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AML Alert Management: How AI can Augment Your Compliance Efficiency

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Tookitaki
07 January 2021
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5 min

We live in a digital world where the threat of money laundering is both increasing and evolving in unprecedented ways and scale. Financial institutions are finding it tough to compete with financial criminals and their sophisticated schemes. In order to establish a robust anti-money laundering (AML) compliance program, it is important for financial institutions to have a first line of defence that monitor, investigate and report suspicious activities. The setup normally involves a combination of technology and staff. Financial institutions rely on a single solution or multiple solutions that monitor and screen transactions, accounts and customers, and generate alerts based on defined rules and thresholds. Once alerts are generated, AML investigators use documented risk-based policies and human judgement to determine if an alert is truly risky.

In present times when AML systems generate several thousands of alerts every month and most of the alerts being identified as no-value alerts (often referred to as false positives), it is becoming increasingly difficult for the compliance team to both keep deadlines and correctly identify cases that matter. Research says that banks are wasting more USD 3.5 billion per year chasing false leads because of outdated AML systems that rely on stale rules and scenarios and generate millions of false positives.

Having stated the problem, we are trying to explain here the potential of artificial intelligence (AI) in alerts management with a real-life use case.

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Why is AML Alerts Management Important for Compliance? 

AML alert management is the process in which a financial institution scrutinizes each and every system-generated alerts for potential suspicious activity. The process helps in separating alerts between those can be ignored and those should be investigated. Having a greater responsibility to participate in AML regulations and facing stricter scrutiny for AML compliance, the financial sector takes alert management seriously and adopts multiple methods to mitigate AML risk. However, the sector is facing one monster of a problem at the moment. As today’s financial institutions deal with millions of daily transactions, several thousands of routine financial transactions are being flagged every month, leading to a whopping number of unproductive alerts. In order to deal with the high rate of false positives generated by their solutions, financial institutions generally adopt a three-tier investigation approach:

  • Triage all alerts to eliminate activity that can be easily classified as ‘no risk’.
  • Investigate remaining alerts further to eliminate activity deemed not suspicious and aggregate common alerts into cases.
  • Case investigation for investigative financial crime analysis to gather additional information and make informed decisions as to whether to file a Suspicious Activity Report (SAR)/Suspicious Transaction Report (STR).

While this approach provides some cost efficiency in leveraging most senior staff to only cases, it is unable to determine the relative risk of individual alerts and adds tremendous operational pressure to AML teams. Research has shown that the average SAR is filed more than 5 months after the suspicious transactions occur. In addition, the approach triples the touches an alert needs to be reviewed to become a suspicious report. Combined, these misses become inhibitors to the prevention of financial crime.

AI Transformation for Worrisome AML Alert Management Problems

AI has brought in disruption in many industries with its ability to mine, structure and analyse huge volumes of data and provide actionable insights. AI can take up repetitive tasks, saving valuable time, effort and resources that can be redirected perform higher-value functions. From an AML compliance perspective, AI can extract risk-relevant information from large volumes of data and present that information in a better coherent manner, making the process of identifying high-risk transactions and clients even easier in the fight against financial crime. The process inefficiencies in AML alert management can be reduced significantly by using the power of AI in the following ways:

  • Reducing manual efforts and saving time: In order to go through each and every AML alert, compliance departments within financial institutions need an army of resources. Considering the ever-increasing number of alerts within these institutions, it is not a feasible and sustainable option for institutions to increase the number of skilled and costly compliance staff. AI can effectively automate repetitive tasks, saving a lot of man-hours for financial institutions and eliminating alert backlogs.
  • Optimal use of scarce compliance resources: Prudent use of skilled compliance staff is vital for compliance departments today. The compliance staff have been given the task of finding out the needle in the haystack and are wasting most of their precious time in closing low-risk alerts. With advanced machine learning, alerts can be grouped based on risk levels so that more time and talent can be dedicated to those that matter.
  • Augmenting process efficiency and accuracy: Heavy workloads can cause distractions and human errors within AML compliance teams, elevating regulatory risk. While AI cannot replace human judgment in the AML compliance process, it can assist humans with predictions, recommendations and powerful analytics, enabling faster and accurate decision making.

Today, the banking and financial services industries are realizing the power of AI and Machine Learning in preventing sophisticated money laundering. Surprisingly, the adoption of AI and Machine Learning is being slowed not by the exceptional improvements the tools provide but with how and where humans and AI work together to drive better outcomes.

Tookitaki’s AI Revolution in Alert Management

Tookitaki developed Anti-Money Laundering Suite (AMLS), a next-generation solution that is powered by advanced Machine Learning and Big Data analytics. AMLS is purpose-built to cut through the noise and clutter generated by legacy AML transaction monitoring and screening processes. It provides an automated alert triaging function, called Smart Triage management. AMLS is validated by leading global advisory firms and banks across Asia Pacific, Europe and North America.

AMLS Smart Triage management learns from historical data and investigative outcomes to build a highly accurate ML model to differentiate alerts into three AML risk levels – Minimal Risk (L1), Low Risk (L2) and High Risk (L3). These categories effectively replace the traditional triage function. The highly accurate alert classification helps compliance teams to allocate time and experience judiciously and effectively address alert backlogs. Compliance analysts can now focus on those high-risk cases (L3 and L2) that require more time to investigate and close. Meanwhile, they can close low-risk alerts (L1) with minimal investigation. AMLS generates a probability score for all alerts, along with an explanation to guide the investigator to make the right decision faster.

Transaction Monitoring Today and with Tookitaki AMLS

Transaction Monitoring Today and with Tookitaki AMLS

AMLS Smart Triage management complements the existing rules-driven primary systems and can be operationalized easily, following risk-based protocols.

Our Experience with a Large Asian Bank

Recently, our AMLS solution went live within the premises of United Overseas Bank (UOB), one of the top 3 banks in Singapore, making us the first company in the APAC region to deploy a complete AI-powered AML solution in production concurrently to transaction monitoring and name screening. By deploying AMLS, UOB could effectively create workflows for prioritizing alerts based on their risk levels to help the compliance team focus on those alerts that matter.

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The use of a standard framework for AML alert management with common filtering tools based on static rules and thresholds would lead to operational constraints such as a high number of false positives to be manually addressed, low SAR/STR rates and labour-intensive case analysis. By using machine learning and advanced data analysis techniques, compliance analysts can better manage their time and increase productivity. They can help save time spent on the release of false positives and manual case analysis, ensuring an enhanced, secured and robust AML alert management framework.

Financial criminals are adapting and evolving money laundering techniques at an accelerating pace. While the benefits of AI are obvious, it is now time for financial institutions to embrace next-generation solutions in order to maintain the safety and soundness of the global financial system. With our proven data-driven, risk-based approach, financial institutions of any size across the globe can effectively move away from static rules-based approaches in AML monitoring. Tookitaki AMLS delivers machine learning in an off-the-shelf package solution that learns over time and provides the functionality to maintain a compliant AML program.

For a demo of our award-winning solution, please get in touch with us.

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30 Jul 2025
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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.

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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
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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.
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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
Blogs
28 Jul 2025
6 min
read

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.

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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.

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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.

The Rising Cost of AML Compliance in Australia: Can Smarter Tools Reduce the Burden?