” refers to the rules and oversight that ensure financial institutions remain safe, stable, and financially sound. That means APRA’s job is to make sure financial organisations can weather risks, protect customer deposits, and operate sustainably.
Why Was APRA Created?
APRA was formed in 1998 following major reforms to Australia’s financial regulatory system. These reforms recognised the need for a dedicated agency to supervise the financial health of institutions.
APRA’s creation brought together prudential functions from:
- The Reserve Bank of Australia
- The Insurance and Superannuation Commission
The goal was simple: Protect customers and promote a stable financial system.
What Organisations Does APRA Regulate?
APRA supervises institutions that hold and manage Australians’ money. These include:
1. Banks and Authorised Deposit-Taking Institutions (ADIs)
- Major banks
- Regional and community-owned banks
- Credit unions
- Building societies
- Digital banks
2. Insurance Companies
- Life insurers
- General insurers
- Private health insurers
3. Superannuation Funds
- Retail, industry, corporate, and public sector funds
4. Some Non-Bank Financial Institutions
Entities that hold financial risk but are not traditional banks.
In total, APRA oversees more than 600 financial institutions that collectively hold trillions of dollars in assets.
APRA’s Main Responsibilities
While APRA has a wide mandate, its work centres around four major responsibilities:
1. Promoting Financial Stability
APRA ensures banks and insurers are strong enough to survive economic shocks.
This includes monitoring capital levels, liquidity, and risk exposure.
If a bank faces difficulties, APRA steps in early to prevent instability from spreading through the system.
2. Ensuring Sound Risk Management
APRA expects all regulated institutions to have strong systems for managing:
- Credit risk
- Market risk
- Operational risk
- Technology risk
- Outsourcing risk
- Climate risk
- Governance breaches
Banks must prove they can identify, measure, and control risks before they cause harm.
3. Supervising Governance and Accountability
APRA sets expectations for:
- Board responsibilities
- Senior management oversight
- Internal audit frameworks
- Remuneration linked to risk
- Fit and proper evaluations
A strong governance culture is considered essential for long-term stability.
4. Protecting Depositors, Policyholders, and Superannuation Members
Perhaps APRA’s most important mandate is protecting the financial interests of Australians.
If a bank fails, APRA ensures deposits are protected up to the government guarantee amount.
If a super fund is mismanaged, APRA intervenes to safeguard members.
How APRA Supervises Banks
APRA uses a structured approach called supervision by risk.
This allows the regulator to focus resources on institutions that pose the greatest potential impact to the system.
APRA’s supervision toolkit includes:
1. Regular Reporting and Compliance Checks
Banks submit detailed financial, operational, and risk data on a scheduled basis.
2. On-Site Reviews
APRA examiners visit institutions to assess governance, risk culture, and operational controls.
3. Prudential Standards
Strict rules and guidelines covering:
- Capital adequacy (APS 110)
- Liquidity requirements (APS 210)
- Remuneration (CPS 511)
- Operational risk (CPS 230)
- Outsourcing (CPS 231)
- Business continuity (CPS 232)
These standards set the baseline for safe and responsible operations.
4. Stress Testing
APRA conducts industry-wide and institution-specific stress tests to simulate economic downturns or market shocks.
5. Enforcement Action
If a bank breaches expectations, APRA may impose:
- Additional capital requirements
- Remediation programs
- Licence restrictions
- Public warnings
- Management changes
While APRA rarely uses penalties, it expects rapid action when weaknesses are identified.

APRA vs AUSTRAC: What’s the Difference?
APRA and AUSTRAC are often mentioned together, but they enforce very different areas of compliance.
APRA
- Focuses on financial safety and stability
- Ensures institutions can survive economic or operational risk
- Regulates governance, culture, capital, liquidity, and risk management
AUSTRAC
- Focuses on preventing financial crime
- Enforces AML/CTF laws
- Oversees monitoring, reporting, and customer verification
Together, they form a complementary regulatory framework.
Why APRA Matters for Businesses and Consumers
APRA’s work affects everyone in Australia.
Here’s how:
For Consumers
- Ensures deposits and savings are safe
- Protects insurance claims
- Holds super funds accountable
- Prevents sudden collapses that disrupt the economy
For Businesses
- Ensures stable banking and payment systems
- Reduces the likelihood of credit shocks
- Promotes trust in financial institutions
For Banks and Financial Institutions
- Drives stronger risk management practices
- Requires investments in data, technology, and training
- Influences board-level decision-making
- Sets expectations for responsible innovation
A strong APRA means a stable financial future for Australia.
APRA in Today’s Banking Landscape
Australia’s financial ecosystem is undergoing major change:
- Digital onboarding
- Instant payments
- Artificial intelligence
- Cloud migration
- Open banking
- Increasing cyber threats
APRA’s role has expanded to include careful oversight of technology, operational resilience, and data integrity.
Its most influential modern standards include:
CPS 230 — Operational Risk Management
One of the most significant reforms in the last decade.
CPS 230 modernises expectations around:
- Critical operations
- Third-party risk
- Service resilience
- Technology oversight
- Incident management
CPS 234 — Information Security
Requires institutions to:
- Maintain strong cyber defences
- Protect sensitive information
- Respond quickly to incidents
- Test security controls regularly
CPS 511 — Remuneration
Aligns executive and employee incentives with non-financial outcomes such as ethics, conduct, and risk behaviour.
Why APRA Standards Matter for AML Teams
While APRA does not directly enforce AML/CTF laws, its standards strongly influence AML programs.
1. Strong Governance Expectations
AML decisions must align with risk appetite and board oversight.
2. Data Integrity Requirements
Accurate AML monitoring depends on clean, governed, high-quality data.
3. Operational Resilience
AML systems must remain stable even in the face of outages, disruptions, or cyber events.
4. Outsourcing Accountability
Banks must demonstrate they understand and control risks related to third-party AML technology providers.
5. Model and Algorithm Accountability
APRA expects explainability and oversight of any automated system used in compliance.
This is where Tookitaki’s emphasis on transparency, explainability, and federated learning aligns strongly with APRA principles.
Real-World Example: Regional Australia Bank
Regional Australia Bank, a community-owned financial institution, shows how APRA’s expectations translate into practical action.
By focusing on:
- Transparent systems
- Strong data practices
- Responsible innovation
- Clear governance
Regional Australia Bank demonstrates that even mid-sized institutions can meet APRA’s standards while modernising with AI.
This balance between technology and accountability reflects the future direction of Australian compliance.
The Future of APRA’s Role in Australian Banking
APRA is evolving alongside the financial system. Here are key areas where its influence is growing:
1. Technology and AI Governance
APRA is now more interested in how models operate, how decisions are made, and how risks are controlled.
2. Operational Resilience
Expectations around continuity, redundancy, and incident response will continue to rise.
3. Third-Party Risk Oversight
Banks must prove they manage outsourced technology with the same rigour as internal systems.
4. Cybersecurity and Data Governance
Data controls and security frameworks will become even more significant.
5. Climate and Sustainability Risk
APRA is exploring how climate events could affect financial stability.
These themes reinforce that prudential regulation is broadening, and institutions must be ready to adapt.
Conclusion
APRA plays a foundational role in shaping the strength, safety, and stability of Australia’s financial system. While consumers may rarely see its work, APRA’s influence touches every bank account, insurance claim, and superannuation balance.
For financial institutions, understanding APRA is not just a regulatory requirement. It is essential for sustainable operations and long-term trust.
As banks modernise their systems, adopt AI, and prepare for instant payments, APRA’s guidance offers a clear framework for responsible innovation.
Institutions like Regional Australia Bank show that meeting APRA expectations and modernising with advanced technology can go hand in hand.
Pro tip: In Australia, a strong AML and fraud strategy begins with a strong prudential foundation. APRA sets the rules that keep that foundation intact.

AML Vendors in Australia: How to Choose the Right Partner in a Rapidly Evolving Compliance Landscape
The AML vendor market in Australia is crowded, complex, and changing fast. Choosing the right partner is now one of the most important decisions a bank will make.
Introduction: A New Era of AML Choices
A decade ago, AML technology buying was simple. Banks picked one of a few rule-based systems, integrated it into their core banking environment, and updated thresholds once a year. Today, the landscape looks very different.
Artificial intelligence, instant payments, cross-border digital crime, APRA’s renewed focus on resilience, and AUSTRAC’s expectations for explainability are reshaping how banks evaluate AML vendors.
The challenge is no longer finding a system that “works”.
It is choosing a partner who can evolve with you.
This blog takes a fresh, practical, and Australian-specific look at the AML vendor ecosystem, what has changed, and what institutions should consider before committing to a solution.

Part 1: Why the AML Vendor Conversation Has Changed
The AML market globally has expanded rapidly, but Australia is experiencing something unique:
a shift from traditional rule-based models to intelligent, adaptive, and real-time compliance ecosystems.
Several forces are driving this change:
1. The Rise of Instant Payments
The New Payments Platform (NPP) introduced unprecedented settlement speed, compressing the investigation window from hours to minutes. Vendors must support real-time analysis, not batch-driven monitoring.
2. APRA’s Renewed Focus on Operational Resilience
Under CPS 230 and CPS 234, vendors are no longer just technology providers.
They are part of a bank’s risk ecosystem.
3. AUSTRAC’s Expectations for Transparency
Explainability is becoming non-negotiable. Vendors must show how their scenarios work, why alerts fire, and how models behave.
4. Evolving Criminal Behaviour
Human trafficking, romance scams, mule networks, synthetic identities.
Typologies evolve weekly.
Banks need vendors who can adapt quickly.
5. Pressure to Lower False Positives
Australian banks carry some of the highest alert volumes relative to population size.
Vendor intelligence matters more than ever.
The result:
Banks are no longer choosing AML software. They are choosing long-term intelligence partners.
Part 2: The Three Types of AML Vendors in Australia
The market can be simplified into three broad categories. Understanding them helps decision-makers avoid mismatches.
1. Legacy Rule-Based Platforms
These systems have existed for 10 to 20 years.
Strengths
- Stable
- Well understood
- Large enterprise deployments
Limitations
- Hard-coded rules
- Minimal adaptation
- High false positives
- Limited intelligence
- High cost of tuning
- Not suitable for real-time payments
Best for
Institutions with low transaction complexity, limited data availability, or a need for basic compliance.
2. Hybrid Vendors (Rules + Limited AI)
These providers add basic machine learning on top of traditional systems.
Strengths
- More flexible than legacy tools
- Some behavioural analytics
- Good for institutions transitioning gradually
Limitations
- Limited explainability
- AI add-ons, not core intelligence
- Still rule-heavy
- Often require large tuning projects
Best for
Mid-sized institutions wanting incremental improvement rather than transformation.
3. Intelligent AML Platforms (Native AI + Federated Insights)
This is the newest category, dominated by vendors who built systems from the ground up to support modern AML.
Strengths
- Built for real-time detection
- Adaptive models
- Explainable AI
- Collaborative intelligence capabilities
- Lower false positives
- Lighter operational load
Limitations
- Requires cultural readiness
- Needs better-quality data inputs
- Deeper organisational alignment
Best for
Banks seeking long-term AML maturity, operational scale, and future-proofing.
Australia is beginning to shift from Category 1 and 2 into Category 3.
Part 3: What Australian Banks Actually Want From AML Vendors in 2025
Interviews and discussions across risk and compliance teams reveal a pattern.
Banks want vendors who can deliver:
1. Real-time capabilities
Batch-based monitoring is no longer enough.
AML must keep pace with instant payments.
2. Explainability
If a model cannot explain itself, AUSTRAC will ask the institution to justify it.
3. Lower alert volumes
Reducing noise is as important as identifying crime.
4. Consistency across channels
Customers interact through apps, branches, wallets, partners, and payments.
AML cannot afford blind spots.
5. Adaptation without code changes
Vendors should deliver new scenarios, typologies, and thresholds without major uplift.
6. Strong support for small and community banks
Institutions like Regional Australia Bank need enterprise-grade intelligence without enterprise complexity.
7. Clear model governance dashboards
Banks want to see how the system performs, evolves, and learns.
8. A vendor who listens
Compliance teams want partners who co-create, not providers who supply static software.
This is why intelligent, collaborative platforms are rapidly becoming the new default.

Part 4: Questions Every Bank Should Ask an AML Vendor
This is the operational value section. It differentiates your blog immediately from generic AML vendor content online.
1. How fast can your models adapt to new typologies?
If the answer is “annual updates”, the vendor is outdated.
2. Do you support Explainable AI?
Regulators will demand transparency.
3. What are your false positive reduction metrics?
If the vendor cannot provide quantifiable improvements, be cautious.
4. How much of the configuration can we control internally?
Banks should not rely on vendor teams for minor updates.
5. Can you support real-time payments and NPP flows?
A modern AML platform must operate at NPP speed.
6. How do you handle federated learning or collective intelligence?
This is the modern competitive edge.
7. What does model drift detection look like?
AML intelligence must stay current.
8. Do analysts get contextual insights, or only alerts?
Context reduces investigation time dramatically.
9. How do you support operational resilience under CPS 230?
This is crucial for APRA-regulated banks.
10. What does onboarding and migration look like?
Banks want smooth transitions, not 18-month replatforming cycles.
Part 5: How Tookitaki Fits Into the AML Vendor Landscape
A Different Kind of AML Vendor
Tookitaki does not position itself as another monitoring system.
It sees AML as a collective intelligence challenge where individual banks cannot keep up with evolving financial crime by fighting alone.
Three capabilities make Tookitaki stand out in Australia:
1. Intelligence that learns from the real world
FinCense is built on a foundation of continuously updated scenario intelligence contributed by a network of global compliance experts.
Banks benefit from new behaviour patterns long before they appear internally.
2. Agentic AI that helps investigators
Instead of just generating alerts, Tookitaki introduces FinMate, a compliance investigation copilot that:
- Surfaces insights
- Suggests investigative paths
- Speeds up decision-making
- Reduces fatigue
- Improves consistency
This turns investigators into intelligence analysts, not data processors.
3. Federated learning that keeps data private
The platform learns from patterns across multiple banks without sharing customer data.
This gives institutions the power of global insight with the privacy of isolated systems.
Why this matters for Australian banks
- Supports real-time monitoring
- Reduces alert volumes
- Strengthens APRA CPS 230 alignment
- Provides explainability for AUSTRAC audits
- Offers a sustainable operational model for small and large banks
It is not just a vendor.
It is the trust layer that helps institutions outpace financial crime.
Part 6: The Future of AML Vendors in Australia
The AML vendor landscape is shifting from “who has the best rules” to “who has the best intelligence”. Here’s what the future looks like:
1. Dynamic intelligence networks
Static rules will fade away.
Networks of shared insights will define modern AML.
2. AI-driven decision support
Analysts will work alongside intelligent copilots, not alone.
3. No-code scenario updates
Banks will update scenarios like mobile apps, not system upgrades.
4. Embedded explainability
Every alert will come with narrative, not guesswork.
5. Real-time everything
Monitoring, detection, response, audit readiness.
6. Collaborative AML ecosystems
Banks will work together, not in silos.
Tookitaki sits at the centre of this shift.
Conclusion
Choosing an AML vendor in Australia is no longer a procurement decision.
It is a strategic one.
Banks today need partners who deliver intelligence, not just infrastructure.
They need transparency for AUSTRAC, resilience for APRA, and scalability for NPP.
They need technology that empowers analysts, not overwhelms them.
As the landscape continues to evolve, institutions that choose adaptable, explainable, and collaborative AML platforms will be future-ready.
The future belongs to vendors who learn faster than criminals.
And the banks who choose them wisely.

Fraud Detection System: How Malaysia Can Stay One Step Ahead of Digital Crime
As Malaysia’s financial system goes digital, fraud detection systems are becoming the silent guardians of consumer trust.
Malaysia’s Expanding Fraud Challenge
Malaysia is experiencing a digital transformation unlike anything seen before. QR payments, e-wallets, instant transfers, digital banks, and cross-border digital commerce have rapidly become part of everyday life.
Innovation has brought convenience, but it has also enabled a wave of sophisticated financial fraud. Criminal networks are using faster payment channels, deep social engineering, and large mule networks to steal and move funds before victims or institutions can react.
The Royal Malaysia Police, Bank Negara Malaysia (BNM), and cybersecurity agencies have consistently flagged the rise in:
- Online investment scams
- E-wallet fraud
- Account takeover attacks
- Romance scams
- Cross-border mule operations
- Deepfake-enabled fraud
- Social engineering targeting retirees and gig workers
Fraud not only causes financial loss but also erodes public trust in digital banking and fintech. As Malaysia accelerates toward a cashless society, the need for intelligent, proactive fraud detection has become a national priority.
This is where the evolution of the fraud detection system becomes central to protecting financial integrity.

What Is a Fraud Detection System?
A fraud detection system is a technology platform that identifies, prevents, and responds to fraudulent financial activity. It analyses millions of transactions, user behaviours, and contextual signals to detect anomalies that indicate fraud.
Modern fraud detection systems protect institutions against:
- Identity theft
- Transaction fraud
- Synthetic identities
- First-party fraud
- Friendly fraud
- Card-not-present attacks
- Social engineering scams
- Mule account activity
- False merchant onboarding
In Malaysia’s dynamic financial ecosystem, the fraud detection system acts as a real-time surveillance layer safeguarding both institutions and consumers.
How a Fraud Detection System Works
A powerful fraud detection system operates through a sequence of intelligent steps.
1. Data Collection
The system gathers data from multiple sources including payment platforms, device information, customer profiles, login behaviour, and transaction history.
2. Behavioural Analysis
Models recognise normal behavioural patterns and build a baseline for each user, device, or merchant.
3. Anomaly Detection
Any deviation from expected behaviour triggers deeper analysis. This includes unusual spending, unknown device access, rapid transactions, or location mismatches.
4. Risk Scoring
Each action or transaction receives a risk score based on probability of fraud.
5. Real-Time Decisioning
The system performs instant checks to accept, challenge, or block the activity.
6. Investigation and Feedback Loop
Alerts are routed to investigators who confirm whether a case is fraud. This feedback retrains machine learning models for higher accuracy.
Fraud detection systems are not static rule engines. They are continuously learning frameworks that adapt to new threats with every case reviewed.
Why Legacy Fraud Systems Fall Short
Despite increased digital adoption, many Malaysian financial institutions still use traditional fraud monitoring tools that struggle to keep pace with modern threats.
Here is where these systems fail:
- Static rule sets cannot detect emerging patterns like deepfake impersonation or mule rings.
- Slow investigation workflows allow fraudulent funds to leave the ecosystem before action can be taken.
- Limited visibility across channels results in blind spots between digital banking, cards, and payment rails.
- High false positives disrupt genuine customers and overwhelm analysts.
- Siloed AML and fraud systems prevent institutions from seeing fraud proceeds that transition into money laundering.
Fraud today is dynamic, distributed, and data driven. Systems built more than a decade ago cannot protect a modern, hyperconnected financial environment.
The Rise of AI-Powered Fraud Detection Systems
Artificial intelligence has transformed fraud detection into a predictive science. AI-powered fraud systems bring a level of intelligence and speed that traditional systems cannot match.
1. Machine Learning for Pattern Recognition
Models learn from millions of past transactions to identify subtle fraud behaviour, even if it has never been seen before.
2. Behavioural Biometrics
AI analyses keystroke patterns, time on page, navigation flow, and device characteristics to distinguish legitimate users from attackers.
3. Real-Time Detection
AI systems analyse risk instantly, giving institutions crucial seconds to block or hold suspicious activity.
4. Lower False Positives
AI reduces unnecessary alerts by understanding context, not just rules.
5. Autonomous Detection and Triage
AI systems prioritise high-risk alerts and automate repetitive tasks, freeing investigators to focus on complex threats.
AI-powered systems do not simply detect fraud. They help institutions anticipate it.
Why Malaysia Needs Next-Generation Fraud Detection
Fraud in Malaysia is no longer isolated to simple scams. Criminal networks have become highly organised, using advanced technologies and exploiting digital loopholes.
Malaysia faces increasing risks from:
- QR laundering through DuitNow
- Instant pay-and-transfer fraud
- Cross-border mule farming
- Scams operated from foreign syndicate hubs
- Cryptocurrency-linked laundering
- Fake merchant setups
- Fast layering to offshore accounts
These patterns require solutions that recognise behaviour, understand typologies, and react in real time. This is why modern fraud detection systems integrated with AI are becoming essential for Malaysian risk teams.
Tookitaki’s FinCense: Malaysia’s Most Advanced Fraud Detection System
At the forefront of AI-driven fraud prevention is Tookitaki’s FinCense, an end-to-end platform built to detect and prevent both fraud and money laundering. It is used by leading banks and fintechs across Asia-Pacific and is increasingly recognised as the trust layer to fight financial crime.
FinCense is built on four pillars that make it uniquely suited to Malaysia’s digital economy.
1. Agentic AI for Faster, Smarter Investigations
FinCense uses intelligent autonomous agents that perform tasks such as alert triage, pattern clustering, narrative generation, and risk explanation.
These agents work around the clock, giving compliance teams:
- Faster case resolution
- Higher accuracy
- Better prioritisation
- Clear decision support
This intelligent layer allows teams to handle high volumes of fraud alerts without burning out or missing critical risks.
2. Federated Intelligence Through the AFC Ecosystem
Fraud patterns often emerge in one market before appearing in another. FinCense connects to the Anti-Financial Crime (AFC) Ecosystem, a collaborative intelligence network of institutions across ASEAN.
Through privacy-preserving federated learning, models benefit from:
- Regional typologies
- New scam patterns
- Real-time cross-border trends
- Behavioural signatures of mule activity
This gives Malaysian institutions early visibility into fraud patterns seen in Singapore, the Philippines, Indonesia, and Thailand.
3. Explainable AI for Trust and Compliance
Regulators expect not just accuracy but clarity. FinCense generates explanations for every flagged event, detailing the data points and logic used in the decision.
This ensures:
- Full transparency
- Audit readiness
- Confidence in automated decisions
- Better regulatory communication
Explainability is essential for AI adoption, and FinCense is designed to meet these expectations.
4. Unified Fraud and AML Detection
Fraud often transitions into money laundering. FinCense unifies fraud detection and AML transaction monitoring into one decisioning platform. This allows teams to:
- Connect fraud events to laundering flows
- Detect mule activity linked to scams
- Analyse both behavioural and transactional trends
- Break criminal networks instead of individual incidents
This unified view creates a powerful defence that legacy siloed systems cannot match.

Real-World Scenario: Detecting Cross-Border Investment Fraud
Consider a popular scam trend. Victims in Malaysia receive calls or WhatsApp messages promising high returns through offshore trading platforms. They deposit funds into mule accounts linked to foreign syndicates.
Here is how FinCense detects and disrupts this:
- The system identifies unusual inbound deposits from unrelated senders.
- Behavioural analysis detects rapid movement of funds between multiple local accounts.
- Federated intelligence matches this behaviour with similar typologies in Singapore and Hong Kong.
- Agentic AI generates a complete case narrative summarising:
- Transaction velocity
- Peer network connections
- Device and login anomalies
- Similar scenarios seen in the region
- The institution blocks the outbound transfer, freezes the account, and prevents losses.
This entire process occurs within minutes, a speed that traditional systems cannot match.
Benefits for Malaysian Financial Institutions
Deploying an AI-powered fraud detection system like FinCense has measurable impact.
- Significant reduction in false positives
- Faster alert resolution times
- Better protection for vulnerable customers
- Higher detection accuracy
- Lower operational costs
- Improved regulator trust
- Better customer experience
Fraud prevention shifts from reactive defence to proactive risk management.
Key Features to Look for in a Modern Fraud Detection System
Financial institutions evaluating fraud systems should prioritise five core capabilities.
1. Intelligence and adaptability
Systems must evolve with new fraud trends and learn continuously.
2. Contextual and behavioural detection
Instead of relying solely on rules, solutions should use behavioural analytics to understand intent.
3. Real-time performance
Fraud moves in seconds. Systems must react instantly.
4. Explainability
Every alert should be transparent and justified for regulatory confidence.
5. Collaborative intelligence
Systems must learn from regional behaviour, not just local data.
FinCense checks all these boxes and provides additional advantages through unified fraud and AML detection.
The Future of Fraud Detection in Malaysia
Malaysia is on a clear path toward a safer digital financial ecosystem. The next phase of fraud detection will be shaped by several emerging trends:
- Open banking data sharing enabling richer identity verification
- Real-time AI models trained on regional intelligence
- Deeper collaboration between banks, fintechs, and regulators
- Human-AI partnerships integrating expertise and computational power
- Unified financial crime platforms merging AML, fraud, and sanctions for complete visibility
Malaysia’s forward-looking regulatory environment positions the country as a leader in intelligent fraud prevention across ASEAN.
Conclusion
Fraud detection is no longer a standalone function. It is the heartbeat of trust in Malaysia’s digital financial future. As criminals innovate faster and exploit new technologies, institutions must adopt tools that can outthink, outpace, and outmanoeuvre sophisticated fraud networks.
Tookitaki’s FinCense stands as the leading fraud detection system built for Malaysia. It blends Agentic AI, federated intelligence, and explainable models to create real-time, transparent, and regionally relevant protection.
By moving from static rules to collaborative intelligence, Malaysia’s financial institutions can stay one step ahead of digital crime and build a safer future for every consumer.

What Is APRA? A Simple Guide to Australia’s Banking Regulator
If you live, work, or bank in Australia, your financial safety is protected by an agency you may not know well: APRA.
Introduction
Most Australians interact with banks every day without ever thinking about the rules and systems that keep the financial sector stable. Behind the scenes, one regulator plays a critical role in ensuring banks are safe, resilient, and well managed: the Australian Prudential Regulation Authority, better known as APRA.
APRA oversees the health of the financial system, ensuring that banks, credit unions, insurers, and superannuation funds operate responsibly. While AUSTRAC focuses on preventing money laundering and financial crime, APRA focuses on stability, governance, risk, and long-term protection.
In a fast-changing financial world, understanding APRA is becoming increasingly important for businesses, compliance teams, fintechs, and even everyday consumers.
This simple guide explains what APRA does, who it regulates, and why its work matters.

What Does APRA Stand For?
APRA stands for the Australian Prudential Regulation Authority.
The term “prudential regulation” refers to the rules and oversight that ensure financial institutions remain safe, stable, and financially sound. That means APRA’s job is to make sure financial organisations can weather risks, protect customer deposits, and operate sustainably.
Why Was APRA Created?
APRA was formed in 1998 following major reforms to Australia’s financial regulatory system. These reforms recognised the need for a dedicated agency to supervise the financial health of institutions.
APRA’s creation brought together prudential functions from:
- The Reserve Bank of Australia
- The Insurance and Superannuation Commission
The goal was simple: Protect customers and promote a stable financial system.
What Organisations Does APRA Regulate?
APRA supervises institutions that hold and manage Australians’ money. These include:
1. Banks and Authorised Deposit-Taking Institutions (ADIs)
- Major banks
- Regional and community-owned banks
- Credit unions
- Building societies
- Digital banks
2. Insurance Companies
- Life insurers
- General insurers
- Private health insurers
3. Superannuation Funds
- Retail, industry, corporate, and public sector funds
4. Some Non-Bank Financial Institutions
Entities that hold financial risk but are not traditional banks.
In total, APRA oversees more than 600 financial institutions that collectively hold trillions of dollars in assets.
APRA’s Main Responsibilities
While APRA has a wide mandate, its work centres around four major responsibilities:
1. Promoting Financial Stability
APRA ensures banks and insurers are strong enough to survive economic shocks.
This includes monitoring capital levels, liquidity, and risk exposure.
If a bank faces difficulties, APRA steps in early to prevent instability from spreading through the system.
2. Ensuring Sound Risk Management
APRA expects all regulated institutions to have strong systems for managing:
- Credit risk
- Market risk
- Operational risk
- Technology risk
- Outsourcing risk
- Climate risk
- Governance breaches
Banks must prove they can identify, measure, and control risks before they cause harm.
3. Supervising Governance and Accountability
APRA sets expectations for:
- Board responsibilities
- Senior management oversight
- Internal audit frameworks
- Remuneration linked to risk
- Fit and proper evaluations
A strong governance culture is considered essential for long-term stability.
4. Protecting Depositors, Policyholders, and Superannuation Members
Perhaps APRA’s most important mandate is protecting the financial interests of Australians.
If a bank fails, APRA ensures deposits are protected up to the government guarantee amount.
If a super fund is mismanaged, APRA intervenes to safeguard members.
How APRA Supervises Banks
APRA uses a structured approach called supervision by risk.
This allows the regulator to focus resources on institutions that pose the greatest potential impact to the system.
APRA’s supervision toolkit includes:
1. Regular Reporting and Compliance Checks
Banks submit detailed financial, operational, and risk data on a scheduled basis.
2. On-Site Reviews
APRA examiners visit institutions to assess governance, risk culture, and operational controls.
3. Prudential Standards
Strict rules and guidelines covering:
- Capital adequacy (APS 110)
- Liquidity requirements (APS 210)
- Remuneration (CPS 511)
- Operational risk (CPS 230)
- Outsourcing (CPS 231)
- Business continuity (CPS 232)
These standards set the baseline for safe and responsible operations.
4. Stress Testing
APRA conducts industry-wide and institution-specific stress tests to simulate economic downturns or market shocks.
5. Enforcement Action
If a bank breaches expectations, APRA may impose:
- Additional capital requirements
- Remediation programs
- Licence restrictions
- Public warnings
- Management changes
While APRA rarely uses penalties, it expects rapid action when weaknesses are identified.

APRA vs AUSTRAC: What’s the Difference?
APRA and AUSTRAC are often mentioned together, but they enforce very different areas of compliance.
APRA
- Focuses on financial safety and stability
- Ensures institutions can survive economic or operational risk
- Regulates governance, culture, capital, liquidity, and risk management
AUSTRAC
- Focuses on preventing financial crime
- Enforces AML/CTF laws
- Oversees monitoring, reporting, and customer verification
Together, they form a complementary regulatory framework.
Why APRA Matters for Businesses and Consumers
APRA’s work affects everyone in Australia.
Here’s how:
For Consumers
- Ensures deposits and savings are safe
- Protects insurance claims
- Holds super funds accountable
- Prevents sudden collapses that disrupt the economy
For Businesses
- Ensures stable banking and payment systems
- Reduces the likelihood of credit shocks
- Promotes trust in financial institutions
For Banks and Financial Institutions
- Drives stronger risk management practices
- Requires investments in data, technology, and training
- Influences board-level decision-making
- Sets expectations for responsible innovation
A strong APRA means a stable financial future for Australia.
APRA in Today’s Banking Landscape
Australia’s financial ecosystem is undergoing major change:
- Digital onboarding
- Instant payments
- Artificial intelligence
- Cloud migration
- Open banking
- Increasing cyber threats
APRA’s role has expanded to include careful oversight of technology, operational resilience, and data integrity.
Its most influential modern standards include:
CPS 230 — Operational Risk Management
One of the most significant reforms in the last decade.
CPS 230 modernises expectations around:
- Critical operations
- Third-party risk
- Service resilience
- Technology oversight
- Incident management
CPS 234 — Information Security
Requires institutions to:
- Maintain strong cyber defences
- Protect sensitive information
- Respond quickly to incidents
- Test security controls regularly
CPS 511 — Remuneration
Aligns executive and employee incentives with non-financial outcomes such as ethics, conduct, and risk behaviour.
Why APRA Standards Matter for AML Teams
While APRA does not directly enforce AML/CTF laws, its standards strongly influence AML programs.
1. Strong Governance Expectations
AML decisions must align with risk appetite and board oversight.
2. Data Integrity Requirements
Accurate AML monitoring depends on clean, governed, high-quality data.
3. Operational Resilience
AML systems must remain stable even in the face of outages, disruptions, or cyber events.
4. Outsourcing Accountability
Banks must demonstrate they understand and control risks related to third-party AML technology providers.
5. Model and Algorithm Accountability
APRA expects explainability and oversight of any automated system used in compliance.
This is where Tookitaki’s emphasis on transparency, explainability, and federated learning aligns strongly with APRA principles.
Real-World Example: Regional Australia Bank
Regional Australia Bank, a community-owned financial institution, shows how APRA’s expectations translate into practical action.
By focusing on:
- Transparent systems
- Strong data practices
- Responsible innovation
- Clear governance
Regional Australia Bank demonstrates that even mid-sized institutions can meet APRA’s standards while modernising with AI.
This balance between technology and accountability reflects the future direction of Australian compliance.
The Future of APRA’s Role in Australian Banking
APRA is evolving alongside the financial system. Here are key areas where its influence is growing:
1. Technology and AI Governance
APRA is now more interested in how models operate, how decisions are made, and how risks are controlled.
2. Operational Resilience
Expectations around continuity, redundancy, and incident response will continue to rise.
3. Third-Party Risk Oversight
Banks must prove they manage outsourced technology with the same rigour as internal systems.
4. Cybersecurity and Data Governance
Data controls and security frameworks will become even more significant.
5. Climate and Sustainability Risk
APRA is exploring how climate events could affect financial stability.
These themes reinforce that prudential regulation is broadening, and institutions must be ready to adapt.
Conclusion
APRA plays a foundational role in shaping the strength, safety, and stability of Australia’s financial system. While consumers may rarely see its work, APRA’s influence touches every bank account, insurance claim, and superannuation balance.
For financial institutions, understanding APRA is not just a regulatory requirement. It is essential for sustainable operations and long-term trust.
As banks modernise their systems, adopt AI, and prepare for instant payments, APRA’s guidance offers a clear framework for responsible innovation.
Institutions like Regional Australia Bank show that meeting APRA expectations and modernising with advanced technology can go hand in hand.
Pro tip: In Australia, a strong AML and fraud strategy begins with a strong prudential foundation. APRA sets the rules that keep that foundation intact.


