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Why Do We Need New Customer AML Risk Rating Models?

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Tookitaki
14 September 2020
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5 min

Estimated at between US$800 billion to US$2 trillion every year, money laundering is a serious problem for the global economy. While regulators and financial institutions are working hard to prevent and reduce the crime, launderers are becoming increasingly sophisticated and are introducing techniques that are harder to decode. Changing customer behaviour and the introduction of numerous digital transaction methods add to the AML compliance worry of banks and impact their customer risk rating models.

The COVID-19 pandemic has also been playing its role, as criminals adapt their strategies to the unforeseen situation. We have previously written about the rising number of cybercrimes and fraud schemes across the globe, where criminals take advantage of the people’s fear, helplessness, the need for immediate financial assistance and medical supplies among others.

AML compliance failures are seemingly on the rise as AML fines in the first six months of 2020 reached US$706 million, up from US$444 million in the entire 2019, according to research. It was also revealed that customer due diligence (CDD), AML management, suspicious activity monitoring and compliance monitoring and oversight are the areas where firms are going wrong repeatedly.

What is needed is a new AML risk rating approach, powered by modern technologies such as AI and machine learning. Tookitaki has developed various solutions in relation to customer due diligence, transaction monitoring and screening. Here, we will focus our innovation in the area of customer risk scoring which is one of the primary tools for Know Your Customer (KYC), CDD and enhanced due diligence (EDD) and continuous monitoring of customers.

The Importance of Customer Risk Scoring

Before onboarding customers, financial institutions are mandated to assess AML risk related to them based on a number of factors such as occupation, income sources, and the banking products used. They conduct customer due diligence and monitor the risk ratings throughout a customer’s lifecycle to make informed decisions on potential money laundering cases.

Banks usually do an identity verification and risk assessment for their individual and corporate customers by collecting various details about them. The process is to ensure that they are not doing business with people or institutions involved in financial crimes such as money laundering and terrorist financing. Banks collect as much data as they can about their customers, analyse the data they obtained, determine their risk and provide a risk rating.

Customers with a high risk rating are closely monitored for their actions. Low-risk customers are also monitored but not as diligently as high-risk customers. Even after onboarding a customer, banks periodically update their database about customers. Typically, they do data updates for high-risk customers more frequently than low-risk customers.

Pitfalls of The Current Customer Risk Rating Matrix

Many of the current customer risk rating models are not robust to capture the complexities of modern-day customer risk management. Customer risk ratings are either carried out manually or are based on matrices that use a limited set of pre-defined risk parameters. This leads to inadequate coverage of risk factors which vary in number and weightage from customer to customer.

Furthermore, the information for most of these risk parameters is static and collected when an account is opened. Often, information about customers is not updated in the required format and frequency. The current models do not consider all the touchpoints of a customer’s activity map and inaccurately score customers, failing to detect some high-risk customers and often misclassifying thousands of low-risk customers as high risk.

Misclassification of customer risk leads to unnecessary case reviews, resulting in high costs and customer dissatisfaction. Adding to this, the static nature of the risk parameters fails to capture the changing behaviour of customers and dynamically adjust the risk ratings, exposing financial institutions to emerging threats.

 

The AI Way of Creating an AML Risk Assessment Matrix

Today, modern technologies like AI and machine learning are getting widespread attention for their ability to improve business processes and regulators are encouraging banks to adopt innovative approaches to combat money laundering. In the area of customer risk scoring, their  is a need for more sophisticated technology that can capture the complete customer activity through proper identification of risk indicators and continuously update customer profiles as underlying activities change.

Keeping that in mind, we have developed a Customer Risk Scoring (CRS) solution as one of the modules of our award-winning Anti-Money Laundering Suite (AMLS). Powered by advanced machine learning, the module addresses the market needs and provides an effective and scalable customer risk rating solution by dynamically identifying relevant risk indicators across a customer’s activity map and scoring customers into three risk tiers – High, Moderate and Low.

The solution adapts to changing customer behaviour to build a 360-degree risk profile thereby providing a risk-based approach to client management. It comes with a powerful analytics layer that includes actionable insights and easy explanations for business users to make faster and more informed decisions.

The key benefits of our CRS solution are:

Broader risk coverage:  CRS assesses risk across a comprehensive range of risk indicators that provides a 360-degree view of AML Risk relative to the customer, their relationships and activities. These dimensions are Customer, Counterparty, Transactions and Network Relationships.

Dynamic customer assessment: The solution provides continuous, on demand and accurate customer risk scoring. Customer AML risk assessment adapts over time to actual customer behaviour. This vastly reduces false signals and improves inappropriate behaviour detection. In short, it acts as a perpetual KYC platform for ongoing due diligence.

Solution level agility: The solution is not a single “model”.  CRS has been developed with advanced ongoing self-learning to evolve based on what is happening within specific client portfolios, business policies and industry trends. This functionality is controlled by client configuration to support all model governance policy and regulation requirements.

Accelerated risk assessment: CRS filters and presents the most critical information needed for investigators to make effective risk-based decisions timely and consistently. The solution simplifies highly complex machine learning decisions into understandable and actionable information.

Reduced time-to-value and clear migration path to ML-based workflows: CRS does not require time and cost consuming change of existing Customer Risk Policies and Controls. Initially complementing your legacy operating environment, CRS provides the functionality required to transition to full machine learning-based AML Customer Risk as and when it is appropriate.

Reduced cost of compliance and reputational risk: The solution helps identify high-risk customers and enable banks to take proactive measures to mitigate the risk of financial loss due to penalties along with various other regulatory, legal and reputational risks.

Money laundering across the globe has increased not just in volume, but also in terms of complexity and sophistication. Customer behaviour has significantly changed with digital banking, transferring funds across geographies has become very easy and even instant in some cases. Such transformations make financial institutions more vigilant. They need to continuously evaluate their customers’ risk score based on their behaviour and monitor based on their updated risks at all times.

As regulators are becoming more stringent globally around AML compliance, strengthening the AML systems continues to remain among top priorities. Our CRS solution enables financial institutions to realise benefits with dynamic customer risk scoring, leveraging advanced machine learning models for improved effectiveness of Enhanced Due Diligence with fewer resources.

To learn more about our AML solution and its unique features, contact us and we will be happy to give you a detailed demo.

 

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12 Dec 2025
7 min
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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.

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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.

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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.

AFASA Explained: What the Philippines’ New Anti-Scam Law Really Means for Banks, Fintechs, and Consumers
Blogs
10 Dec 2025
6 min
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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.

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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.

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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.

Beyond the Smoke: How Illicit Tobacco Became Australia’s New Money-Laundering Engine
Blogs
02 Dec 2025
6 min
read

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.

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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

  1. Repeated high value transfers to individuals claiming to provide advisory or spiritual services.
  2. Elderly or vulnerable customers making sudden, unexplained payments to unfamiliar parties.
  3. Transfers that increase in value and frequency over weeks or months.
  4. Sudden depletion of retirement accounts or long held savings.
  5. Immediate onward transfers from the recipient to offshore banks.
  6. Significant cash withdrawals following online advisory sessions.
  7. Purchases of gold, jewellery, or luxury goods inconsistent with customer profiles.

Key Behavioural Red Flags

  1. Customers showing visible distress or referencing “urgent help” required by an adviser.
  2. Hesitation or refusal to explain the purpose of a transaction.
  3. Uncharacteristic secrecy regarding financial decisions.
  4. Statements referencing curses, spiritual threats, or emotional manipulation.

KYC and Profile Level Red Flags

  1. Service providers with no registered business presence.
  2. Mismatch between declared income and transaction activity.
  3. 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.

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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.

  1. Understanding the human psychology behind financial crime.
  2. 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.

Inside Australia’s $200 Million Psychic Scam: How a Mother–Daughter Syndicate Manipulated Victims and Laundered Millions