Blog

Future-Proofing AML: Insights from Singapore's Risk Assessment

Site Logo
Tookitaki
24 July 2024
read
6 min

Anti-money laundering (AML) strategies are crucial for the financial sector. They help prevent and detect illegal activities, protecting the integrity of financial systems. The 2024 Singapore National Money Laundering Risk Assessment Report highlights the evolving threats and the need for advanced solutions.

The report states, "Singapore is exposed to the risks of transnational Money Laundering (ML), Terrorism Financing (TF), and Proliferation Financing (PF)." These risks require robust and adaptive AML strategies. Advanced AI can play a key role in future-proofing these strategies, providing real-time monitoring and improved accuracy.

The Evolving Nature of Money Laundering Threats

Singapore's risk assessment identifies several key ML threats. These include cyber-enabled fraud, organised crime, corruption, tax crimes, and trade-based money laundering (TBML). Each of these threats is evolving with technology and global changes.

  • Cyber-Enabled Fraud: Cyber-enabled fraud is a significant threat. It involves using the internet to commit fraud and launder money. The report notes, "Singapore has also observed an increase in ML threat posed by cyber-enabled fraud committed domestically, orchestrated by syndicates typically located overseas."
  • Organised Crime: Organised crime, such as illegal online gambling, poses high risks. Criminals use complex methods to launder large sums of money. The report highlights a recent case involving over S$3 billion worth of seized and prohibited assets linked to foreign organized crime groups.
  • Corruption: Corruption remains a major threat. Criminals use sophisticated methods to hide and move illegal funds. The report states, "The threat of corruption proceeds being laundered through our region is assessed to be high, given Singapore’s geographical location and status as an international business, financial and trading centre."
  • Tax Crimes: Tax crimes are also on the rise. Singapore's status as a wealth management hub attracts criminals looking to launder tax crime proceeds. The report observes, "Singapore has seen an increase in the number of incoming foreign requests relating to tax offences."
  • Trade-Based Money Laundering (TBML): TBML is another growing threat. Criminals use trade transactions to hide and move illegal money. The report mentions, "Singapore faces an inherent threat of foreign TBML given its status as a trading and transportation hub."

These threats are evolving with technological advancements and geopolitical changes. Criminals are using more sophisticated techniques and digital platforms to launder money. This makes it essential to have adaptive and robust AML strategies.

{{cta-guide}}

Sectoral Risk Assessments

Financial Sector

The financial sector, particularly banks and wealth management services, poses the highest ML risks. This is due to their extensive networks and high transaction volumes. The report states, "The banking sector has been assessed to pose the highest ML risk to Singapore. The role of banks in facilitating transactions in the financial system, and their wide networks through which cross-border transactions can be conducted, make banks a common channel which criminals exploit."

Additionally, payment institutions and digital payment token service providers face significant risks due to the nature of their operations, which involve handling large volumes of transactions and providing services that can be misused for money laundering.

Designated Non-Financial Businesses and Professions (DNFBP)

The DNFBP sector also faces substantial ML risks. Corporate Service Providers (CSPs) are particularly at high risk because of their role in company incorporation, which can be exploited by criminals to set up shell companies for money laundering purposes. The report highlights that "CSPs pose higher ML risks given the role they play in providing upstream services such as incorporation of companies."

Other high-risk sectors in the DNFBP category include real estate, casinos, and precious stones and metals dealers. These sectors are vulnerable due to their involvement in high-value transactions, which can be attractive for money launderers seeking to integrate illicit funds into the legitimate economy.

Guidance to Financial Institutions to Prevent Money Laundering

The 2024 Singapore National Money Laundering Risk Assessment Report provides detailed guidance to financial institutions (FIs) on enhancing AML efforts by adopting a risk-based approach tailored to specific risks. This includes conducting thorough risk assessments, implementing robust controls, and integrating NRA findings into internal risk assessments for better risk mitigation.

The report emphasises the need for continuous improvement in AML strategies. Financial institutions should utilise AI to enhance monitoring, detect suspicious activities, and reduce false positives. Inter-agency cooperation is also crucial for staying updated on emerging threats and best practices in AML. By following these guidelines, FIs can build more effective AML frameworks.

Challenges in Traditional AML Strategies

Traditional AML methods face several limitations. These methods depend heavily on manual processes, which are slow and less effective. This over-reliance makes it hard to keep up with fast-evolving money laundering techniques.

Over-reliance on Manual Processes

Manual processes involve significant human intervention. This can lead to delays and errors. It also makes it difficult to process large volumes of transactions quickly.

High Rates of False Positives

One major problem with traditional AML methods is the high rate of false positives. Many alerts are triggered by legitimate transactions, which wastes time and resources. This makes it harder to identify real threats.

Slow Response to Emerging Threats

Traditional AML methods are often slow to respond to new threats. Criminals are always finding new ways to launder money. Manual systems can't adapt quickly enough to these changes.

The Need for More Dynamic and Responsive AML Strategies

Given these limitations, there is a clear need for more dynamic and responsive AML strategies. These strategies should be able to analyse large amounts of data quickly and accurately. This is where advanced AI can make a significant difference.

The Role of Advanced AI in AML

Advanced AI offers powerful tools for AML. It can handle real-time monitoring and analysis of transactions. AI can quickly process large volumes of data, making it ideal for modern AML needs.

  • Real-Time Monitoring and Analysis: AI enables real-time monitoring and analysis of transactions. It can process millions of transactions per second. This helps financial institutions detect suspicious activities as they happen.
  • Improved Accuracy in Detecting Suspicious Activities: AI improves the accuracy of detecting suspicious transactions. It learns from past data to identify patterns of illegal activities. This helps reduce the number of false positives and focuses on real threats.
  • Reduction in False Positives: One of the biggest benefits of AI in AML is the reduction in false positives. AI systems can differentiate between legitimate and suspicious transactions more effectively. This saves time and resources, allowing compliance teams to focus on genuine threats.

Tookitaki’s AI-Driven AML Solutions

Tookitaki's FinCense is the most intelligent financial crime prevention platform available. This distinction is driven by our innovative use of collective intelligence and a federated approach. Our Anti-Financial Crime (AFC) Ecosystem leverages an expert network that continuously updates and shares knowledge, acting as a force multiplier. This collaborative model significantly outperforms the siloed approaches used by our competitors, ensuring our clients benefit from the most comprehensive and up-to-date financial crime prevention strategies.

Tookitaki utilises a multi-layered AI approach in the FinCense suite and AFC ecosystem for robust and adaptive financial crime prevention. Leveraging insights from the AFC ecosystem, AI models in FinCense analyse transactions in real time for fraud prevention and AML transaction monitoring. AI also enhances name screening and customer risk scoring, while reducing false alerts.

The AFC ecosystem shares typologies of financial crimes through AI-enhanced analysis, while adaptive learning continuously updates crime prevention strategies. Tookitaki's Data Science Studio supports multiple ML models and includes an explainability framework for transparent AI-driven decisions, ensuring comprehensive financial crime prevention and operational efficiency.

{{cta-ebook}}

How to Fight Emerging ML Threats

Tookitaki’s AI-driven solutions are designed to adapt to the ever-changing landscape of money laundering threats. One of the key features is the continuous learning and updating of AML models. The AI models within Tookitaki's system learn from new data and experiences, allowing them to stay ahead of emerging threats. This adaptive learning process ensures that the AML strategies remain effective even as criminals develop new techniques.

Another significant advantage is the proactive identification of new crime patterns. Tookitaki’s AI leverages insights from the Anti-Financial Crime (AFC) ecosystem, which is a collaborative network of experts sharing knowledge on financial crime typologies. This collective intelligence enables the AI to identify and respond to new patterns of suspicious activity swiftly. By staying informed about the latest methodologies used by criminals, Tookitaki ensures that financial institutions are always equipped with the most current and effective tools to combat money laundering.

Scalability is also a crucial aspect of Tookitaki’s AI-driven solutions. The platform is built to handle increasing transaction volumes and the complexities of modern financial operations. As financial institutions grow and process more transactions, Tookitaki’s AI can scale seamlessly to meet these demands. This scalability is essential for maintaining robust AML defences in an environment where transaction volumes can grow rapidly and unpredictably.

Final Thoughts

Future-proofing AML strategies with advanced AI is crucial. AI-driven solutions offer real-time monitoring, improved accuracy, and scalability to handle increasing transaction volumes. Tookitaki's innovative approach, leveraging collective intelligence and a federated learning model, ensures financial institutions are equipped with the most current and effective tools to combat financial crime.

Financial institutions must explore Tookitaki’s AI-driven solutions to enhance their AML compliance. By adopting these advanced technologies, institutions can stay ahead of criminals, reduce operational inefficiencies, and ensure a safer financial environment. Embrace the future of AML with Tookitaki and build a robust defence against financial crime.

By submitting the form, you agree that your personal data will be processed to provide the requested content (and for the purposes you agreed to above) in accordance with the Privacy Notice

success icon

We’ve received your details and our team will be in touch shortly.

In the meantime, explore how Tookitaki is transforming financial crime prevention.
Learn More About Us
Oops! Something went wrong while submitting the form.

Ready to Streamline Your Anti-Financial Crime Compliance?

Our Thought Leadership Guides

Blogs
20 Aug 2025
6 min
read

Ferraris, Ghost Cars, and Dirty Money: Inside Australia’s 2025 Barangaroo Laundering Scandal

In July 2025, Sydney’s Barangaroo precinct became the unlikely stage for one of Australia’s most audacious money laundering cases. Beyond the headlines about Ferraris and luxury goods lies a sobering truth: criminals are still exploiting the blind spots in Australia’s financial crime defences.

A Case That Reads Like a Movie Script

On 30 July 2025, Australian police raided properties across Sydney and arrested two men—Bing “Michael” Li, 38, and Yizhe “Tony” He, 34.

Both men were charged with an astonishing 194 fraud-related offences. Li faces 87 charges tied to AUD 12.9 million, while He faces 107 charges tied to about AUD 4 million. Authorities also froze AUD 38 million worth of assets, including Bentleys, Ferraris, designer goods, and property leases.

At the heart of the case was a fraud and laundering scheme that funnelled stolen money into the high-end economy of cars, luxury fashion, and short-term property leases. Investigators dubbed them “ghost cars”—vehicles purchased as a way to obscure illicit funds.

It’s a tale that grabs attention for its glitz, but what really matters is the deeper lesson: Australia still has critical AML blind spots that criminals know how to exploit.

Talk to an Expert

How the Syndicate Operated

The mechanics of the scheme reveal just how calculated it was:

  • Rapid loan cycling: The accused are alleged to have obtained loans, often short-term, which were cycled quickly to create complex repayment patterns. This made tracing the origins of funds difficult.
  • Luxury asset laundering: The money was used to purchase high-value cars (Ferraris, Bentleys, Mercedes) and designer items from brands like Louis Vuitton. Assets of prestige become a laundering tool, integrating dirty money into seemingly legitimate wealth.
  • Property as camouflage: Short-term leases of expensive properties in Barangaroo and other high-end districts provided both a lifestyle cover and another channel to absorb illicit funds.
  • Gatekeeper loopholes: Real estate agents, accountants, and luxury dealers in Australia are not yet fully bound by AML/CTF obligations. This gap created the perfect playground for laundering.

What’s striking is not the creativity of the scheme—it’s the simplicity. By targeting sectors without AML scrutiny, the syndicate turned everyday transactions into a pipeline for cleaning millions.

The Regulatory Gap

This case lands at a critical time. For years, Australia has been under pressure from the Financial Action Task Force (FATF) to extend AML/CTF laws to the so-called “gatekeeper professions”—real estate agents, accountants, lawyers, and dealers in high-value goods.

As of 2025, these obligations are still not fully in place. The expansion is only scheduled to take effect from July 2026. Until then, large swathes of the economy remain outside AUSTRAC’s oversight.

The Barangaroo arrests underscore what critics have long warned: criminals don’t wait for legislation. They are already steps ahead, embedding illicit funds into sectors that regulators have yet to fence off.

For businesses in real estate, luxury retail, and professional services, this case is more than a headline—it’s a wake-up call to prepare now, not later.

ChatGPT Image Aug 19, 2025, 01_54_51 PM

Why This Case Matters for Australia

The Barangaroo case isn’t just about two individuals—it highlights systemic vulnerabilities in the Australian financial ecosystem.

  1. Criminal Adaptation: Syndicates will always pivot to the weakest link. If banks tighten their checks, criminals move to less regulated industries.
  2. Erosion of Trust: When high-value markets become conduits for laundering, it damages Australia’s reputation as a clean, well-regulated financial hub.
  3. Compliance Risk: Businesses in these sectors risk being blindsided by new regulations if they don’t start implementing AML controls now.
  4. Global Implications: With assets like luxury cars and crypto being easy to move or sell internationally, local failures in AML quickly ripple across borders.

This isn’t an isolated story. It’s part of a broader trend where fraud, luxury assets, and regulatory lag intersect to create fertile ground for financial crime.

Lessons for Businesses

For financial institutions, fintechs, and gatekeeper industries, the Barangaroo case offers several practical takeaways:

  • Monitor for rapid loan cycling: Short-term loans repaid unusually fast, or loans tied to sudden high-value purchases, should trigger alerts.
  • Scrutinise asset purchases: Repeated luxury acquisitions, especially where the source of funds is vague, are classic laundering red flags.
  • Don’t rely solely on regulation: Just because AML obligations aren’t mandatory yet doesn’t mean businesses can ignore risk. Voluntary adoption of AML best practices can prevent reputational damage.
  • Collaborate cross-sector: Banks, real estate firms, and luxury dealers must share intelligence. Laundering rarely stays within one sector.
  • Prepare for 2026: When the law expands, regulators will expect not just compliance but also readiness. Being proactive now can avoid penalties later.

How Tookitaki’s FinCense Can Help

The Barangaroo case demonstrates a truth that regulators and compliance teams already know: criminals are fast, and rules often move too slowly.

This is where FinCense, Tookitaki’s AI-powered compliance platform, makes the difference.

  • Scenario-based Monitoring
    FinCense doesn’t just look for generic suspicious behaviour—it monitors for specific typologies like “rapid loan cycling leading to high-value asset purchases.” These scenarios mirror real-world cases, allowing institutions to spot laundering patterns early.
  • Federated Intelligence
    FinCense leverages insights from a global compliance community. A laundering method detected in one country can be quickly shared and simulated in others. If the Barangaroo pattern emerged elsewhere, FinCense could help Australian institutions adapt almost immediately.
  • Agentic AI for Real-Time Detection
    Criminal tactics evolve constantly. FinCense’s Agentic AI ensures models don’t go stale—it adapts to new data, learns continuously, and responds to threats as they arise. That means institutions don’t wait months for rule updates; they act in real time.
  • End-to-End Compliance Coverage
    From customer onboarding to transaction monitoring and investigation, FinCense provides a unified platform. For banks, this means capturing anomalies at multiple points, not just after funds have already flowed into cars and luxury handbags.

The result is a system that doesn’t just tick compliance boxes but actively prevents fraud and laundering—protecting both businesses and Australia’s reputation.

The Bigger Picture: Trust and Reputation

Australia has ambitions to strengthen its role as a regional financial hub. But trust is the currency that underpins global finance.

Cases like Barangaroo remind us that even one high-profile lapse can shake investor and customer confidence. With scams and laundering scandals making headlines globally—from Crown Resorts to major online frauds—Australia cannot afford to be reactive.

For businesses, the message is clear: compliance isn’t just about avoiding fines, it’s about protecting your licence to operate. Customers and partners expect vigilance, transparency, and accountability.

Conclusion: A Warning Shot

The Barangaroo “ghost cars and luxury laundering” saga is more than a crime story—it’s a preview of what happens when regulation lags and businesses underestimate financial crime risk.

With AUSTRAC set to extend AML coverage in 2026, industries like real estate and luxury retail must act now. Waiting until the law forces compliance could mean walking straight into reputational disaster.

For financial institutions and businesses alike, the smarter path is to embrace advanced solutions like Tookitaki’s FinCense, which combine scenario-driven intelligence with adaptive AI.

Because at the end of the day, Ferraris and Bentleys may be glamorous—but when they’re bought with dirty money, they carry a far higher cost.

Ferraris, Ghost Cars, and Dirty Money: Inside Australia’s 2025 Barangaroo Laundering Scandal
Blogs
30 Jul 2025
5 min
read

Cracking Down Under: How Australia Is Fighting Back Against Fraud

Fraud in Australia has moved beyond stolen credit cards, today’s threats are smarter, faster, and often one step ahead.

Australia is facing a new wave of financial fraud—complex scams, cyber-enabled deception, and social engineering techniques that prey on trust. From sophisticated investment frauds to deepfake impersonations, criminals are evolving rapidly. And so must our fraud prevention strategies.

This blog explores how fraud is impacting Australia, what new methods criminals are using, and how financial institutions, businesses, and individuals can stay ahead of the game. Whether you're in compliance, fintech, banking, or just a concerned citizen, fraud prevention is everyone’s business.

The Fraud Landscape in Australia: A Wake-Up Call

In 2024 alone, Australians lost over AUD 2.7 billion to scams, according to data from the Australian Competition and Consumer Commission (ACCC). The Scamwatch program reported an alarming rise in phishing, investment scams, identity theft, and fake billing.

A few alarming trends:

  • Investment scams accounted for over AUD 1.3 billion in losses.
  • Business email compromise (BEC) and invoice fraud targeted SMEs.
  • Romance and remote access scams exploited personal vulnerability.
  • Deepfake scams and AI-generated impersonations are on the rise, particularly targeting executives and finance teams.

The fraud threat has gone digital, cross-border, and real-time. Traditional controls alone are no longer enough.

Talk to an Expert

Why Fraud Prevention Is a National Priority

Fraud isn't just a financial issue—it’s a matter of public trust. When scams go undetected, victims don’t just lose money—they lose faith in financial institutions, government systems, and digital innovation.

Here’s why fraud prevention is now top of mind in Australia:

  • Real-time payments mean real-time risks: With the rise of the New Payments Platform (NPP), funds can move across banks instantly. This has increased the urgency to detect and prevent fraud in milliseconds—not days.
  • Rise in money mule networks: Criminal groups are exploiting students, gig workers, and the elderly to launder stolen funds.
  • Increased regulatory pressure: AUSTRAC and ASIC are putting more pressure on institutions to identify and report suspicious activities more proactively.

Common Fraud Techniques Seen in Australia

Understanding how fraud works is the first step to preventing it. Here are some of the most commonly observed fraud techniques:

a) Business Email Compromise (BEC)

Fraudsters impersonate vendors, CEOs, or finance officers to divert funds through fake invoices or urgent payment requests. This is especially dangerous for SMEs.

b) Investment Scams

Fake trading platforms, crypto Ponzi schemes, and fraudulent real estate investments have tricked thousands. Often, these scams use fake celebrity endorsements or “guaranteed returns” to lure victims.

c) Romance and Sextortion Scams

These scams manipulate victims emotionally, often over weeks or months, before asking for money. Some even involve blackmail using fake or stolen intimate content.

d) Deepfake Impersonation

Using AI-generated voice or video, scammers are impersonating real people to initiate fund transfers or manipulate staff into giving away sensitive information.

e) Synthetic Identity Fraud

Criminals use a blend of real and fake information to create a new, ‘clean’ identity that can bypass onboarding checks at banks and fintechs.

20250730_2107_Cybersecurity Precaution Scene_remix_01k1dzk8hwfd4t9rd8mkhzgr1w

Regulatory Push for Smarter Controls

Regulators in Australia are stepping up their efforts:

  • AUSTRAC has introduced updated guidance for transaction monitoring and suspicious matter reporting, pushing institutions to adopt more adaptive, risk-based approaches.
  • ASIC is cracking down on investment scams and calling for platforms to implement stricter identity and payment verification systems.
  • The ACCC’s National Anti-Scam Centre launched a multi-agency initiative to disrupt scam operations through intelligence sharing and faster response times.

But even regulators acknowledge: compliance alone won't stop fraud. Prevention needs smarter tools, better collaboration, and real-time intelligence.

A New Approach: Proactive, AI-Powered Fraud Prevention

The most forward-thinking banks and fintechs in Australia are moving from reactive to proactive fraud prevention. Here's what the shift looks like:

✅ Real-Time Transaction Monitoring

Instead of relying on static rules, modern systems use machine learning to flag suspicious behaviour—like unusual payment patterns, high-risk geographies, or rapid account-to-account transfers.

✅ Behavioural Analytics

Understanding what ‘normal’ looks like for each user helps detect anomalies fast—like a customer suddenly logging in from a new country or making a large transfer outside business hours.

✅ AI Copilots for Investigators

Tools like AI-powered investigation assistants can help analysts triage alerts faster, recommend next steps, and even generate narrative summaries for suspicious activity reports.

✅ Community Intelligence

Fraudsters often reuse tactics across institutions. Platforms like Tookitaki’s AFC Ecosystem allow banks to share anonymised fraud scenarios and red flags—so everyone can learn and defend together.

✅ Federated Learning Models

These models allow banks to collaborate on fraud detection algorithms without sharing customer data—bringing the power of collective intelligence without compromising privacy.

Fraud Prevention Best Practices for Australian Institutions

Whether you're a Tier-1 bank or a growing fintech, these best practices are critical:

  1. Prioritise real-time fraud detection tools that work across payment channels and digital platforms.
  2. Train your teams—fraudsters are exploiting human error more than technical flaws.
  3. Invest in explainable AI to build trust with regulators and internal stakeholders.
  4. Use layered defences: Combine transaction monitoring, device fingerprinting, behavioural analytics, and biometric verification.
  5. Collaborate across the ecosystem—join industry platforms, share intel, and learn from others.

How Tookitaki Supports Fraud Prevention in Australia

Tookitaki is helping Australian institutions stay ahead of fraud by combining advanced AI with collective intelligence. Our FinCense platform offers:

  • End-to-end fraud and AML detection across transactions, customers, and devices.
  • Federated learning that enables risk detection with insights contributed by a global network of financial crime experts.
  • Smart investigation tools to reduce alert fatigue and speed up response times.

The Role of Public Awareness in Prevention

It’s not just institutions—customers play a key role too. Public campaigns like Scamwatch, educational content from banks, and media coverage of fraud trends all contribute to prevention.

Simple actions like verifying sender details, avoiding suspicious links, and reporting scam attempts can go a long way. In the fight against fraud, awareness is the first line of defence.

Conclusion: Staying Ahead in a Smarter Fraud Era

Fraud prevention in Australia can no longer be treated as an afterthought. The threats are too advanced, too fast, and too costly.

With the right mix of technology, collaboration, and education, Australia can stay ahead of financial criminals—and turn the tide in favour of consumers, businesses, and institutions alike.

Whether it’s adopting AI tools, sharing threat insights, or empowering individuals, fraud prevention is no longer optional. It’s the new frontline of trust.

Cracking Down Under: How Australia Is Fighting Back Against Fraud
Blogs
29 Jul 2025
6 min
read

The CEO Wasn’t Real: Inside Singapore’s $499K Deepfake Video Scam

In March 2025, a finance director at a multinational firm in Singapore authorised a US$499,000 payment during what appeared to be a Zoom call with the company’s senior leadership. There was just one problem: none of the people on the call were real.

What seemed like a routine virtual meeting turned out to be a highly orchestrated deepfake scam, where cybercriminals used artificial intelligence to impersonate the company’s Chief Financial Officer and other top executives. The finance director, believing the request was genuine, wired nearly half a million dollars to a fraudulent account.

The incident has sent shockwaves across the financial and corporate world, underscoring the fast-evolving threat of deepfake technology.

Background of the Scam

According to Singapore police reports, the finance executive received a message from someone posing as the company’s UK-based CFO. The message requested an urgent fund transfer to facilitate a confidential acquisition. To build credibility, the fraudster set up a Zoom call — featuring multiple senior executives, all appearing and sounding authentic.

But the entire video call was fabricated using deepfake technology.

These weren’t just stolen profile photos; they were AI-generated likenesses with synced facial movements and realistic voices, mimicking actual executives. The finance director, seeing what seemed like familiar faces and hearing familiar voices, followed through with the transfer.

Only later did the company realise that the actual executives had never been on the call.

What the Case Revealed

This wasn’t just another phishing email or spoofed WhatsApp message. This was next-level digital deception. Here’s what made it chillingly effective:

  • Multi-party deepfake execution – The fraud involved several synthetic identities, all rendered convincingly in real-time to simulate a legitimate boardroom environment.
  • High-level impersonation – Senior figures like the CFO were cloned with accurate visual and vocal characteristics, heightening the illusion of authority and urgency.
  • Deeply contextual manipulation – The scam leveraged business context (e.g. M&A activity, board-level communications) that suggested insider knowledge.

Singapore’s police reported this as one of the most convincing cases of AI-powered impersonation seen to date — and issued a national warning to corporations and finance professionals.

Impact on Financial Institutions and Corporates

While the fraud targeted one company, its implications ripple across the entire financial system:

Deepfake Fatigue and Trust Erosion

When even video calls are no longer trustworthy, confidence in digital communication takes a hit. This undermines both internal decision-making and external client relationships.

CFOs and Finance Teams in the Crosshairs

Finance and treasury teams are prime targets for scams like this. These professionals are expected to act fast, handle large sums, and follow instructions from the top — making them vulnerable to high-pressure frauds.

Breakdown of Traditional Verification

Emails, video calls, and even voice confirmations can be falsified. Without secondary verification protocols, companies remain dangerously exposed.

ChatGPT Image Jul 29, 2025, 02_34_13 PM

Lessons Learned from the Scam

The Singapore deepfake case isn’t an outlier — it’s a glimpse into the future of financial crime. Key takeaways:

  1. Always Verify High-Value Requests
    Especially those involving new accounts or cross-border transfers. A secondary channel of verification — via phone or an encrypted app — is now a must.
  2. Educate Senior Leadership
    Executives need to be aware that their digital identities can be hijacked. Regular briefings on impersonation risks are essential.
  3. Adopt Real-Time Behavioural Monitoring
    Advanced analytics can flag abnormal transaction patterns — even when the request appears “approved” by an authority figure.
  4. Invest in Deepfake Detection Tools
    There are now software solutions that scan video content for artefacts, inconsistencies, or signs of AI manipulation.
  5. Strengthen Internal Protocols
    Critical payment workflows should always require multi-party authorisation, escalation logic, and documented rationale.

The Role of Technology in Prevention

Scams like this are designed to outsmart conventional defences. A new kind of defence is required — one that adapts in real-time and learns from emerging threats.

This is where Tookitaki’s compliance platform, FinCense, plays a vital role.

Powered by the AFC Ecosystem and Agentic AI:

  • Typology-Driven Detection: FinCense continuously updates its detection logic based on real-world scam scenarios contributed by financial crime experts worldwide.
  • AI-Powered Simulation: Institutions can simulate deepfake-driven fraud scenarios to test and refine their internal controls.
  • Federated Learning: Risk signals and red flags from across institutions are shared securely without compromising sensitive data.
  • Smart Case Disposition: Agentic AI reviews and narrates alerts, allowing compliance officers to respond faster and with greater clarity — even in complex scams like this.
Talk to an Expert

Moving Forward: Facing the Synthetic Threat Landscape

Deepfake technology has moved from the realm of novelty to real-world risk. The Singapore incident is a wake-up call for companies across ASEAN and beyond.

When identity can be faked in real-time, and fraudsters learn faster than regulators, the only defence is to stay ahead — with intelligence, collaboration, and next-generation tech.

Because next time, the CEO might not be real, but the money lost will be.

The CEO Wasn’t Real: Inside Singapore’s $499K Deepfake Video Scam