With instant payments now standard in Australia, real-time fraud detection software is essential for protecting customers and meeting AUSTRAC standards.

Introduction

Fraud is evolving at the same speed as financial innovation. In 2024, Australians lost more than AUD 3 billion to scams, much of it through banking and payment channels. The introduction of the New Payments Platform (NPP) has been a game-changer for consumers, enabling instant, 24/7 transfers, but it has also created new opportunities for fraudsters.

Traditional fraud monitoring systems, designed for batch processing, cannot cope with this real-time environment. To fight back, institutions are investing in real-time fraud detection software that can identify and stop suspicious activity before funds leave the bank.

Why Real-Time Fraud Detection Matters in Australia

1. Instant Payments Require Instant Protection

The NPP enables funds to move in seconds. Fraudsters exploit this speed to launder or steal funds before detection is possible with legacy systems.

2. Scam Epidemic

Authorised push payment (APP) fraud, romance scams, and investment scams are increasing, often leaving customers with little recourse once funds are gone.

3. Regulatory Expectations

AUSTRAC requires institutions to implement effective monitoring. Real-time fraud detection aligns with regulatory expectations by identifying red flags at the point of transaction.

4. Reputation and Trust

A single fraud scandal can damage years of customer trust. Real-time protection is not only about compliance but also about maintaining credibility in a competitive market.

What is Real-Time Fraud Detection Software?

Real-time fraud detection software monitors transactions, customer behaviour, and device activity as they occur. Using AI, behavioural analytics, and machine learning, these systems decide in milliseconds whether to approve, block, or escalate a transaction.

Core components include:

• Transaction Monitoring: Continuous risk scoring of transactions.
• Behavioural Analytics: Tracking customer activity across channels.
• Device and Location Fingerprinting: Identifying unusual access.
• AI Models: Detecting anomalies and adapting to new threats.
• Case Management Integration: Feeding alerts to investigators in real time.

Common Fraud Typologies in Australia Detected in Real Time

1. Account Takeover (ATO): Criminals gain control of accounts through phishing or malware, then move funds instantly.
2. Authorised Push Payment (APP) Fraud: Victims are tricked into transferring funds to mule accounts.
3. Mule Account Activity: Networks of accounts pass funds rapidly with minimal balances.
4. Card-Not-Present Fraud: Stolen card details used in e-commerce transactions.
5. Crypto Laundering: Funds converted to crypto in real time to obscure origins.
6. Business Email Compromise (BEC): Fraudsters impersonate vendors or executives to redirect payments.

Red Flags for Real-Time Detection

• High-value transfers to new or unverified beneficiaries.
• Multiple small transactions designed to evade thresholds.
• Sudden changes in login location or device fingerprint.
• Unusual transaction times, such as midnight high-value payments.
• Customers reluctant to provide verification or documentation.
• Rapid in-and-out flows of funds with no balance retention.

Benefits of Real-Time Fraud Detection Software

1. Prevents Losses Before They Happen: Stops fraudulent transfers before funds are irretrievable.
2. Reduces False Positives: AI models distinguish between genuine unusual activity and fraud.
3. Improves Customer Experience: Detects fraud without unnecessary friction for legitimate users.
4. Strengthens Regulatory Compliance: Ensures institutions meet AUSTRAC’s AML/CTF requirements.
5. Protects Reputation: Demonstrates proactive fraud prevention to customers and regulators.

Challenges in Deploying Real-Time Systems

• Integration Complexity: Connecting to legacy banking systems can be resource-intensive.
• Data Overload: Real-time monitoring generates large data volumes that must be processed efficiently.
• False Positives: Poorly calibrated systems can still burden compliance teams.
• Cost of Implementation: High initial investment may be difficult for smaller institutions.
• Talent Shortages: Skilled AML and fraud investigators are in short supply in Australia.

Case Example: Community-Owned Banks Leading the Way

Community-owned banks like Regional Australia Bank and Beyond Bank are deploying advanced compliance platforms to strengthen fraud detection. Despite their smaller scale compared to Tier-1 institutions, they have successfully implemented real-time monitoring to protect their customers and ensure AUSTRAC compliance.

Their example shows that innovation is not limited to large banks. With the right technology, any institution can achieve world-class fraud prevention.

Spotlight: Tookitaki’s FinCense

FinCense, Tookitaki’s compliance platform, delivers advanced real-time fraud detection capabilities tailored to the Australian market.

• Real-Time Monitoring: Detects suspicious activity across NPP, PayTo, and cross-border corridors in milliseconds.
• Agentic AI: Continuously learns from fraud patterns to reduce false positives.
• Federated Intelligence: Accesses typologies from the AFC Ecosystem, a global compliance knowledge community.
• FinMate AI Copilot: Assists investigators with summaries, recommendations, and regulator-ready reporting.
• AUSTRAC Compliance: Automated SMR and TTR reporting, with complete audit trails.
• Cross-Channel Coverage: Banking, cards, wallets, remittances, and crypto all monitored in one platform.

By adopting FinCense, Australian institutions can prevent fraud effectively while reducing operational workload and compliance costs.

Future Trends in Real-Time Fraud Detection

1. Deeper Integration with PayTo: New overlay services will require stronger monitoring.
2. Deepfake and AI Scams: Fraudsters are already using AI to impersonate voices and identities, requiring advanced countermeasures.
3. Shared Fraud Databases: Industry-wide intelligence sharing will help stop scams in real time.
4. AI-Driven Investigations: Copilots like FinMate will automate large portions of fraud investigations.
5. Customer-Centric Security: The future will focus on balancing strong protection with frictionless user experiences.

Conclusion

In an environment where payments move in seconds, fraud detection must be just as fast. Legacy systems designed for batch reviews are no longer sufficient. Real-time fraud detection software is now essential for Australian banks, fintechs, and remittance providers.

Community-owned banks like Regional Australia Bank and Beyond Bank demonstrate that advanced real-time monitoring is achievable even for smaller institutions. By adopting platforms like FinCense, financial institutions can not only meet AUSTRAC’s standards but also build customer trust and resilience.

Pro tip: Invest in real-time fraud detection that adapts to new threats, reduces false positives, and provides regulator-ready transparency. Anything less leaves your institution one step behind criminals.

Every transaction leaves a trail, but only vigilant monitoring can reveal which ones are hiding trouble.

In the Philippines, financial institutions are under growing scrutiny. The country’s removal from the FATF grey list in 2024 was a milestone, but it also raised expectations for stronger controls. At the heart of these controls lies suspicious transaction monitoring, a process that goes beyond simple rule checks to safeguard banks, customers, and the wider economy from money laundering and financial crime.

Understanding Suspicious Transaction Monitoring

Suspicious transaction monitoring refers to the continuous review of customer activity to identify unusual, inconsistent, or potentially illicit patterns. Unlike generic rule-based detection, this process requires context and judgement.

At its core, monitoring involves:

• Reviewing customer transactions against expected behaviour.
• Identifying red flags such as structuring, rapid inflows and outflows, or activity linked to sanctioned jurisdictions.
• Investigating unusual cases to decide whether they warrant escalation.
• Filing Suspicious Transaction Reports (STRs) with the Anti-Money Laundering Council (AMLC) if suspicions remain.

This approach is designed not only to comply with regulation but also to build resilience and trust in the banking system.

Why It Matters in the Philippines

The Philippines is particularly exposed to financial crime risks. Several factors make suspicious transaction monitoring essential:

1. Massive remittance inflows
   The country is among the top recipients of overseas worker remittances, with more than USD 36 billion flowing annually. These funds are critical to the economy but also a target for laundering schemes that exploit remittance channels.
2. Rapid digitalisation
   Mobile wallets, digital-only banks, and e-payment platforms have expanded access to finance. At the same time, they have created new opportunities for fraudsters to move funds quickly and anonymously.
3. Cross-border risks
   Criminal syndicates exploit porous regional networks, correspondent banking channels, and shell companies to funnel illicit proceeds.
4. High cash usage
   In rural areas, cash remains dominant, complicating the ability of banks to detect abnormal flows through digital systems.

For these reasons, regulators have placed heightened importance on detecting suspicious activity early and accurately.

What Counts as a Suspicious Transaction?

Suspicion is not proof of wrongdoing. It is about identifying inconsistencies or behaviours that do not fit a customer’s known profile. Some of the most common indicators include:

• Multiple small deposits designed to avoid reporting thresholds.
• Large sums moving rapidly in and out of an account without clear economic purpose.
• Customer activity inconsistent with known income or business operations.
• Transactions routed through high-risk or sanctioned countries.
• Dormant accounts suddenly becoming active with significant transfers.
• Fund movements involving shell companies or entities with unclear ownership.

When flagged, these activities require timely investigation.

How Suspicious Transaction Monitoring Works

The monitoring process usually unfolds in several steps:

1. Data Collection
   Banks gather transaction and customer data across channels including deposits, withdrawals, wire transfers, and digital payments.
2. Automated Screening
   Predefined rules or advanced machine learning models analyse activity and flag unusual patterns.
3. Alert Generation
   Cases that meet risk thresholds are escalated as alerts.
4. Case Review and Investigation
   Investigators examine flagged cases, combining transactional data with KYC information and external intelligence.
5. Decision Making
   Cases are either dismissed with justification or escalated for further action.
6. Regulatory Reporting
   If suspicion remains, an STR is filed with the AMLC within the required timeline.

Limitations of Traditional Monitoring Systems

While transaction monitoring has been part of banking compliance for decades, many institutions still rely on legacy systems that struggle to keep pace. Common challenges include:

• High false positives that overwhelm investigators and waste resources.
• Static rules that fail to capture evolving fraud tactics.
• Siloed data scattered across different systems, limiting visibility.
• Slow investigation workflows that delay reporting and expose banks to penalties.

These limitations highlight why modernisation is not optional.

Modern Approaches: Smarter Monitoring for Smarter Criminals

Financial crime is becoming more sophisticated, so monitoring systems must evolve. Leading institutions are adopting:

1. Risk-Based Monitoring
   Systems that assign risk scores to customers and transactions, allowing banks to prioritise alerts that truly matter.
2. Machine Learning Models
   AI-driven detection that learns from historical patterns, cutting down false positives while catching new typologies.
3. Behavioural Analytics
   Analysing normal customer behaviour and flagging deviations, such as sudden high-value transfers from low-income accounts.
4. Real-Time Monitoring
   Instead of reviewing transactions in batches, suspicious activity is flagged instantly before funds leave the system.
5. Explainable AI (XAI)
   Models that not only detect anomalies but also provide clear explanations regulators and investigators can understand.

Philippine Scenarios Where Monitoring Is Critical

Several local typologies highlight why monitoring suspicious activity is crucial:

• Remittance Structuring
  Overseas funds split into multiple small transfers, eventually consolidated into one account.
• Terror Financing
  Frequent low-value transfers directed toward high-risk regions.
• Casino Laundering
  Large buy-ins followed by minimal play and quick cash-outs, often linked to junket operators.
• Trade-Based Laundering
  Invoices mismatched with payment values in cross-border trade.
• Money Mule Recruitment
  Students, retirees, or low-income individuals used to move illicit funds unknowingly.

Each of these cases demonstrates how criminals adapt to exploit the financial system, making advanced monitoring essential.

Regulatory Requirements for Suspicious Transaction Monitoring

The Anti-Money Laundering Act (AMLA) and BSP guidelines set strict obligations for covered institutions:

• Continuous monitoring of customer activity.
• Filing of STRs within five working days of detecting suspicion.
• Maintenance of auditable records of monitoring processes.
• Enhanced scrutiny of high-risk customers such as politically exposed persons (PEPs).

The AMLC has emphasised that institutions must adopt a risk-based and technology-driven approach, aligning with FATF standards.

Challenges for Philippine Banks and Fintechs

Despite awareness, institutions often face practical hurdles:

• Difficulty integrating monitoring tools with legacy core banking systems.
• Shortage of trained AML investigators to handle complex cases.
• Budget limitations for rural banks and smaller fintechs.
• Criminal groups leveraging cryptocurrency, deepfakes, and social engineering to bypass controls.

These realities underscore the need for smarter, collaborative solutions.

Best Practices for Stronger Monitoring Programs

To meet expectations and stay ahead of criminals, banks should:

• Adopt hybrid models combining traditional rules with machine learning.
• Collaborate across the industry to share typologies and red flags.
• Retrain models frequently with the latest data on emerging fraud trends.
• Invest in investigator training to build digital forensics expertise.
• Prioritise explainability to ensure all flagged cases stand up to regulatory scrutiny.

The Tookitaki Edge: Smarter Monitoring with FinCense

Tookitaki’s FinCense is designed as a trust layer for financial institutions in the Philippines. It strengthens suspicious transaction monitoring with:

• Agentic AI models that adapt quickly to evolving threats.
• Federated intelligence from the AFC Ecosystem, bringing real-world typologies contributed by industry experts.
• Smart Disposition engine that generates investigation summaries to accelerate STR filing.
• Transparent decision-making aligned with BSP and AMLC requirements.

By combining advanced technology with collaborative intelligence, FinCense helps banks cut false positives, improve investigation quality, and build stronger regulatory trust.

Conclusion: Turning Compliance into Confidence

Suspicious transaction monitoring is not just a regulatory obligation. It is a foundation for trust in the Philippine financial system. By upgrading to smarter, AI-powered monitoring solutions, banks can move from a reactive posture to a proactive stance.

The institutions that treat suspicious transaction monitoring as a strategic investment rather than a compliance burden will be the ones best equipped to fight crime, satisfy regulators, and win customer loyalty in the years ahead.

As fintechs reshape banking in Australia, AML compliance has become a critical factor in building trust and meeting AUSTRAC’s expectations.

Introduction

Australia’s fintech industry has grown rapidly over the last decade, transforming how people save, invest, borrow, and send money. With innovations in digital wallets, buy now pay later (BNPL), peer-to-peer lending, and cross-border payments, fintechs are driving financial inclusion and competition.

But growth also brings risk. Fintechs, like banks and remittance providers, are exposed to money laundering and terrorism financing threats. Regulators, led by AUSTRAC, are raising the bar for compliance. For fintechs, AML compliance is not just about avoiding penalties. It is about securing customer trust, enabling partnerships, and scaling responsibly.

Why AML Compliance Matters for Fintechs

1. Regulatory Obligation

Under the AML/CTF Act 2006, fintechs offering financial services are classified as reporting entities. They must register with AUSTRAC and comply with AML requirements.

2. Customer Trust

Consumers expect fintechs to be safe and secure. Failing to manage AML risks undermines confidence and slows adoption.

3. Partnerships with Banks

Banks and larger institutions require fintechs to demonstrate robust AML programs before forming partnerships. Weak compliance is a barrier to growth.

4. Fraud and Money Laundering Risks

Fintechs are particularly exposed to mule accounts, synthetic identities, and cross-border laundering through digital platforms.

5. Global Reputation

Strong AML frameworks make it easier for fintechs to expand internationally and align with regulators in other jurisdictions.

AML Challenges Unique to Fintechs

1. Rapid Growth: Scaling quickly often means compliance processes lag behind product development.
2. Limited Resources: Smaller teams may lack dedicated compliance officers or advanced monitoring systems.
3. High Transaction Volumes: Digital platforms process large numbers of small transactions, making suspicious activity harder to detect.
4. Cross-Border Exposure: Many fintechs rely on international payment rails that increase exposure to laundering risks.
5. Evolving Typologies: Fraudsters exploit fintech products in novel ways, from BNPL abuse to crypto laundering.

Key AML Obligations for Fintechs in Australia

1. AML/CTF Program

Fintechs must establish a tailored AML/CTF program that outlines risk management procedures. This includes governance, staff training, and independent reviews.

2. Customer Due Diligence (CDD)

• Verify customer identities before providing services.
• Apply enhanced due diligence (EDD) for high-risk customers.
• Conduct ongoing monitoring to detect unusual behaviour.

3. Transaction Monitoring

• Detect suspicious transactions in real time.
• Configure systems to adapt to evolving typologies.

4. Reporting to AUSTRAC

Fintechs must submit:

• Suspicious Matter Reports (SMRs)
• Threshold Transaction Reports (TTRs)
• International Funds Transfer Instructions (IFTIs)

5. Record Keeping

Maintain records of identity verification and transactions for at least seven years.

6. Annual Compliance Reporting

Submit an annual compliance report (ACR) to AUSTRAC to confirm adherence to AML/CTF obligations.

High-Risk Areas for Fintechs

1. Digital Wallets: Can be used for layering funds.
2. BNPL Services: Attractive to fraudsters using stolen or synthetic identities.
3. Cross-Border Remittances: High risk due to exposure to overseas laundering networks.
4. Crypto Transactions: Increasingly used to obscure fund flows.
5. Peer-to-Peer Lending: Vulnerable to misuse for placement and layering of illicit funds.

Red Flags Fintechs Should Watch For

• Customers transacting at odd hours or in unusual patterns.
• High volumes of small-value transactions designed to avoid thresholds.
• Customers reluctant to provide source-of-funds information.
• Rapid pass-through activity with no account balance retention.
• Accounts linked to multiple devices or IP addresses.
• Transfers to high-risk jurisdictions without clear business purpose.

Best Practices for AML in Fintechs

1. Embed Compliance Early: Design AML processes alongside product development, not after launch.
2. Adopt Real-Time Monitoring: Batch systems cannot keep pace with instant payments like NPP and PayTo.
3. Leverage AI and Machine Learning: Reduce false positives and improve anomaly detection.
4. Automate Onboarding: Integrate digital KYC/CDD tools for efficiency and accuracy.
5. Train Staff Continuously: Keep teams updated on typologies and AUSTRAC expectations.
6. Engage Regulators Proactively: Open dialogue with AUSTRAC helps fintechs stay ahead of compliance trends.
7. Collaborate with Industry Peers: Sharing typologies strengthens resilience against organised crime.

Case Example: Community-Owned Banks and Compliance Innovation

Community-owned banks such as Regional Australia Bank and Beyond Bank demonstrate how even mid-sized institutions can deploy advanced compliance solutions. Fintechs can take inspiration from these banks, which have successfully reduced false positives, improved reporting speed, and strengthened trust through advanced technology adoption.

Spotlight: Tookitaki’s FinCense for Fintechs

FinCense is designed to support fintechs in Australia by combining AML and fraud prevention into one platform.

• Real-Time Monitoring: Detects suspicious activity across NPP, BNPL, wallets, and cross-border corridors.
• Agentic AI: Continuously learns from new laundering typologies, reducing false positives.
• Federated Intelligence: Accesses insights from the AFC Ecosystem, a global compliance community.
• FinMate AI Copilot: Helps investigators close cases faster with summaries and regulator-ready reports.
• AUSTRAC-Ready: Automates SMRs, TTRs, and IFTIs, with full audit trails.
• Scalable Deployment: Works for startups and scaling fintechs as well as larger banks.

FinCense empowers fintechs to grow without compromising on compliance, making it easier to secure partnerships and satisfy regulators.

Future Trends in AML for Fintechs

1. Deeper Integration with NPP and PayTo: Real-time payments will require even stronger monitoring.
2. Crypto Oversight: Stricter regulation of digital asset service providers will shape fintech AML frameworks.
3. AI-First Compliance Teams: AI copilots like FinMate will become standard tools for investigators.
4. Cross-Border Collaboration: Fintechs expanding internationally will need AML programs aligned with multiple regulators.
5. Sustainability of Compliance: Automation will be essential to balance compliance costs with growth.

Conclusion

For fintechs in Australia, AML compliance is not just about satisfying AUSTRAC. It is about building trust with customers, securing partnerships with banks, and enabling sustainable growth. Criminals are exploiting fintech platforms, but with the right tools and frameworks, fintechs can stay ahead.

Community-owned banks like Regional Australia Bank and Beyond Bank prove that strong compliance is possible for institutions of any size. Fintechs that embrace advanced, AI-powered compliance platforms will be better positioned to innovate and scale responsibly.

Pro tip: Make AML compliance part of your fintech’s DNA. It will pay dividends in trust, resilience, and long-term growth.