Roundtable Rewind: Embracing Next-gen Tech & Collaboration
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As a regtech player with a vision to enable sustainable compliance programs in financial institutions, Tookitaki organized a first-of-its-kind industry roundtable in Charlotte, NC, USA to discuss opportunities to innovate current AML programs and meet regulatory expectations. The roundtable was an open forum for various stakeholder groups to ideate on how to assimilate new-edge technologies like artificial intelligence (AI) and machine learning (ML) within their system of internal controls and across the industry to improve the detection and investigation of illicit financial flows.
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It saw participation from global financial institutions, risk and compliance professionals, consulting firms and technology services firms. Long time AML industry veteran and associate director at Protiviti, George Harris, moderated the discussion topics. The roundtable addressed the following questions:
• How can we create a sustainable AML program design and address challenges associated with today’s compliance processes and systems?
• What role can modern technology play in bringing together banks’ money laundering wisdom?
• How can we deploy an AI model in production with scale and mitigate compliance risk?
Sustainability and modern tech
Our participants unanimously agreed that the general skepticism surrounding the adoption of AI and ML to address regulatory compliance issues has started fading away as such technologies meet applicable security guidelines, improves overall operational performance, and improves customer experience. However, the industry experts suggested specific key points, while adopting modern technologies to create sustainable compliance programs.
1. Behavior monitoring rather than transactional monitoring is the need of the hour.
As threshold setting may not be always effective to identify suspicious transactions, it’s imperative on today’s compliance programs to follow a behavior monitoring approach. They need to understand what normal behavior is to find out whether specific cases standout from that normal behavior. If we can identify the normal behavior, then we can weed out those non-productive cases much faster and in a sustainable way. To understand customers’ true banking behavior, we need to go beyond pre-defined banking segments and transactions and look at them from a holistic point of view. This approach would bring together all relevant customer information such as lifetime STRs/SARs, multiple accounts, demographics, and relationship with counterparties to create customer groups and identify normal and abnormal behavior.
2. Compliance programs are not sustainable unless they are explainable. Transparency has been an issue with AI/ML-powered solutions in any area. RegTech solutions are no exception. Results from a compliance solution should be explainable and auditable so that there is a complete audit trail to back up whatever decision a compliance professional is taking to handle all non-productive cases.
3. Data integrity is at the heart of machine learning.
To unlock the full potential of machine learning in compliance programs, the quality of input data must be verified. The first step in generating quality output from a machine learning model is to ensure that the right quality data is going into the model. If things are really bad, machine learning is nothing but garbage.
4. Less sophistication is better.
One of the problems with modern technologies such as Big Data and AI is that they require highly specialized, expensive people. While looking for AI-powered AML software, ensure it offers explanations for its output in business-friendly language, easy to implement with existing banking infrastructure and low in maintenance.
5. Secondary scoring is a nice way to transition to a new paradigm.
Though basic systems can get “incrementally matter, they can’t become materially better”. At some point in time, the industry is going to recognize, particularly regulators, that innovation is to come and take over. Machine learning is taking over our lives in multi-faceted ways. When we apply machine learning to financial crimes, the fundamental question is how we will move towards the new technology without disrupting the normal business flow and how we will do that in a low-risk way. Secondary scoring is a nice way to transition compliance to a new paradigm. If we layer in machine learning over whatever technologies currently in use, then over time, banks can shift towards machine learning models and phase out the older technology without disrupting business. Subsequently, you may put in an infrastructure of analytics that will serve the whole lifecycle of the behavioral process, onboarding with name matching, transaction monitoring, and investigations.
Collaboration and robust compliance programs
Banks are always looking to detect non-productive alerts in a better way so that they can prioritize alerts better. Currently, they are building rules or machine learning models on top of rules in silos to solve compliance issues. These rules and models are built based on information available to individual banks. However, if we have to move to the next generation of compliance systems, we need to go beyond these custom models that we are building. The fundamental challenge we have is building an ecosystem where each bank can share money laundering patterns to create a robust library of patterns that can help banks make their models more powerful in terms of detecting those scenarios beyond what they already have.
However, as banks cannot share their customer data, building a pattern sharing AML ecosystem is a real challenge. Tookitaki has brought in innovation to address the challenge and offer financial institutions an ability to share money laundering patterns, without data leakage. The approach helps banks enrich their detection rate for suspicious cases, especially complex structuring cases, with significantly low misclassification rates. There had been questions on the practicality of the novel concept. The most important ones and their responses are noted down below.
1. Is pattern sharing applicable across different types of financial institutions?
It’s up to the banks to decide whether they want a specific pattern or not. Also, the typologies work on each banks’ data. If the scenarios are not applicable to your data, then they will not be effective. As part of the regulatory approval, it has to be justified that the framework works or not. For that, it has to undergo rigorous testing, including under-the-line testing and over-the-line testing, and the results are to be validated at the segment level and the rules level.
2. Will the pattern-sharing framework replace existing solutions?
Most of Tookitaki’s regional and global deployments have been complementary solutions. We are looking at providing incremental results by adding new typologies without a ‘rip and replace’. Tookitaki solution can sit on top of existing solutions and drastically improve alerts prioritization with a 50% reduction in false positives. That level of efficiency can only happen when we can bring in industry knowledge rather than siloed knowledge.
3. Will it make sense if there is regulatory pressure to continuously update models?
By default, the concept follows a champion-challenger framework, where the challenger model needs to undergo various validation steps to become a champion model. While regulations for AI-powered model governance in the financial services industry are yet to mature, there are guidelines that banks can follow while taking up AI. Singapore’s FEAT framework (fairness, ethics, accountability, and transparency) and the US OCC guidance on model risk management can be used to promote the ethical and unbiased use of AI within financial institutions.
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Pursuing the AI Journey for AML
Moving from innovation experimentation to day-to-day enterprise usage requires engineering excellence and a business case for change. It’s often more than ROI that banks consider while adopting a fresh approach to AML risk management.
1. It is important to get examiners to agree with you.
As standard setters in the industry, examiners play a crucial role in the adoption of AI in financial services. As one of the initial steps, it is recommended that business stakeholders talk to examiners, demo the solution and get their buy-in. Various examiners compete with each other, so if we can get one to adopt modern technology, the others will follow.
2. A proper balance between better risk management and monetary benefits is crucial.
While ROI is important for each and every bank, the benefits from the use of modern technology in regulatory compliance are primarily about augmented risk management and the ability to do compliance jobs better. These will obviously result in financial benefits in the long run in addition to the competitive edge over peers.
3. Have we reached a machine vs. human state? Not as of now.
As of now, we cannot take humans out of the compliance process and replace them with machine learning. Human intuition is something that cannot be replaced. Modern compliance technology looks to assist human beings with better analytics and intelligence so that they can do their work faster and better.
Conclusion
The roundtable concluded that the future of regulatory compliance will be powered by advanced technology. As criminals are using technology to outsmart legacy systems, financial institutions are lagging behind with process inefficiencies and heightened risk. The use of right technology can help them mitigate risk as well as reduce operational costs significantly. It is important to create awareness about novel technological approaches in the market to address AML compliance and ensure sustainability. In addition, collaboration among various industry stakeholders such as financial institutions, regulators and technology firms can surely alter the current AML compliance picture and make the world a better place to live.
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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.
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.
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:
- Prioritise real-time fraud detection tools that work across payment channels and digital platforms.
- Train your teams—fraudsters are exploiting human error more than technical flaws.
- Invest in explainable AI to build trust with regulators and internal stakeholders.
- Use layered defences: Combine transaction monitoring, device fingerprinting, behavioural analytics, and biometric verification.
- 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.
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.
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:
- 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. - Educate Senior Leadership
Executives need to be aware that their digital identities can be hijacked. Regular briefings on impersonation risks are essential. - Adopt Real-Time Behavioural Monitoring
Advanced analytics can flag abnormal transaction patterns — even when the request appears “approved” by an authority figure. - Invest in Deepfake Detection Tools
There are now software solutions that scan video content for artefacts, inconsistencies, or signs of AI manipulation. - 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.
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.
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The Rising Cost of AML Compliance in Australia: Can Smarter Tools Reduce the Burden?
Anti-Money Laundering (AML) compliance in Australia has never been more critical — or more expensive.
As regulatory scrutiny increases and financial crime becomes more complex, financial institutions are under pressure to spend more time, money, and resources just to keep up.
But is this sustainable? And is there a smarter way to stay compliant without letting costs spiral out of control?
Let’s take a closer look at why compliance costs are rising, what’s at stake for banks and fintechs in Australia, and how modern AML solutions, powered by AI and collaboration, are helping institutions future-proof their compliance programmes.
Why Are AML Compliance Costs Rising in Australia?
Over the past few years, Australia has seen a surge in regulatory activity around financial crime. From high-profile casino investigations to AUSTRAC’s growing enforcement role, the message is clear: AML compliance is non-negotiable.
Here’s what’s driving the rising cost:
1. Tighter Regulatory Expectations
AUSTRAC expects more than just basic transaction monitoring. Institutions must demonstrate proactive risk assessments, tailored customer due diligence (CDD), and robust ongoing monitoring — all supported by detailed documentation and audit trails.
2. More Complex Financial Crime
Criminals are getting smarter. Whether it’s mule networks exploiting instant payments or layering funds across crypto and traditional channels, detecting illicit activity now requires more sophisticated tools and deeper data insights.
3. Manual Workflows and Legacy Systems
Many institutions still rely on outdated systems and siloed processes, which increase the burden on compliance teams and inflate operational costs. Manually reviewing false positives or investigating fragmented alerts takes time — and people.
4. Reputational Risk and Fines
In recent years, enforcement actions have brought AML failures into public view — from Crown and Star casinos to financial institutions under investigation. The reputational damage, legal risk, and remediation costs far outweigh the cost of modernising compliance infrastructure.
What Do Rising AML Costs Look Like on the Ground?
According to industry estimates, large Australian banks are spending hundreds of millions annually on compliance-related activities. Mid-sized banks and fintechs may not face the same scale, but they often carry a disproportionate burden due to leaner teams and tighter budgets.
Here’s where the costs add up:
- Hiring and retaining skilled AML staff
- Managing alert fatigue from legacy monitoring systems
- Frequent audits and remediation exercises
- Technology upgrades and consultant fees
- Delays in customer onboarding due to manual CDD reviews
These costs aren’t just financial — they also affect speed, agility, and customer experience.
Can Smarter Tools Reduce the Burden?
The short answer: yes — but only if they’re the right tools.
Smarter AML compliance doesn't mean more tools. It means better tools that are purpose-built for modern financial crime risks. Here's what that looks like:
What Smarter AML Compliance Looks Like
1. Behavioural Transaction Monitoring
Modern systems go beyond rule-based monitoring to detect suspicious patterns based on behaviour. This reduces false positives and increases detection accuracy — freeing up analysts to focus on what matters.
2. Federated Learning and Shared Intelligence
Collaborative platforms enable institutions to share insights and typologies without sharing sensitive data. This reduces blind spots and helps detect new risks earlier — especially in cross-border and real-time payments.
3. Automation and AI Assistants
AI-powered investigation assistants can summarise alerts, prioritise high-risk cases, and auto-generate audit trails — helping compliance teams do more with less.
4. Dynamic Risk Scoring
Instead of static scoring, smarter systems update customer risk profiles in real-time based on behaviour, location, transaction type, and other dynamic inputs.
5. Plug-and-Play Integration
Modern AML solutions should integrate easily with core banking systems, customer onboarding tools, and case management platforms — reducing overhead and ensuring a seamless compliance workflow.
How Tookitaki’s FinCense Is Helping Australian Institutions Stay Ahead
At Tookitaki, we’ve designed FinCense to deliver smarter compliance — not just cheaper, but better.
Built on a modular, federated AI framework, FinCense empowers banks, fintechs, and payment platforms to stay ahead of financial crime risks without overburdening teams or budgets.
With FinCense, institutions get:
- Up to 72% reduction in false positives
- 3.5x faster case resolutions
- Real-time, scenario-based monitoring tailored to local risks
- Federated typology sharing via the AFC Ecosystem
- Smart Disposition engine for audit-ready alert summaries
Whether you're dealing with domestic mule activity, complex layering, or regulatory audits — FinCense helps you detect, investigate, and respond with speed, accuracy, and confidence.
The Stakes Are Higher Than Ever
Financial crime is evolving rapidly, and so is the regulatory bar. But throwing more people, more tools, and more money at the problem isn’t the answer.
The future of AML compliance in Australia lies in smarter systems, collaborative intelligence, and scalable solutions that adapt as the threat landscape changes.
Final Thought
Rising AML compliance costs don’t have to mean rising pain.
With the right technology, institutions in Australia can reduce risk, improve efficiency, and build lasting trust with regulators and customers alike.
If you're ready to reduce the cost and complexity of compliance, without compromising on quality — Tookitaki is here to help.
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.
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.
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:
- Prioritise real-time fraud detection tools that work across payment channels and digital platforms.
- Train your teams—fraudsters are exploiting human error more than technical flaws.
- Invest in explainable AI to build trust with regulators and internal stakeholders.
- Use layered defences: Combine transaction monitoring, device fingerprinting, behavioural analytics, and biometric verification.
- 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.
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.
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:
- 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. - Educate Senior Leadership
Executives need to be aware that their digital identities can be hijacked. Regular briefings on impersonation risks are essential. - Adopt Real-Time Behavioural Monitoring
Advanced analytics can flag abnormal transaction patterns — even when the request appears “approved” by an authority figure. - Invest in Deepfake Detection Tools
There are now software solutions that scan video content for artefacts, inconsistencies, or signs of AI manipulation. - 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.
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 Rising Cost of AML Compliance in Australia: Can Smarter Tools Reduce the Burden?
Anti-Money Laundering (AML) compliance in Australia has never been more critical — or more expensive.
As regulatory scrutiny increases and financial crime becomes more complex, financial institutions are under pressure to spend more time, money, and resources just to keep up.
But is this sustainable? And is there a smarter way to stay compliant without letting costs spiral out of control?
Let’s take a closer look at why compliance costs are rising, what’s at stake for banks and fintechs in Australia, and how modern AML solutions, powered by AI and collaboration, are helping institutions future-proof their compliance programmes.
Why Are AML Compliance Costs Rising in Australia?
Over the past few years, Australia has seen a surge in regulatory activity around financial crime. From high-profile casino investigations to AUSTRAC’s growing enforcement role, the message is clear: AML compliance is non-negotiable.
Here’s what’s driving the rising cost:
1. Tighter Regulatory Expectations
AUSTRAC expects more than just basic transaction monitoring. Institutions must demonstrate proactive risk assessments, tailored customer due diligence (CDD), and robust ongoing monitoring — all supported by detailed documentation and audit trails.
2. More Complex Financial Crime
Criminals are getting smarter. Whether it’s mule networks exploiting instant payments or layering funds across crypto and traditional channels, detecting illicit activity now requires more sophisticated tools and deeper data insights.
3. Manual Workflows and Legacy Systems
Many institutions still rely on outdated systems and siloed processes, which increase the burden on compliance teams and inflate operational costs. Manually reviewing false positives or investigating fragmented alerts takes time — and people.
4. Reputational Risk and Fines
In recent years, enforcement actions have brought AML failures into public view — from Crown and Star casinos to financial institutions under investigation. The reputational damage, legal risk, and remediation costs far outweigh the cost of modernising compliance infrastructure.
What Do Rising AML Costs Look Like on the Ground?
According to industry estimates, large Australian banks are spending hundreds of millions annually on compliance-related activities. Mid-sized banks and fintechs may not face the same scale, but they often carry a disproportionate burden due to leaner teams and tighter budgets.
Here’s where the costs add up:
- Hiring and retaining skilled AML staff
- Managing alert fatigue from legacy monitoring systems
- Frequent audits and remediation exercises
- Technology upgrades and consultant fees
- Delays in customer onboarding due to manual CDD reviews
These costs aren’t just financial — they also affect speed, agility, and customer experience.
Can Smarter Tools Reduce the Burden?
The short answer: yes — but only if they’re the right tools.
Smarter AML compliance doesn't mean more tools. It means better tools that are purpose-built for modern financial crime risks. Here's what that looks like:
What Smarter AML Compliance Looks Like
1. Behavioural Transaction Monitoring
Modern systems go beyond rule-based monitoring to detect suspicious patterns based on behaviour. This reduces false positives and increases detection accuracy — freeing up analysts to focus on what matters.
2. Federated Learning and Shared Intelligence
Collaborative platforms enable institutions to share insights and typologies without sharing sensitive data. This reduces blind spots and helps detect new risks earlier — especially in cross-border and real-time payments.
3. Automation and AI Assistants
AI-powered investigation assistants can summarise alerts, prioritise high-risk cases, and auto-generate audit trails — helping compliance teams do more with less.
4. Dynamic Risk Scoring
Instead of static scoring, smarter systems update customer risk profiles in real-time based on behaviour, location, transaction type, and other dynamic inputs.
5. Plug-and-Play Integration
Modern AML solutions should integrate easily with core banking systems, customer onboarding tools, and case management platforms — reducing overhead and ensuring a seamless compliance workflow.
How Tookitaki’s FinCense Is Helping Australian Institutions Stay Ahead
At Tookitaki, we’ve designed FinCense to deliver smarter compliance — not just cheaper, but better.
Built on a modular, federated AI framework, FinCense empowers banks, fintechs, and payment platforms to stay ahead of financial crime risks without overburdening teams or budgets.
With FinCense, institutions get:
- Up to 72% reduction in false positives
- 3.5x faster case resolutions
- Real-time, scenario-based monitoring tailored to local risks
- Federated typology sharing via the AFC Ecosystem
- Smart Disposition engine for audit-ready alert summaries
Whether you're dealing with domestic mule activity, complex layering, or regulatory audits — FinCense helps you detect, investigate, and respond with speed, accuracy, and confidence.
The Stakes Are Higher Than Ever
Financial crime is evolving rapidly, and so is the regulatory bar. But throwing more people, more tools, and more money at the problem isn’t the answer.
The future of AML compliance in Australia lies in smarter systems, collaborative intelligence, and scalable solutions that adapt as the threat landscape changes.
Final Thought
Rising AML compliance costs don’t have to mean rising pain.
With the right technology, institutions in Australia can reduce risk, improve efficiency, and build lasting trust with regulators and customers alike.
If you're ready to reduce the cost and complexity of compliance, without compromising on quality — Tookitaki is here to help.