The Transformative Role of Generative AI in Financial Crime Compliance
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When we look at the financial crime landscape today, it’s clear that we’re on the brink of a significant evolution. The traditional methods of combating money laundering and fraud, which have relied heavily on rule-based systems and static models, are rapidly being eclipsed by the transformative potential of artificial intelligence (AI) and machine learning (ML). Over the last two decades, these technologies have fundamentally changed how we identify and respond to illicit activities. But as we look into the next few years, a new tech transformation is set to reshape the field: generative AI.
This isn't just another technological upgrade—it’s a paradigm shift. Generative AI is poised to redefine the rules of the game, offering unprecedented capabilities that go beyond the detection and prevention tools we’ve relied on so far. While ML has already improved our ability to spot suspicious patterns, generative AI promises to tackle more sophisticated threats, adapt faster to evolving tactics, and bring a new level of intelligence to financial crime compliance.
But with this promise comes a critical question: How exactly will generative AI or specifically, Large Language Models (LLM) transform financial crime compliance? The answer lies not just in its advanced capabilities but in its potential to alter the way we approach detection and prevention fundamentally. As we prepare for this next wave of innovation, it’s essential to understand the opportunities—and the challenges—that come with it.
When it comes to leveraging LLM in financial crime compliance, the possibilities are profound. Let’s break down some of the key areas where this technology can make a real impact:
- Data Generation and Augmentation: LLM has the unique ability to create synthetic data that closely mirrors real-world financial transactions. This isn’t just about filling in gaps; it’s about creating a rich, diverse dataset that can be used to train machine learning models more effectively. This is particularly valuable for fintech startups that may not have extensive historical data to draw from. With generative AI, they can test and deploy robust financial crime solutions while preserving the privacy of sensitive information. It’s like having a virtual data lab that’s always ready for experimentation.
- Unsupervised Anomaly Detection: Traditional systems often struggle to catch the nuanced, sophisticated patterns of modern financial crime. Large language models, however, can learn the complex behaviours of legitimate transactions and use this understanding as a baseline. When a new transaction deviates from this learned norm, it raises a red flag. These models can detect subtle irregularities that traditional rule-based systems or simpler machine learning algorithms might overlook, providing a more refined, proactive defence against potential fraud or money laundering.
- Automating the Investigation Process: Compliance professionals know the grind of sifting through endless alerts and drafting investigation reports. Generative AI offers a smarter way forward. By automating the creation of summaries, reports, and investigation notes, it frees up valuable time for compliance teams to focus on what really matters: strategic decision-making and complex case analysis. This isn’t just about making things faster—it’s about enabling a deeper, more insightful investigative process.
- Scenario Simulation and Risk Assessment: Generative AI can simulate countless financial transaction scenarios, assessing their risk levels based on historical data and regulatory requirements. This capability allows financial institutions to anticipate and prepare for a wide range of potential threats. It’s not just about reacting to crime; it’s about being ready for what comes next, armed with the insights needed to stay one step ahead.
To truly appreciate the transformative power of generative AI, we need to take a closer look at two critical areas: anomaly detection and explainability. These are the foundations upon which the future of financial crime compliance will be built.
Anomaly detection
One of the perennial challenges in fraud detection is the reliance on labelled data, where traditional machine learning models need clear examples of both legitimate and fraudulent transactions to learn from. This can be a significant bottleneck. After all, obtaining such labelled data—especially for emerging or sophisticated fraud schemes—is not only time-consuming but also often incomplete. This is where generative AI steps in, offering a fresh perspective with its capability for unsupervised anomaly detection, bypassing the need for labelled datasets.
To understand how this works, let’s break it down.
Traditional Unsupervised ML Approach
Typically, financial institutions using unsupervised machine learning might deploy clustering algorithms like k-means. Here’s how it works: transactions are grouped into clusters based on various features—transaction amount, time of day, location, and so on. Anomalies are then identified as transactions that don’t fit neatly into any of these clusters or exhibit characteristics that deviate significantly from the norm.
While this method has its merits, it can struggle to keep up with the complexity of modern fraud patterns. What happens when the anomalies are subtle or when legitimate variations are mistakenly flagged? The result is a system that can’t always distinguish between a genuine threat and a benign fluctuation.
Generative AI Approach
Generative AI offers a more nuanced solution. Consider the use of a variational autoencoder (VAE). Instead of relying on predefined labels, a VAE learns the underlying distribution of normal transactions by reconstructing them during training. Think of it as the model teaching itself what “normal” looks like. As it learns, the VAE can even generate synthetic transactions that closely resemble real ones, effectively creating a virtual landscape of typical behavior.
Once trained, this model becomes a powerful tool for anomaly detection. Here’s how: every incoming transaction is reconstructed by the VAE and compared to its original version. Transactions that deviate significantly, exhibiting high reconstruction errors, are flagged as potential anomalies. It’s like having a highly sensitive radar that picks up on the slightest deviations from the expected course. Moreover, by generating synthetic transactions and comparing them to real ones, the model can spot discrepancies that might otherwise go unnoticed.
This isn’t just an incremental improvement—it’s a leap forward. Generative AI’s ability to capture the intricate relationships within transaction data means it can detect anomalies with greater accuracy, reducing false positives and enhancing the overall effectiveness of fraud detection.
Explainability and Automated STR Reporting in Local Languages
One of the most pressing issues in machine learning (ML)-based systems is their often opaque decision-making process. For compliance officers and regulators tasked with understanding why a certain transaction was flagged, this lack of transparency can be a significant hurdle. Enter explainability techniques like LIME and SHAP. These tools are designed to peel back the layers of complex generative AI models, offering insights into how and why specific decisions were made. It’s like shining a light into the black box, providing much-needed clarity in a landscape where every decision could have significant implications.
But explainability is only one piece of the puzzle. Compliance is a global game, played on a field marked by varied and often stringent regulatory requirements. This is where generative AI’s natural language processing (NLP) capabilities come into play, revolutionizing how suspicious transaction reports (STRs) are generated and communicated. Imagine a system that can not only identify suspicious activities but also automatically draft detailed, accurate STRs in multiple languages, tailored to the specific regulatory nuances of each jurisdiction.
This is more than just a time-saver; it’s a transformative tool that ensures compliance officers can operate seamlessly across borders. By automating the generation of STRs in local languages, AI not only speeds up the process but also reduces the risk of miscommunication or regulatory missteps. It’s about making compliance more accessible and more effective, no matter where you are in the world.
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Upcoming Challenges
While the potential of generative AI is undeniably transformative, it’s not without its hurdles. From technical intricacies to regulatory constraints, there are several challenges that must be navigated to fully harness this technology in the fight against financial crime.
LLMs and Long Text Processing
One of the key challenges is ensuring that Generative Language Models (GLMs) like the Large Language Model (LLM) go beyond simple tasks like summarization to demonstrate true analytical intelligence. The introduction of Gemini 1.5 is a step forward, bringing enhanced capabilities for processing long texts. Yet, the question remains: can these models truly grasp the complexities of financial transactions and provide actionable insights? It’s not just about understanding more data; it’s about understanding it better.
Implementation Hurdles
- Data Quality and Preprocessing: Generative AI models are only as good as the data they’re trained on. Inconsistent or low-quality data can skew results, leading to false positives or overlooked threats. For financial institutions, ensuring clean, standardized, and comprehensive datasets is not just important—it’s imperative. This involves meticulous data preprocessing, including feature engineering, normalization, and handling missing values. Each step is crucial to preparing the data for training, ensuring that the models can perform at their best.
- Model Training and Scalability: Training large-scale models like LLMs and GANs is no small feat. The process is computationally intensive, requiring vast resources and advanced infrastructure. Scalability becomes a critical issue here. Strategies like distributed training and model parallelization, along with efficient hardware utilization, are needed to make these models not just a technological possibility but a practical tool for real-world AML/CFT systems.
- Evaluation Metrics and Interpretability: How do we measure success in generative AI for financial crime compliance? Traditional metrics like reconstruction error or sample quality don’t always capture the whole picture. In this context, evaluation criteria need to be more nuanced, combining these general metrics with domain-specific ones that reflect the unique demands of AML/CFT. But it’s not just about performance. The interpretability of these models is equally vital. Without clear, understandable outputs, building trust with regulators and compliance officers remains a significant challenge.
- Potential Limitations and Pitfalls: As powerful as generative AI can be, it’s not infallible. These models can inherit biases and inconsistencies from their training data, leading to unreliable or even harmful outputs. It’s a risk that cannot be ignored. Implementing robust techniques for bias detection and mitigation, alongside rigorous risk assessment and continuous monitoring, is essential to ensure that generative AI is used safely and responsibly in financial crime compliance.
Regulatory and Ethical Considerations
As we venture into the integration of generative AI in anti-money laundering (AML) and counter-financing of terrorism (CFT) systems, it’s not just the technological challenges that we need to be mindful of. The regulatory and ethical landscape presents its own set of complexities, demanding careful navigation and proactive engagement with stakeholders.
Regulatory Compliance
The deployment of generative AI in AML/CFT isn’t simply about adopting new technology—it’s about doing so within a framework that respects the rule of law. This means a close, ongoing dialogue with regulatory bodies to ensure that these advanced systems align with existing laws, guidelines, and best practices. Establishing clear standards for the development, validation, and governance of AI models is not just advisable; it’s essential. Without a robust regulatory framework, even the most sophisticated AI models could become liabilities rather than assets.
Ethical AI and Fairness
In the realm of financial crime compliance, the stakes are high. Decisions influenced by AI models can have significant impacts on individuals and businesses, which makes fairness and non-discrimination more than just ethical considerations—they are imperatives. Generative AI systems must be rigorously tested for biases and unintended consequences. This means implementing rigorous validation processes to ensure that these models uphold the principles of ethical AI and fairness, especially in high-stakes scenarios. We’re not just building technology; we’re building trust.
Privacy and Data Protection
With generative AI comes the promise of advanced capabilities like synthetic data generation and privacy-preserving analytics. But these innovations must be handled with care. Compliance with data protection regulations and the safeguarding of customer privacy rights should be at the forefront of any implementation strategy. Clear policies and robust safeguards are crucial to protect sensitive financial information, ensuring that the deployment of these models doesn’t inadvertently compromise the very data they are designed to protect.
Model Security and Robustness
Generative AI models, such as LLMs and GANs, bring immense power but also vulnerabilities. The risk of adversarial attacks or model extraction cannot be overlooked. To safeguard the integrity and confidentiality of these models, robust security measures need to be put in place. Techniques like differential privacy, watermarking, and the use of secure enclaves should be explored and implemented to protect these systems from malicious exploitation. It’s about creating not just intelligent models, but resilient ones.
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Gen AI in Tookitaki FinCense
Tookitaki’s FinCense platform is pioneering the use of Generative AI to redefine financial crime compliance. We are actively collaborating with our clients through lighthouse projects to put the advanced Gen AI capabilities of FinCense to the test. Powered by a local LLM engine built on Llama models, FinCense introduces a suite of features designed to transform the compliance landscape.
One standout feature is the Smart Disposition Engine, which automates the handling of alerts with remarkable efficiency. By incorporating rules, policy checklists, and reporting in local languages, this engine streamlines the entire alert management process, cutting manual investigation time by an impressive 50-60%. It’s a game-changer for compliance teams, enabling them to focus on complex cases rather than getting bogged down in routine tasks.
Then there’s FinMate, an AI investigation copilot tailored to the unique needs of AML compliance professionals. Based on a local LLM model, FinMate serves as an intelligent assistant, offering real-time support during investigations. It doesn’t just provide information; it delivers actionable insights and suggestions that help compliance teams navigate through cases more swiftly and effectively.
Moreover, the platform’s Local Language Reporting feature enhances its usability across diverse regions. By supporting multiple local languages, FinCense ensures that compliance teams can manage alerts and generate reports seamlessly, regardless of their location. This localization capability is more than just a convenience—it’s a critical tool that enables teams to work more effectively within their regulatory environments.
With these cutting-edge features, Tookitaki’s FinCense platform is not just keeping up with the evolution of financial crime compliance—it’s leading the way, setting new standards for what’s possible with Generative AI in this critical field.
Final Thoughts
The future of financial crime compliance is set to be revolutionized by the advancements in AI and ML. Over the next few years, generative AI will likely become an integral part of our arsenal, pushing the boundaries of what’s possible in detecting and preventing illicit activities. Large Language Models (LLMs) like GPT-3 and its successors are not just promising—they are poised to transform the landscape. From automating the generation of Suspicious Activity Reports (SARs) to conducting in-depth risk assessments and offering real-time decision support to compliance analysts, these models are redefining what’s possible in the AML/CFT domain.
But LLMs are only part of the equation. Generative Adversarial Networks (GANs) are also emerging as a game-changer. Their ability to create synthetic, privacy-preserving datasets is a breakthrough for financial institutions struggling with limited access to real-world data. These synthetic datasets can be used to train and test machine learning models, making it easier to simulate and study complex financial crime scenarios without compromising sensitive information.
The real magic, however, lies in the convergence of LLMs and GANs. Imagine a system that can not only detect anomalies but also generate synthetic transaction narratives or provide explanations for suspicious activities. This combination could significantly enhance the interpretability and transparency of AML/CFT systems, making it easier for compliance teams to understand and act on the insights provided by these advanced models.
Embracing these technological advancements isn’t just an option—it’s a necessity. The challenge will be in implementing them responsibly, ensuring they are used to build a more secure and transparent financial ecosystem. This will require a collaborative effort between researchers, financial institutions, and regulatory bodies. Only by working together can we address the technical and ethical challenges that come with deploying generative AI, ensuring that these powerful tools are used to their full potential—responsibly and effectively.
The road ahead is filled with promise, but it’s also lined with challenges. By navigating this path with care and foresight, we can leverage generative AI to not only stay ahead of financial criminals but to create a future where the financial system is safer and more resilient than ever before.
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AFASA Explained: What the Philippines’ New Anti-Scam Law Really Means for Banks, Fintechs, and Consumers
If there is one thing everyone in the financial industry felt in the last few years, it was the speed at which scams evolved. Fraudsters became smarter, attacks became faster, and stolen funds moved through dozens of accounts in seconds. Consumers were losing life savings. Banks and fintechs were overwhelmed. And regulators had to act.
This is the backdrop behind the Anti-Financial Account Scamming Act (AFASA), Republic Act No. 12010 — the Philippines’ most robust anti-scam law to date. AFASA reshapes how financial institutions detect fraud, protect accounts, coordinate with one another, and respond to disputes.
But while many have written about the law, most explanations feel overly legalistic or too high-level. What institutions really need is a practical, human-friendly breakdown of what AFASA truly means in day-to-day operations.
This blog does exactly that.
What Is AFASA? A Simple Explanation
AFASA exists for a clear purpose: to protect consumers from rapidly evolving digital fraud. The law recognises that as more Filipinos use e-wallets, online banking, and instant payments, scammers have gained more opportunities to exploit vulnerabilities.
Under AFASA, the term financial account is broad. It includes:
- Bank deposit accounts
- Credit card and investment accounts
- E-wallets
- Any account used to access financial products and services
The law focuses on three main categories of offences:
1. Money Muling
This covers the buying, selling, renting, lending, recruiting, or using of financial accounts to receive or move illicit funds. Many young people and jobseekers were unknowingly lured into mule networks — something AFASA squarely targets.
2. Social Engineering Schemes
From phishing to impersonation, scammers have mastered psychological manipulation. AFASA penalises the use of deception to obtain sensitive information or access accounts.
3. Digital Fraud and Account Tampering
This includes unauthorised transfers, synthetic identities, hacking incidents, and scams executed through electronic communication channels.
In short: AFASA criminalises both the scammer and the infrastructure used for the scam — the accounts, the networks, and the people recruited into them.
Why AFASA Became Necessary
Scams in the Philippines reached a point where traditional fraud rules, old operational processes, and siloed detection systems were not enough.
Scam Trend 1: Social engineering became hyper-personal
Fraudsters learned to sound like bank agents, government officers, delivery riders, HR recruiters — even loved ones. OTP harvesting and remote access scams became common.
Scam Trend 2: Real-time payments made fraud instant
InstaPay and other instant channels made moving money convenient — but also made stolen funds disappear before anyone could react.
Scam Trend 3: Mule networks became organised
Criminal groups built structured pipelines of mule accounts, often recruiting vulnerable populations such as students, OFWs, and low-income households.
Scam Trend 4: E-wallet adoption outpaced awareness
A fast-growing digital economy meant millions of first-time digital users were exposed to sophisticated scams they were not prepared for.
AFASA was designed to break this cycle and create a safer digital financial environment.
New Responsibilities for Banks and Fintechs Under AFASA
AFASA introduces significant changes to how institutions must protect accounts. It is not just a compliance exercise — it demands real operational transformation.
These responsibilities are further detailed in new BSP circulars that accompany the law.
1. Stronger IT Risk Controls
Financial institutions must now implement advanced fraud and cybersecurity controls such as:
- Device fingerprinting
- Geolocation monitoring
- Bot detection
- Blacklist screening for devices, merchants, and IPs
These measures allow institutions to understand who is accessing accounts, how, and from where — giving them the tools to detect anomalies before fraud occurs.
2. Mandatory Fraud Management Systems (FMS)
Both financial institutions and clearing switch operators (including InstaPay and PESONet) must operate real-time systems that:
- Flag suspicious activity
- Block disputed or high-risk transactions
- Detect behavioural anomalies
This ensures that fraud monitoring is consistent across the payment ecosystem — not just within individual institutions.
3. Prohibition on unsolicited clickable links
Institutions can no longer send clickable links or QR codes to customers unless explicitly initiated by the customer. This directly tackles phishing attacks that relied on spoofed messages.
4. Continuous customer awareness
Banks and fintechs must actively educate customers about:
- Cyber hygiene
- Secure account practices
- Fraud patterns and red flags
- How to report incidents quickly
Customer education is no longer optional — it is a formally recognised part of fraud prevention.
5. Shared accountability framework
AFASA moves away from the old “blame the victim” mentality. Fraud prevention is now a shared responsibility across:
- Financial institutions
- Account owners
- Third-party service providers
This model recognises that no single party can combat fraud alone.
The Heart of AFASA: Temporary Holding of Funds & Coordinated Verification
Among all the changes introduced by AFASA, this is the one that represents a true paradigm shift.
Previously, once stolen funds were transferred out, recovery was almost impossible. Banks had little authority to stop or hold the movement of funds.
AFASA changes that.
Temporary Holding of Funds
Financial institutions now have the authority — and obligation — to temporarily hold disputed funds for up to 30 days. This includes both the initial hold and any permitted extension. The purpose is simple:
freeze the money before it disappears.
Triggers for Temporary Holding
A hold can be initiated through:
- A victim’s complaint
- A suspicious transaction flagged by the institution’s FMS
- A request from another financial institution
This ensures that action can be taken proactively or reactively depending on the scenario.
Coordinated Verification Process
Once funds are held, institutions must immediately begin a coordinated process that involves:
- The originating institution
- Receiving institutions
- Clearing entities
- The account owners involved
This process validates whether the transaction was legitimate or fraudulent. It creates a formal, structured, and time-bound mechanism for investigation.
Detailed Transaction Logs Are Now Mandatory
Institutions must maintain comprehensive transaction logs — including device information, authentication events, IP addresses, timestamps, password changes, and more. Logs must be retained for at least five years.
This gives investigators the ability to reconstruct transactions and understand the full context of a disputed transfer.
An Industry-Wide Protocol Must Be Built
AFASA requires the entire industry to co-develop a unified protocol for handling disputed funds and verification. This ensures consistency, promotes collaboration, and reduces delays during investigations.
This is one of the most forward-thinking aspects of the law — and one that will significantly raise the standard of scam response in the country.
BSP’s Expanded Powers Through CAPO
AFASA also strengthens regulatory oversight.
BSP’s Consumer Account Protection Office (CAPO) now has the authority to:
- Conduct inquiries into financial accounts suspected of involvement in fraud
- Access financial account information required to investigate prohibited acts
- Coordinate with law enforcement agencies
Crucially, during these inquiries, bank secrecy laws and the Data Privacy Act do not apply.
This is a major shift that reflects the urgency of combating digital fraud.
Crucially, during these inquiries, bank secrecy laws and the Data Privacy Act do not apply.
This is a major shift that reflects the urgency of combating digital fraud.
Penalties Under AFASA
AFASA imposes serious penalties to deter both scammers and enablers:
1. Criminal penalties for money muling
Anyone who knowingly participates in using, recruiting, or providing accounts for illicit transfers is liable to face imprisonment and fines.
2. Liability for failing to protect funds
Institutions may be held accountable if they fail to properly execute a temporary hold when a dispute is raised.
3. Penalties for improper holding
Institutions that hold funds without valid reason may also face sanctions.
4. Penalties for malicious reporting
Consumers or individuals who intentionally file false reports may also be punished.
5. Administrative sanctions
Financial institutions that fail to comply with AFASA requirements may be penalised by BSP.
The penalties underscore the seriousness with which the government views scam prevention.
What AFASA Means for Banks and Fintechs: The Practical Reality
Here’s what changes on the ground:
1. Fraud detection becomes real-time — not after-the-fact
Institutions need modern systems that can flag abnormal behaviour within seconds.
2. Dispute response becomes faster
Timeframes are tight, and institutions need streamlined internal workflows.
3. Collaboration is no longer optional
Banks, e-wallets, payment operators, and regulators must work as one system.
4. Operational pressure increases
Fraud teams must handle verification, logging, documentation, and communication under strict timelines.
5. Liability is higher
Institutions may be held responsible for lapses in protection, detection, or response.
6. Technology uplift becomes non-negotiable
Legacy systems will struggle to meet AFASA’s requirements — particularly around logging, behavioural analytics, and real-time detection.
How Tookitaki Helps Institutions Align With AFASA
AFASA sets a higher bar for fraud prevention. Tookitaki’s role as the Trust Layer to Fight Financial Crime helps institutions strengthen their AFASA readiness with intelligent, real-time, and collaborative capabilities.
1. Early detection of money mule networks
Through the AFC Ecosystem’s collective intelligence, institutions can detect mule-like patterns sooner and prevent illicit transactions before they spread across the system.
2. Real-time monitoring aligned with AFASA needs
FinCense’s advanced transaction monitoring engine flags suspicious activity instantly — helping institutions support temporary holding procedures and respond within required timelines.
3. Deep behavioural intelligence and comprehensive logs
Tookitaki provides the contextual understanding needed to trace disputed transfers, reconstruct transaction paths, and support investigative workflows.
4. Agentic AI to accelerate investigations
FinMate, the AI investigation copilot, streamlines case analysis, surfaces insights quickly, and reduces investigation workload — especially crucial when time-sensitive AFASA processes are triggered.
5. Federated learning for privacy-preserving model improvement
Institutions can enhance detection models without sharing raw data, aligning with AFASA’s broader emphasis on secure and responsible handling of financial information.
Together, these capabilities enable banks and fintechs to strengthen fraud defences, modernise their operations, and protect financial accounts with confidence.
Looking Ahead: AFASA’s Long-Term Impact
AFASA is not a one-time regulatory update — it is a structural shift in how the Philippine financial ecosystem handles scams.
Expect to see:
- More real-time fraud rules and guidance
- Industry-wide technical standards for dispute management
- Higher expectations for digital onboarding and authentication
- Increased coordination between banks, fintechs, and regulators
- Greater focus on intelligence-sharing and network-level detection
Most importantly, AFASA lays the foundation for a safer, more trusted digital economy — one where consumers have confidence that institutions and regulators can protect them from fast-evolving threats.
Conclusion
AFASA represents a turning point in the Philippines’ fight against financial scams. It transforms how institutions detect fraud, protect accounts, collaborate with others, and support customers. For banks and fintechs, the message is clear: the era of passive fraud response is over.
The institutions that will thrive under AFASA are those that embrace real-time intelligence, strengthen operational resilience, and adopt technology that enables them to stay ahead of criminal innovation.
The Philippines has taken a bold step toward a safer financial system — and now, it’s time for the industry to match that ambition.
Beyond the Smoke: How Illicit Tobacco Became Australia’s New Money-Laundering Engine
In early December 2025, Australian authorities executed one of the most significant financial crime crackdowns of the year — dismantling a sprawling A$150 million money-laundering syndicate operating across New South Wales. What began as an illicit tobacco investigation quickly escalated into a full-scale disruption of an organised network using shell companies, straw directors, and cross-border transfers to wash millions in criminal proceeds.
This case is more than a police success story. It offers a window into Australia’s evolving financial crime landscape — one where illicit trade, complex laundering tactics, and systemic blind spots intersect to form a powerful engine for organised crime.
The Anatomy of an Illicit Tobacco Syndicate
The syndicate uncovered by Australian Federal Police (AFP), NSW Police, AUSTRAC, and the Illicit Tobacco Taskforce was not a small-time criminal operation. It was a coordinated enterprise that combined distribution networks, financial handlers, logistics operators, and front companies into a single ecosystem.
What investigators seized tells a clear story:
- 10 tonnes of illicit tobacco
- 2.1 million cigarettes packaged for distribution
- Over A$300,000 in cash
- A money-counting machine
- Luxury items, including a Rolex
- A firearm and ammunition
These items paint the picture of a network with scale, structure, and significant illicit revenue streams.
Why illicit tobacco?
Australia’s tobacco excise — among the highest globally — has unintentionally created a lucrative black market. Criminal groups can import or manufacture tobacco products cheaply and sell them at prices far below legal products, yet still generate enormous margins.
As a result, illicit tobacco has grown into one of the country's most profitable predicate crimes, fuelling sophisticated laundering operations.
The Laundering Playbook: How A$150M Moved Through the System
Behind the physical contraband lay an even more intricate financial scheme. The syndicate relied on three primary laundering techniques:
a) Straw Directors and Front Companies
The criminals recruited individuals to:
- Set up companies
- Open business bank accounts
- Serve as “directors” in name only
These companies had no legitimate operations — no payroll, no expenses, no suppliers. Their sole function was to provide a façade of legitimacy for high-volume financial flows.
b) Rapid Layering Across Multiple Accounts
Once operational, these accounts saw intense transactional activity:
- Large incoming deposits
- Immediate outbound transfers
- Funds bouncing between newly created companies
- Volumes inconsistent with stated business profiles
This rapid movement made it difficult for financial institutions to track the money trail or link transactions back to illicit tobacco proceeds.
c) Round-Tripping Funds Overseas
To further obscure the origin of funds, the syndicate:
- Sent money to overseas accounts
- Repatriated it disguised as legitimate business payments or “invoice settlements”
To a bank, these flows could appear routine. But in reality, they were engineered to sever any detectable connection to criminal activity.
Why It Worked: Systemic Blind Spots Criminals Exploited
This laundering scheme did not succeed simply because it was complex — it succeeded because it targeted specific weaknesses in Australia’s financial crime ecosystem.
a) High-Profit Illicit Trade
Australia’s tobacco excise structure unintentionally fuels criminal profitability. With margins this high, illicit networks have the financial resources to build sophisticated laundering infrastructures.
b) Fragmented Visibility Across Entities
Most financial institutions only see one customer at a time. They do not automatically connect multiple companies created by the same introducer, or accounts accessed using the same device fingerprints.
This allows straw-director networks to thrive.
c) Legacy Rule-Based Monitoring
Traditional AML systems rely heavily on static thresholds and siloed rules:
- “Large transaction” alerts
- Basic velocity checks
- Limited behavioural analysis
Criminals know this — and structure their laundering techniques to evade these simplistic rules.
d) Cross-Border Complexity
Once funds leave Australia, visibility drops sharply. When they return disguised as payments from overseas vendors, they often blend into the financial system undetected.
Red Flags Financial Institutions Should Watch For
This case provides powerful lessons for compliance teams. Below are the specific indicators FIs should be alert to.
KYC & Profile Red Flags
- Directors with little financial or business experience
- Recently formed companies with generic business descriptions
- Multiple companies tied to the same:
- phone numbers
- IP addresses
- mailing addresses
- No digital footprint or legitimate online presence
Transaction Red Flags
- High turnover in accounts with minimal retained balances
- Rapid movement of funds with no clear business rationale
- Structured cash deposits
- Transfers between unrelated companies with no commercial relationship
- Overseas remittances followed by identical inbound amounts weeks later
Network Behaviour Red Flags
- Shared device IDs used to access multiple company accounts
- Overlapping beneficiaries across supposedly unrelated entities
- Repeated transactions involving known high-risk sectors (e.g., tobacco, logistics, import/export)
These indicators form the behavioural “signature” of a sophisticated laundering ring.
How Tookitaki Strengthens Defences Against These Schemes
The A$150 million case demonstrates why financial institutions need AML systems that move beyond simple rule-based detection.
Tookitaki helps institutions strengthen their defences by focusing on:
a) Typology-Driven Detection
Pre-built scenarios based on real-world criminal behaviours — including straw directors, shell companies, layering, and round-tripping — ensure early detection of organised laundering patterns.
b) Network Relationship Analysis
FinCense connects multiple entities through shared attributes (IP addresses, devices, common directors), surfacing hidden networks that traditional systems miss.
c) Behavioural Analytics
Instead of static thresholds, Tookitaki analyses patterns in account behaviour, highlighting anomalies even when individual transactions seem normal.
d) Collaborative Intelligence via the AFC Ecosystem
Insights from global financial crime experts empower institutions to stay ahead of emerging laundering techniques, including those tied to illicit trade.
e) AI-Powered Investigation Support
FinMate accelerates investigations by providing contextual insights, summarising risks, and identifying links across accounts and entities.
Together, these capabilities help institutions detect sophisticated laundering activity long before it reaches a scale of A$150 million.
Conclusion: Australia’s New Financial Crime Reality
The A$150 million illicit tobacco laundering bust is more than a headline — it’s a signal.
Illicit trade-based laundering is expanding. Criminal networks are becoming more organised. And traditional monitoring systems are no longer enough to keep up.
For banks, fintechs, regulators, and law enforcement, the implications are clear:
- Financial crime in Australia is evolving.
- Laundering networks now mirror corporate structures.
- Advanced AML technology is essential to stay ahead.
As illicit tobacco continues to grow as a predicate offence, the financial system must be prepared for more complex laundering operations — and more aggressive attempts to exploit gaps in institutional defences.
Inside Australia’s $200 Million Psychic Scam: How a Mother–Daughter Syndicate Manipulated Victims and Laundered Millions
1. Introduction of the Scam
In one of Australia’s most astonishing financial crime cases, police arrested a mother and daughter in November 2025 for allegedly running a two hundred million dollar fraud and money laundering syndicate. Their cover was neither a shell company nor a darknet marketplace. They presented themselves as psychics who claimed the ability to foresee danger, heal emotional wounds, and remove spiritual threats that supposedly plagued their clients.
The case captured national attention because it combined two worlds that rarely collide at this scale. Deep emotional manipulation and sophisticated financial laundering. What seemed like harmless spiritual readings turned into a highly profitable criminal enterprise that operated quietly for years.
The scam is a stark reminder that fraud is evolving beyond impersonation calls and fake investment pitches. Criminals are finding new ways to step into the most vulnerable parts of people’s lives. Understanding this case helps financial institutions identify similar behavioural and transactional signals before they escalate into million dollar losses.
2. Anatomy of the Scam
Behind the illusion of psychic counselling was a methodical, multi layered fraud structure designed to extract wealth while maintaining unquestioned authority over victims.
A. Establishing Irresistible Authority
The syndicate created an aura of mystique. They styled themselves as spiritual guides with special insight into personal tragedies, relationship breakdowns, and looming dangers. This emotional framing created an asymmetric relationship. The victims were the ones seeking answers. The scammers were the ones providing them.
B. Cultivating Dependence Over Time
Victims did not transfer large sums immediately. The scammers first built trust through frequent sessions, emotional reinforcement, and manufactured “predictions” that aligned with the victims’ fears or desires. Once trust solidified, dependence followed. Victims began to rely on the scammers’ counsel for major life decisions.
C. Escalating Financial Requests Under Emotional Pressure
As dependence grew, payments escalated. Victims were told that removing a curse or healing an emotional blockage required progressively higher financial sacrifices. Some were convinced that failing to comply would bring harm to themselves or loved ones. Fear became the payment accelerator.
D. Operating as a Structured Syndicate
Although the mother and daughter fronted the scheme, police uncovered several associates who helped receive funds, manage assets, and distance the organisers from the flow of money. This structure mirrored the operational models of organised fraud groups.
E. Exploiting the Legitimacy of “Services”
The payments appeared as consulting or spiritual services, which are common and often unregulated. This gave the syndicate a major advantage. Bank transfers looked legitimate. Transaction descriptions were valid. And the activity closely resembled the profiles of other small service providers.
This blending of emotional exploitation and professional disguise is what made the scam extraordinarily effective.
3. Why Victims Fell for It: The Psychology at Play
People often believe financial crime succeeds because victims are careless. This case shows the opposite. The victims were targeted precisely because they were thoughtful, concerned, and searching for help.
A. Authority and Expertise Bias
When someone is positioned as an expert, whether a doctor, advisor, or psychic, their guidance feels credible. Victims trusted the scammers’ “diagnosis” because it appeared grounded in unique insight.
B. Emotional Vulnerability
Many victims were dealing with grief, loneliness, uncertainty, or family conflict. These emotional states are fertile ground for manipulation. Scammers do not need access to bank accounts when they already have access to the human heart.
C. The Illusion of Personal Connection
Fraudsters used personalised predictions and tailored spiritual advice. This created a bond that felt intimate and unique. When a victim feels “understood,” their defences lower.
D. Fear Based Decision Making
Warnings like “your family is at risk unless you act now” are extremely powerful. Under fear, rationality is overshadowed by urgency.
E. The Sunk Cost Trap
Once a victim has invested a significant amount, they continue paying to “finish the process” rather than admit the entire relationship was fraudulent.
Understanding these psychological drivers is essential. They are increasingly common across romance scams, deepfake impersonations, sham consultant schemes, and spiritual frauds across APAC.
4. The Laundering Playbook Behind the Scam
Once the scammers extracted money, the operation transitioned into a textbook laundering scheme designed to conceal the origin of illicit funds and distance the perpetrators from the victims.
A. Multi Layered Account Structures
Money flowed through personal accounts, associates’ accounts, and small businesses that provided cover for irregular inflows. This layering reduced traceability.
B. Conversion Into High Value Assets
Luxury goods, vehicles, property, and jewellery were used to convert liquid funds into stable, movable wealth. These assets can be held long term or liquidated in smaller increments to avoid detection.
C. Cross Jurisdiction Fund Movement
Authorities suspect that portions of the money were transferred offshore. Cross border movements complicate the investigative trail and exploit discrepancies between regulatory frameworks.
D. Cash Based Structuring
Victims were sometimes encouraged to withdraw cash, buy gold, or convert savings into prepaid instruments. These activities create gaps in the financial record that help obscure illicit origins.
E. Service Based Laundering Through Fake Invoices
The scammers reportedly issued or referenced “healing services,” “spiritual cleansing,” and similar descriptions. Because these services are intangible, verifying their legitimacy is difficult.
The laundering strategy was not unusual. What made it hard to detect was its intimate connection to a long term emotional scam.
5. Red Flags for FIs
Financial institutions can detect the early signals of scams like this through behavioural and transactional monitoring.
Key Transaction Red Flags
- Repeated high value transfers to individuals claiming to provide advisory or spiritual services.
- Elderly or vulnerable customers making sudden, unexplained payments to unfamiliar parties.
- Transfers that increase in value and frequency over weeks or months.
- Sudden depletion of retirement accounts or long held savings.
- Immediate onward transfers from the recipient to offshore banks.
- Significant cash withdrawals following online advisory sessions.
- Purchases of gold, jewellery, or luxury goods inconsistent with customer profiles.
Key Behavioural Red Flags
- Customers showing visible distress or referencing “urgent help” required by an adviser.
- Hesitation or refusal to explain the purpose of a transaction.
- Uncharacteristic secrecy regarding financial decisions.
- Statements referencing curses, spiritual threats, or emotional manipulation.
KYC and Profile Level Red Flags
- Service providers with no registered business presence.
- Mismatch between declared income and transaction activity.
- Shared addresses or accounts among individuals connected to the same adviser.
Financial institutions that identify these early signals can prevent significant losses and support customers before the harm intensifies.
6. How Tookitaki Strengthens Defences
Modern financial crime is increasingly psychological, personalised, and disguised behind legitimate looking service payments. Tookitaki equips institutions with the intelligence and technology to identify these patterns early.
A. Behavioural Analytics Trained on Real World Scenarios
FinCense analyses changes in spending, emotional distress indicators, unusual advisory payments, and deviations from customer norms. These subtle behavioural cues often precede standard red flags.
B. Collective Intelligence Through the AFC Ecosystem
Compliance experts across Asia Pacific contribute emerging fraud scenarios, including social engineering, spiritual scams, and coercion based typologies. Financial institutions benefit from insights grounded in real world criminal activity, not static rules.
C. Dynamic Detection Models for Service Based Laundering
FinCense distinguishes between ordinary professional service payments and laundering masked as consulting or spiritual fees. This is essential for cases where invoice based laundering is the primary disguise.
D. Automated Threshold Optimisation and Simulation
Institutions can simulate how new scam scenarios would trigger alerts and generate thresholds that adapt to the bank’s customer base. This reduces false positives while improving sensitivity.
E. Early Intervention for Vulnerable Customers
FinCense helps identify elderly or high risk individuals who show sudden behavioural changes. Banks can trigger outreach before the customer falls deeper into manipulation.
F. Investigator Support Through FinMate
With FinMate, compliance teams receive contextual insights, pattern explanations, and recommended investigative paths. This accelerates understanding and action on complex scam patterns.
Together, these capabilities form a proactive defence system that protects victims and reinforces institutional trust.
7. Conclusion
The two hundred million dollar psychic scam is more than a headline. It is a lesson in how deeply fraud can infiltrate personal lives and how effectively criminals can disguise illicit flows behind emotional manipulation. It is also a warning that traditional monitoring systems, which rely on transactional patterns alone, may miss the early behavioural signals that reveal the true nature of emerging scams.
For financial institutions, two capabilities are becoming non negotiable.
- Understanding the human psychology behind financial crime.
- Using intelligent, adaptive systems that can detect the behavioural and transactional interplay.
Tookitaki helps institutions meet both challenges. Through FinCense and the AFC Ecosystem, institutions benefit from collective intelligence, adaptive detection, and technology designed to understand the complexity of modern fraud.
As scams continue to evolve, so must defences. Building stronger systems today protects customers, prevents loss, and strengthens trust across the financial ecosystem.
AFASA Explained: What the Philippines’ New Anti-Scam Law Really Means for Banks, Fintechs, and Consumers
If there is one thing everyone in the financial industry felt in the last few years, it was the speed at which scams evolved. Fraudsters became smarter, attacks became faster, and stolen funds moved through dozens of accounts in seconds. Consumers were losing life savings. Banks and fintechs were overwhelmed. And regulators had to act.
This is the backdrop behind the Anti-Financial Account Scamming Act (AFASA), Republic Act No. 12010 — the Philippines’ most robust anti-scam law to date. AFASA reshapes how financial institutions detect fraud, protect accounts, coordinate with one another, and respond to disputes.
But while many have written about the law, most explanations feel overly legalistic or too high-level. What institutions really need is a practical, human-friendly breakdown of what AFASA truly means in day-to-day operations.
This blog does exactly that.
What Is AFASA? A Simple Explanation
AFASA exists for a clear purpose: to protect consumers from rapidly evolving digital fraud. The law recognises that as more Filipinos use e-wallets, online banking, and instant payments, scammers have gained more opportunities to exploit vulnerabilities.
Under AFASA, the term financial account is broad. It includes:
- Bank deposit accounts
- Credit card and investment accounts
- E-wallets
- Any account used to access financial products and services
The law focuses on three main categories of offences:
1. Money Muling
This covers the buying, selling, renting, lending, recruiting, or using of financial accounts to receive or move illicit funds. Many young people and jobseekers were unknowingly lured into mule networks — something AFASA squarely targets.
2. Social Engineering Schemes
From phishing to impersonation, scammers have mastered psychological manipulation. AFASA penalises the use of deception to obtain sensitive information or access accounts.
3. Digital Fraud and Account Tampering
This includes unauthorised transfers, synthetic identities, hacking incidents, and scams executed through electronic communication channels.
In short: AFASA criminalises both the scammer and the infrastructure used for the scam — the accounts, the networks, and the people recruited into them.
Why AFASA Became Necessary
Scams in the Philippines reached a point where traditional fraud rules, old operational processes, and siloed detection systems were not enough.
Scam Trend 1: Social engineering became hyper-personal
Fraudsters learned to sound like bank agents, government officers, delivery riders, HR recruiters — even loved ones. OTP harvesting and remote access scams became common.
Scam Trend 2: Real-time payments made fraud instant
InstaPay and other instant channels made moving money convenient — but also made stolen funds disappear before anyone could react.
Scam Trend 3: Mule networks became organised
Criminal groups built structured pipelines of mule accounts, often recruiting vulnerable populations such as students, OFWs, and low-income households.
Scam Trend 4: E-wallet adoption outpaced awareness
A fast-growing digital economy meant millions of first-time digital users were exposed to sophisticated scams they were not prepared for.
AFASA was designed to break this cycle and create a safer digital financial environment.
New Responsibilities for Banks and Fintechs Under AFASA
AFASA introduces significant changes to how institutions must protect accounts. It is not just a compliance exercise — it demands real operational transformation.
These responsibilities are further detailed in new BSP circulars that accompany the law.
1. Stronger IT Risk Controls
Financial institutions must now implement advanced fraud and cybersecurity controls such as:
- Device fingerprinting
- Geolocation monitoring
- Bot detection
- Blacklist screening for devices, merchants, and IPs
These measures allow institutions to understand who is accessing accounts, how, and from where — giving them the tools to detect anomalies before fraud occurs.
2. Mandatory Fraud Management Systems (FMS)
Both financial institutions and clearing switch operators (including InstaPay and PESONet) must operate real-time systems that:
- Flag suspicious activity
- Block disputed or high-risk transactions
- Detect behavioural anomalies
This ensures that fraud monitoring is consistent across the payment ecosystem — not just within individual institutions.
3. Prohibition on unsolicited clickable links
Institutions can no longer send clickable links or QR codes to customers unless explicitly initiated by the customer. This directly tackles phishing attacks that relied on spoofed messages.
4. Continuous customer awareness
Banks and fintechs must actively educate customers about:
- Cyber hygiene
- Secure account practices
- Fraud patterns and red flags
- How to report incidents quickly
Customer education is no longer optional — it is a formally recognised part of fraud prevention.
5. Shared accountability framework
AFASA moves away from the old “blame the victim” mentality. Fraud prevention is now a shared responsibility across:
- Financial institutions
- Account owners
- Third-party service providers
This model recognises that no single party can combat fraud alone.
The Heart of AFASA: Temporary Holding of Funds & Coordinated Verification
Among all the changes introduced by AFASA, this is the one that represents a true paradigm shift.
Previously, once stolen funds were transferred out, recovery was almost impossible. Banks had little authority to stop or hold the movement of funds.
AFASA changes that.
Temporary Holding of Funds
Financial institutions now have the authority — and obligation — to temporarily hold disputed funds for up to 30 days. This includes both the initial hold and any permitted extension. The purpose is simple:
freeze the money before it disappears.
Triggers for Temporary Holding
A hold can be initiated through:
- A victim’s complaint
- A suspicious transaction flagged by the institution’s FMS
- A request from another financial institution
This ensures that action can be taken proactively or reactively depending on the scenario.
Coordinated Verification Process
Once funds are held, institutions must immediately begin a coordinated process that involves:
- The originating institution
- Receiving institutions
- Clearing entities
- The account owners involved
This process validates whether the transaction was legitimate or fraudulent. It creates a formal, structured, and time-bound mechanism for investigation.
Detailed Transaction Logs Are Now Mandatory
Institutions must maintain comprehensive transaction logs — including device information, authentication events, IP addresses, timestamps, password changes, and more. Logs must be retained for at least five years.
This gives investigators the ability to reconstruct transactions and understand the full context of a disputed transfer.
An Industry-Wide Protocol Must Be Built
AFASA requires the entire industry to co-develop a unified protocol for handling disputed funds and verification. This ensures consistency, promotes collaboration, and reduces delays during investigations.
This is one of the most forward-thinking aspects of the law — and one that will significantly raise the standard of scam response in the country.
BSP’s Expanded Powers Through CAPO
AFASA also strengthens regulatory oversight.
BSP’s Consumer Account Protection Office (CAPO) now has the authority to:
- Conduct inquiries into financial accounts suspected of involvement in fraud
- Access financial account information required to investigate prohibited acts
- Coordinate with law enforcement agencies
Crucially, during these inquiries, bank secrecy laws and the Data Privacy Act do not apply.
This is a major shift that reflects the urgency of combating digital fraud.
Crucially, during these inquiries, bank secrecy laws and the Data Privacy Act do not apply.
This is a major shift that reflects the urgency of combating digital fraud.
Penalties Under AFASA
AFASA imposes serious penalties to deter both scammers and enablers:
1. Criminal penalties for money muling
Anyone who knowingly participates in using, recruiting, or providing accounts for illicit transfers is liable to face imprisonment and fines.
2. Liability for failing to protect funds
Institutions may be held accountable if they fail to properly execute a temporary hold when a dispute is raised.
3. Penalties for improper holding
Institutions that hold funds without valid reason may also face sanctions.
4. Penalties for malicious reporting
Consumers or individuals who intentionally file false reports may also be punished.
5. Administrative sanctions
Financial institutions that fail to comply with AFASA requirements may be penalised by BSP.
The penalties underscore the seriousness with which the government views scam prevention.
What AFASA Means for Banks and Fintechs: The Practical Reality
Here’s what changes on the ground:
1. Fraud detection becomes real-time — not after-the-fact
Institutions need modern systems that can flag abnormal behaviour within seconds.
2. Dispute response becomes faster
Timeframes are tight, and institutions need streamlined internal workflows.
3. Collaboration is no longer optional
Banks, e-wallets, payment operators, and regulators must work as one system.
4. Operational pressure increases
Fraud teams must handle verification, logging, documentation, and communication under strict timelines.
5. Liability is higher
Institutions may be held responsible for lapses in protection, detection, or response.
6. Technology uplift becomes non-negotiable
Legacy systems will struggle to meet AFASA’s requirements — particularly around logging, behavioural analytics, and real-time detection.
How Tookitaki Helps Institutions Align With AFASA
AFASA sets a higher bar for fraud prevention. Tookitaki’s role as the Trust Layer to Fight Financial Crime helps institutions strengthen their AFASA readiness with intelligent, real-time, and collaborative capabilities.
1. Early detection of money mule networks
Through the AFC Ecosystem’s collective intelligence, institutions can detect mule-like patterns sooner and prevent illicit transactions before they spread across the system.
2. Real-time monitoring aligned with AFASA needs
FinCense’s advanced transaction monitoring engine flags suspicious activity instantly — helping institutions support temporary holding procedures and respond within required timelines.
3. Deep behavioural intelligence and comprehensive logs
Tookitaki provides the contextual understanding needed to trace disputed transfers, reconstruct transaction paths, and support investigative workflows.
4. Agentic AI to accelerate investigations
FinMate, the AI investigation copilot, streamlines case analysis, surfaces insights quickly, and reduces investigation workload — especially crucial when time-sensitive AFASA processes are triggered.
5. Federated learning for privacy-preserving model improvement
Institutions can enhance detection models without sharing raw data, aligning with AFASA’s broader emphasis on secure and responsible handling of financial information.
Together, these capabilities enable banks and fintechs to strengthen fraud defences, modernise their operations, and protect financial accounts with confidence.
Looking Ahead: AFASA’s Long-Term Impact
AFASA is not a one-time regulatory update — it is a structural shift in how the Philippine financial ecosystem handles scams.
Expect to see:
- More real-time fraud rules and guidance
- Industry-wide technical standards for dispute management
- Higher expectations for digital onboarding and authentication
- Increased coordination between banks, fintechs, and regulators
- Greater focus on intelligence-sharing and network-level detection
Most importantly, AFASA lays the foundation for a safer, more trusted digital economy — one where consumers have confidence that institutions and regulators can protect them from fast-evolving threats.
Conclusion
AFASA represents a turning point in the Philippines’ fight against financial scams. It transforms how institutions detect fraud, protect accounts, collaborate with others, and support customers. For banks and fintechs, the message is clear: the era of passive fraud response is over.
The institutions that will thrive under AFASA are those that embrace real-time intelligence, strengthen operational resilience, and adopt technology that enables them to stay ahead of criminal innovation.
The Philippines has taken a bold step toward a safer financial system — and now, it’s time for the industry to match that ambition.
Beyond the Smoke: How Illicit Tobacco Became Australia’s New Money-Laundering Engine
In early December 2025, Australian authorities executed one of the most significant financial crime crackdowns of the year — dismantling a sprawling A$150 million money-laundering syndicate operating across New South Wales. What began as an illicit tobacco investigation quickly escalated into a full-scale disruption of an organised network using shell companies, straw directors, and cross-border transfers to wash millions in criminal proceeds.
This case is more than a police success story. It offers a window into Australia’s evolving financial crime landscape — one where illicit trade, complex laundering tactics, and systemic blind spots intersect to form a powerful engine for organised crime.
The Anatomy of an Illicit Tobacco Syndicate
The syndicate uncovered by Australian Federal Police (AFP), NSW Police, AUSTRAC, and the Illicit Tobacco Taskforce was not a small-time criminal operation. It was a coordinated enterprise that combined distribution networks, financial handlers, logistics operators, and front companies into a single ecosystem.
What investigators seized tells a clear story:
- 10 tonnes of illicit tobacco
- 2.1 million cigarettes packaged for distribution
- Over A$300,000 in cash
- A money-counting machine
- Luxury items, including a Rolex
- A firearm and ammunition
These items paint the picture of a network with scale, structure, and significant illicit revenue streams.
Why illicit tobacco?
Australia’s tobacco excise — among the highest globally — has unintentionally created a lucrative black market. Criminal groups can import or manufacture tobacco products cheaply and sell them at prices far below legal products, yet still generate enormous margins.
As a result, illicit tobacco has grown into one of the country's most profitable predicate crimes, fuelling sophisticated laundering operations.
The Laundering Playbook: How A$150M Moved Through the System
Behind the physical contraband lay an even more intricate financial scheme. The syndicate relied on three primary laundering techniques:
a) Straw Directors and Front Companies
The criminals recruited individuals to:
- Set up companies
- Open business bank accounts
- Serve as “directors” in name only
These companies had no legitimate operations — no payroll, no expenses, no suppliers. Their sole function was to provide a façade of legitimacy for high-volume financial flows.
b) Rapid Layering Across Multiple Accounts
Once operational, these accounts saw intense transactional activity:
- Large incoming deposits
- Immediate outbound transfers
- Funds bouncing between newly created companies
- Volumes inconsistent with stated business profiles
This rapid movement made it difficult for financial institutions to track the money trail or link transactions back to illicit tobacco proceeds.
c) Round-Tripping Funds Overseas
To further obscure the origin of funds, the syndicate:
- Sent money to overseas accounts
- Repatriated it disguised as legitimate business payments or “invoice settlements”
To a bank, these flows could appear routine. But in reality, they were engineered to sever any detectable connection to criminal activity.
Why It Worked: Systemic Blind Spots Criminals Exploited
This laundering scheme did not succeed simply because it was complex — it succeeded because it targeted specific weaknesses in Australia’s financial crime ecosystem.
a) High-Profit Illicit Trade
Australia’s tobacco excise structure unintentionally fuels criminal profitability. With margins this high, illicit networks have the financial resources to build sophisticated laundering infrastructures.
b) Fragmented Visibility Across Entities
Most financial institutions only see one customer at a time. They do not automatically connect multiple companies created by the same introducer, or accounts accessed using the same device fingerprints.
This allows straw-director networks to thrive.
c) Legacy Rule-Based Monitoring
Traditional AML systems rely heavily on static thresholds and siloed rules:
- “Large transaction” alerts
- Basic velocity checks
- Limited behavioural analysis
Criminals know this — and structure their laundering techniques to evade these simplistic rules.
d) Cross-Border Complexity
Once funds leave Australia, visibility drops sharply. When they return disguised as payments from overseas vendors, they often blend into the financial system undetected.
Red Flags Financial Institutions Should Watch For
This case provides powerful lessons for compliance teams. Below are the specific indicators FIs should be alert to.
KYC & Profile Red Flags
- Directors with little financial or business experience
- Recently formed companies with generic business descriptions
- Multiple companies tied to the same:
- phone numbers
- IP addresses
- mailing addresses
- No digital footprint or legitimate online presence
Transaction Red Flags
- High turnover in accounts with minimal retained balances
- Rapid movement of funds with no clear business rationale
- Structured cash deposits
- Transfers between unrelated companies with no commercial relationship
- Overseas remittances followed by identical inbound amounts weeks later
Network Behaviour Red Flags
- Shared device IDs used to access multiple company accounts
- Overlapping beneficiaries across supposedly unrelated entities
- Repeated transactions involving known high-risk sectors (e.g., tobacco, logistics, import/export)
These indicators form the behavioural “signature” of a sophisticated laundering ring.
How Tookitaki Strengthens Defences Against These Schemes
The A$150 million case demonstrates why financial institutions need AML systems that move beyond simple rule-based detection.
Tookitaki helps institutions strengthen their defences by focusing on:
a) Typology-Driven Detection
Pre-built scenarios based on real-world criminal behaviours — including straw directors, shell companies, layering, and round-tripping — ensure early detection of organised laundering patterns.
b) Network Relationship Analysis
FinCense connects multiple entities through shared attributes (IP addresses, devices, common directors), surfacing hidden networks that traditional systems miss.
c) Behavioural Analytics
Instead of static thresholds, Tookitaki analyses patterns in account behaviour, highlighting anomalies even when individual transactions seem normal.
d) Collaborative Intelligence via the AFC Ecosystem
Insights from global financial crime experts empower institutions to stay ahead of emerging laundering techniques, including those tied to illicit trade.
e) AI-Powered Investigation Support
FinMate accelerates investigations by providing contextual insights, summarising risks, and identifying links across accounts and entities.
Together, these capabilities help institutions detect sophisticated laundering activity long before it reaches a scale of A$150 million.
Conclusion: Australia’s New Financial Crime Reality
The A$150 million illicit tobacco laundering bust is more than a headline — it’s a signal.
Illicit trade-based laundering is expanding. Criminal networks are becoming more organised. And traditional monitoring systems are no longer enough to keep up.
For banks, fintechs, regulators, and law enforcement, the implications are clear:
- Financial crime in Australia is evolving.
- Laundering networks now mirror corporate structures.
- Advanced AML technology is essential to stay ahead.
As illicit tobacco continues to grow as a predicate offence, the financial system must be prepared for more complex laundering operations — and more aggressive attempts to exploit gaps in institutional defences.
Inside Australia’s $200 Million Psychic Scam: How a Mother–Daughter Syndicate Manipulated Victims and Laundered Millions
1. Introduction of the Scam
In one of Australia’s most astonishing financial crime cases, police arrested a mother and daughter in November 2025 for allegedly running a two hundred million dollar fraud and money laundering syndicate. Their cover was neither a shell company nor a darknet marketplace. They presented themselves as psychics who claimed the ability to foresee danger, heal emotional wounds, and remove spiritual threats that supposedly plagued their clients.
The case captured national attention because it combined two worlds that rarely collide at this scale. Deep emotional manipulation and sophisticated financial laundering. What seemed like harmless spiritual readings turned into a highly profitable criminal enterprise that operated quietly for years.
The scam is a stark reminder that fraud is evolving beyond impersonation calls and fake investment pitches. Criminals are finding new ways to step into the most vulnerable parts of people’s lives. Understanding this case helps financial institutions identify similar behavioural and transactional signals before they escalate into million dollar losses.
2. Anatomy of the Scam
Behind the illusion of psychic counselling was a methodical, multi layered fraud structure designed to extract wealth while maintaining unquestioned authority over victims.
A. Establishing Irresistible Authority
The syndicate created an aura of mystique. They styled themselves as spiritual guides with special insight into personal tragedies, relationship breakdowns, and looming dangers. This emotional framing created an asymmetric relationship. The victims were the ones seeking answers. The scammers were the ones providing them.
B. Cultivating Dependence Over Time
Victims did not transfer large sums immediately. The scammers first built trust through frequent sessions, emotional reinforcement, and manufactured “predictions” that aligned with the victims’ fears or desires. Once trust solidified, dependence followed. Victims began to rely on the scammers’ counsel for major life decisions.
C. Escalating Financial Requests Under Emotional Pressure
As dependence grew, payments escalated. Victims were told that removing a curse or healing an emotional blockage required progressively higher financial sacrifices. Some were convinced that failing to comply would bring harm to themselves or loved ones. Fear became the payment accelerator.
D. Operating as a Structured Syndicate
Although the mother and daughter fronted the scheme, police uncovered several associates who helped receive funds, manage assets, and distance the organisers from the flow of money. This structure mirrored the operational models of organised fraud groups.
E. Exploiting the Legitimacy of “Services”
The payments appeared as consulting or spiritual services, which are common and often unregulated. This gave the syndicate a major advantage. Bank transfers looked legitimate. Transaction descriptions were valid. And the activity closely resembled the profiles of other small service providers.
This blending of emotional exploitation and professional disguise is what made the scam extraordinarily effective.
3. Why Victims Fell for It: The Psychology at Play
People often believe financial crime succeeds because victims are careless. This case shows the opposite. The victims were targeted precisely because they were thoughtful, concerned, and searching for help.
A. Authority and Expertise Bias
When someone is positioned as an expert, whether a doctor, advisor, or psychic, their guidance feels credible. Victims trusted the scammers’ “diagnosis” because it appeared grounded in unique insight.
B. Emotional Vulnerability
Many victims were dealing with grief, loneliness, uncertainty, or family conflict. These emotional states are fertile ground for manipulation. Scammers do not need access to bank accounts when they already have access to the human heart.
C. The Illusion of Personal Connection
Fraudsters used personalised predictions and tailored spiritual advice. This created a bond that felt intimate and unique. When a victim feels “understood,” their defences lower.
D. Fear Based Decision Making
Warnings like “your family is at risk unless you act now” are extremely powerful. Under fear, rationality is overshadowed by urgency.
E. The Sunk Cost Trap
Once a victim has invested a significant amount, they continue paying to “finish the process” rather than admit the entire relationship was fraudulent.
Understanding these psychological drivers is essential. They are increasingly common across romance scams, deepfake impersonations, sham consultant schemes, and spiritual frauds across APAC.
4. The Laundering Playbook Behind the Scam
Once the scammers extracted money, the operation transitioned into a textbook laundering scheme designed to conceal the origin of illicit funds and distance the perpetrators from the victims.
A. Multi Layered Account Structures
Money flowed through personal accounts, associates’ accounts, and small businesses that provided cover for irregular inflows. This layering reduced traceability.
B. Conversion Into High Value Assets
Luxury goods, vehicles, property, and jewellery were used to convert liquid funds into stable, movable wealth. These assets can be held long term or liquidated in smaller increments to avoid detection.
C. Cross Jurisdiction Fund Movement
Authorities suspect that portions of the money were transferred offshore. Cross border movements complicate the investigative trail and exploit discrepancies between regulatory frameworks.
D. Cash Based Structuring
Victims were sometimes encouraged to withdraw cash, buy gold, or convert savings into prepaid instruments. These activities create gaps in the financial record that help obscure illicit origins.
E. Service Based Laundering Through Fake Invoices
The scammers reportedly issued or referenced “healing services,” “spiritual cleansing,” and similar descriptions. Because these services are intangible, verifying their legitimacy is difficult.
The laundering strategy was not unusual. What made it hard to detect was its intimate connection to a long term emotional scam.
5. Red Flags for FIs
Financial institutions can detect the early signals of scams like this through behavioural and transactional monitoring.
Key Transaction Red Flags
- Repeated high value transfers to individuals claiming to provide advisory or spiritual services.
- Elderly or vulnerable customers making sudden, unexplained payments to unfamiliar parties.
- Transfers that increase in value and frequency over weeks or months.
- Sudden depletion of retirement accounts or long held savings.
- Immediate onward transfers from the recipient to offshore banks.
- Significant cash withdrawals following online advisory sessions.
- Purchases of gold, jewellery, or luxury goods inconsistent with customer profiles.
Key Behavioural Red Flags
- Customers showing visible distress or referencing “urgent help” required by an adviser.
- Hesitation or refusal to explain the purpose of a transaction.
- Uncharacteristic secrecy regarding financial decisions.
- Statements referencing curses, spiritual threats, or emotional manipulation.
KYC and Profile Level Red Flags
- Service providers with no registered business presence.
- Mismatch between declared income and transaction activity.
- Shared addresses or accounts among individuals connected to the same adviser.
Financial institutions that identify these early signals can prevent significant losses and support customers before the harm intensifies.
6. How Tookitaki Strengthens Defences
Modern financial crime is increasingly psychological, personalised, and disguised behind legitimate looking service payments. Tookitaki equips institutions with the intelligence and technology to identify these patterns early.
A. Behavioural Analytics Trained on Real World Scenarios
FinCense analyses changes in spending, emotional distress indicators, unusual advisory payments, and deviations from customer norms. These subtle behavioural cues often precede standard red flags.
B. Collective Intelligence Through the AFC Ecosystem
Compliance experts across Asia Pacific contribute emerging fraud scenarios, including social engineering, spiritual scams, and coercion based typologies. Financial institutions benefit from insights grounded in real world criminal activity, not static rules.
C. Dynamic Detection Models for Service Based Laundering
FinCense distinguishes between ordinary professional service payments and laundering masked as consulting or spiritual fees. This is essential for cases where invoice based laundering is the primary disguise.
D. Automated Threshold Optimisation and Simulation
Institutions can simulate how new scam scenarios would trigger alerts and generate thresholds that adapt to the bank’s customer base. This reduces false positives while improving sensitivity.
E. Early Intervention for Vulnerable Customers
FinCense helps identify elderly or high risk individuals who show sudden behavioural changes. Banks can trigger outreach before the customer falls deeper into manipulation.
F. Investigator Support Through FinMate
With FinMate, compliance teams receive contextual insights, pattern explanations, and recommended investigative paths. This accelerates understanding and action on complex scam patterns.
Together, these capabilities form a proactive defence system that protects victims and reinforces institutional trust.
7. Conclusion
The two hundred million dollar psychic scam is more than a headline. It is a lesson in how deeply fraud can infiltrate personal lives and how effectively criminals can disguise illicit flows behind emotional manipulation. It is also a warning that traditional monitoring systems, which rely on transactional patterns alone, may miss the early behavioural signals that reveal the true nature of emerging scams.
For financial institutions, two capabilities are becoming non negotiable.
- Understanding the human psychology behind financial crime.
- Using intelligent, adaptive systems that can detect the behavioural and transactional interplay.
Tookitaki helps institutions meet both challenges. Through FinCense and the AFC Ecosystem, institutions benefit from collective intelligence, adaptive detection, and technology designed to understand the complexity of modern fraud.
As scams continue to evolve, so must defences. Building stronger systems today protects customers, prevents loss, and strengthens trust across the financial ecosystem.