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5 Top Myths and Facts about AI Implementation in AML Programs

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Jerin Mathew
02 Jul 2020
4 min
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We are more confirmed about the power of Artificial Intelligence (AI) to transform lives and businesses now. There are countless possible applications of AI and machine learning at present, and we see and hear exciting ways how these modern technologies are being used for value addition or for tasks deemed impossible with human intelligence. When we move to the anti-money laundering (AML) compliance space, the potential of AI is immense. Many banks have pilot projects ongoing with the multiple vendors after regulators including the US Financial Crime Enforcement Network (FinCEN) encouraged banks “to consider, evaluate, and, where appropriate, responsibly implement innovative approaches to meet their Bank Secrecy Act/anti-money laundering (BSA/AML) compliance obligations, in order to further strengthen the financial system against the illicit financial activity.” Increasing complexity of AML threats during the COVID-19 times, ever-increasing volumes of data to analyse, false alerts rising to unmanageable levels, ongoing reliance on manual processes and the ballooning cost of compliance are prompting many financial institutions to adopt modern technology and improve their risk profile.

Many banks were able to develop scientifically sound machine learning algorithms that provide obvious effectiveness and efficiency improvements. However, most of these projects are finding it difficult to come out of the lab as deploying a machine learning model in production with real value addition is a harder task than what we expected. Many banks are stuck at the AI implementation stage where they come face-to-face with certain barriers unfathomed before. During a webinar, we asked our audience about the barriers that prevent their organization from adopting AI in AML compliance and we got the following result.

Survey: Factors Inhibiting AI Adoption in AML Programs

Crossing this ‘AI chasm’ is often difficult but not impossible. Here, we are trying to dive deep into certain myths that hinder AI implementation and bust them with relevant facts.

Myth 1: AI systems need massive volumes of data to be effective

Of course, data is at the heart of all machine learning models. However, it is the quality of data, rather than quantity, that decides a machine learning model’s use in the real world. For machine learning, the basic rule is ‘garbage in, garbage out’. There are ways to build effective and implementable machine-learning models with a minimal set of historical data. However, for algorithms to become smarter over time, they constantly require new data. These models should have the ability to collect, ingest and learn from incremental data and update themselves automatically at regular intervals.

Myth 2: AI is a ‘Black-box’; you give an input and you get an output

In general, the process of an AI algorithm producing an output from input data points by correlating specific data features is difficult for data scientists and users to interpret. Many renowned AI projects were abandoned due to this issue. The same problem is relevant in the banking industry as well. If regulators pose a question: how AI has reached at a conclusion with regard to a banking problem, banks should be able to explain the same. Such an audit is not possible with a ‘black box’ AI model. Most widely accepted model governance frameworks have model transparency as a key element for adoption. Research is ongoing in this area to make transparent models. For example, Tookitaki has created a framework and method to create explainable machine-learning models. The patent-pending ‘Glass-box’ approach helps create transparent AI models with interpretable predictions. It provides actionable insights to users, enabling them to make business-relevant decisions in a quicker manner.

Myth 3: AI systems are difficult to integrate into existing systems

In the machine learning lifecycle, the stage of integration into existing systems comes after exploratory data analysis, model selection and model evaluation. The ability of a machine learning model to integrate into upstream and downstream systems is crucial for its successful deployment in production. There are cutting-edge engineering techniques available to seamlessly integrate models into existing systems. For example, Tookitaki’s AI-enabled solutions come with pre-packed connectors for various data sources making them adaptable to various enterprise architectures and up-stream systems. Also, well-designed REST interfaces and detailed integration guides make it easier for downstream applications to consume the output from Machine Learning pipelines.

Myth 4: It is expensive to deploy AI-powered AML system in production

There are various factors that impact the cost of an AI-powered AML system. First of all, institutions can choose between in-house development and third-party software. From a cost perspective, third-party options fare better. Data format, data storage, data structure, processing speed and dashboard requirements are some other areas where firms can decide and optimize based on their needs. In order to save hugely on hardware, software and licenses, they can also opt for cloud and API-based models. In short, the cost of implementing AI depends largely on the customer’s requirements.

Myth 5: AI systems have longer ROI realization period

Business users often have concerns about the return on investment (ROI) of an AI system. Generalised and pre-packed AI models for AML compliance help financial institutions avoid starting from scratch. Assisted by the vendor’s expertise in the area and technology, these models can be implemented easily for faster time-to-value. They can be adapted quickly to existing AML compliance workflows and human resources can be allocated optimally to suit specific needs.

In order to overcome the barriers to AI implementation in AML programs, financial institutions should identify the areas where AI is needed the most. They can be transaction monitoring, names/sanctions/payments screening, customer risk scoring, etc. Once the areas are decided, the companies need to consider their integration options and deployment architecture. While selecting vendors, those providing transparent models and a robust model governance framework, where models are automatically updated amid incremental changes in data, should be given preference.

There are proven examples, such as that of Tookitaki, of putting cutting-edge machine learning research into production. Deploying AI-powered AML systems in production to improve operational efficiency and returns is just the beginning. There are also ways in which financial institutions with productised AI-based AML models can enhance their financial crime detection by leveraging collective intelligence. Join our virtual roundtable on ‘Federated Learning: Bringing together the industry’s AML intelligence’ to visualize the future where AML patterns (not customer data) are shared to stop the bad actors together.

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Blogs
19 Jun 2025
5 min
read

Australia on Alert: Why Financial Crime Prevention Needs a Smarter Playbook

From traditional banks to rising fintechs, Australia's financial sector is under siege—not from market volatility, but from the surging tide of financial crime. In recent years, the country has become a hotspot for tech-enabled fraud and cross-border money laundering.

A surge in scams, evolving typologies, and increasingly sophisticated actors are pressuring institutions to confront a hard truth: the current playbook is outdated. With fraudsters exploiting digital platforms and faster payments, financial institutions must now pivot from reactive defences to real-time, intelligence-led prevention strategies.

The Australian government has stepped up through initiatives like the National Anti-Scam Centre and legislative reforms—but the real battleground lies inside financial institutions. Their ability to adapt fast, collaborate widely, and think smarter will define who stays ahead.

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The Evolving Threat Landscape

Australia’s shift to instant payments via the New Payments Platform (NPP) has revolutionised financial convenience. However, it's also reduced the window for detecting fraud to mere seconds—exposing institutions to high-velocity, low-footprint crime.

In 2024, Australians lost over AUD 2 billion to scams, according to the ACCC’s Scamwatch report:

  • Investment scams accounted for the largest losses at AUD 945 million
  • Remote access scams followed with AUD 106 million
  • Other high-loss categories included payment redirection and phishing scams

Behind many of these frauds are organised crime groups that exploit vulnerabilities in onboarding systems, mule account networks, and compliance delays. These syndicates operate internationally, often laundering funds through unsuspecting victims or digital assets.

Recent alerts from AUSTRAC and ASIC also highlighted the misuse of cryptocurrency exchanges, online gaming wallets, and e-commerce platforms in money laundering schemes. The message is clear: financial crime is mutating faster than most defences can adapt.

Australia FC

Why Traditional Defences Are Falling Short

Despite growing threats, many financial institutions still rely on legacy systems that were designed for a static risk environment. These tools:

  • Depend on manual rule updates, which can take weeks or months to deploy
  • Trigger false positives at scale, overwhelming compliance teams
  • Operate in silos, with no shared visibility across institutions

For instance, a suspicious pattern flagged at one bank may go entirely undetected at another—simply because they don’t share learnings. This fragmented model gives criminals a huge advantage, allowing them to exploit gaps in coverage and coordination.

The consequences aren’t just operational—they’re strategic. As financial criminals embrace automation, phishing kits, and AI-generated deepfakes, institutions using static tools are increasingly being outpaced.

The Cost of Inaction

The financial and reputational fallout from poor detection systems can be severe.

1. Consumer Trust Erosion

Australians are increasingly vocal about scam experiences. Victims often turn to social media or regulators after being defrauded—especially if they feel the bank was slow to react or dismissive of their case.

2. Regulatory Enforcement

AUSTRAC has made headlines with its tough stance on non-compliance. High-profile penalties against Crown Resorts, Star Entertainment, and non-bank remittance services show that even giants are not immune to scrutiny.

3. Market Reputation Risk

Investors and partners view AML and fraud management as core risk factors. A single failure can trigger media attention, customer churn, and long-term brand damage.

The bottom line? Institutions can no longer afford to treat compliance as a cost centre. It’s a driver of brand trust and operational resilience.

Rethinking AML and Fraud Prevention in Australia

As criminal innovation continues to escalate, the defence strategy must be proactive, intelligent, and collaborative. The foundations of this smarter approach include:

✅ AI-Powered Detection Systems

These systems move beyond rule-based alerts to analyse behavioural patterns in real-time. By learning from past frauds and adapting dynamically, AI models can flag suspicious activity before it becomes systemic.

For example:

  • Unusual login behaviour combined with high-value NPP transfers
  • Layered payments through multiple prepaid cards and wallets
  • Transactions just under the reporting threshold from new accounts

These patterns may look innocuous in isolation, but form high-risk signals when viewed in context.

✅ Federated Intelligence Sharing

Australia’s siloed infrastructure has long limited inter-institutional learning. A federated model enables institutions to share insights without exposing sensitive data—helping detect emerging scams faster.

Shared typologies, red flags, and network patterns allow compliance teams to benefit from collective intelligence rather than fighting crime alone.

✅ Human-in-the-Loop Collaboration

Technology is only part of the answer. AI tools must be designed to empower investigators, not replace them. When AI surfaces the right alerts, compliance professionals can:

  • Reduce time-to-investigation
  • Make informed, contextual decisions
  • Focus on complex cases with real impact

This fusion of human judgement and machine precision is key to staying agile and accurate.

A Smarter Playbook in Action: How Tookitaki Helps

At Tookitaki, we’ve built an ecosystem that reflects this smarter, modern approach.

FinCense is an AI-native platform designed for real-time detection across fraud and AML. It automates threshold tuning, uses network analytics to detect mule activity, and continuously evolves with new typologies.

The AFC Ecosystem is our collaborative network of compliance professionals and institutions who contribute real-world risk scenarios and emerging fraud patterns. These scenarios are curated, validated, and available out-of-the-box for immediate deployment in FinCense.

Some examples already relevant to Australian institutions include:

  • QR code-enabled scams using fake invoice payments
  • Micro-laundering via e-wallet top-ups and fast NPP withdrawals
  • Cross-border layering involving crypto exchanges and shell businesses

Together, FinCense and the AFC Ecosystem enable institutions to:

Building a Future-Ready Framework

The question is no longer if financial crime will strike—it’s how well prepared your institution is when it does.

To be future-ready, institutions must:

  • Break silos through collaborative platforms
  • Invest in continuous learning systems that evolve with threats
  • Equip teams with intelligent tools, not more manual work

Those who act now will not only improve operational resilience, but also lead in restoring public trust.

As the financial landscape transforms, so too must the compliance infrastructure. Tomorrow’s threats demand a shared response, built on intelligence, speed, and community-led innovation.

Strengthening AML Compliance Through Technology and Collaboration

Conclusion: Trust Is the New Currency

Australia is at a turning point. The cost of reactive, siloed compliance is too high—and criminals are already exploiting the lag.

It’s time to adopt a smarter playbook. One where technology, collaboration, and shared intelligence replace outdated controls.

At Tookitaki, we’re proud to build the Trust Layer for Financial Services—empowering banks and fintechs to:

  • Stop fraud before it escalates
  • Reduce false positives and compliance fatigue
  • Strengthen transparency and accountability

Through FinCense and the AFC Ecosystem, our mission is simple: enable smarter decisions, faster actions, and safer financial systems.

Australia on Alert: Why Financial Crime Prevention Needs a Smarter Playbook
Blogs
23 Jun 2025
5 min
read

Behind the Compliance Curtain: The Future of AML in Australia

Australia’s sunny financial reputation has come under scrutiny—and this time, the spotlight is global.

From casino scandals to multi-billion-dollar remittance breaches, the country’s anti-money laundering (AML) framework is facing a pivotal moment. What was once seen as a gold standard in regional governance is now under pressure to catch up—and compliance officers across banks, fintechs, and regulatory bodies are watching closely.

So what lies behind the curtain of AML in Australia today—and what must the financial community do next?

Talk to an Expert

The AML Landscape in Australia: Where Things Stand

Australia’s AML/CFT regime has long been led by AUSTRAC, the nation’s financial intelligence unit and regulator. Over the past few years, AUSTRAC has made headlines with major enforcement actions:

  • Westpac (2020): A $1.3 billion penalty over 23 million breaches of AML laws.
  • Crown Resorts (2022): Systemic failure to monitor high-risk transactions, especially tied to junket operators and casinos.
  • Star Entertainment Group (2022): Similar failings in AML controls and customer due diligence.

These cases revealed a troubling pattern: AML risks were known, red flags existed, but institutions lacked either the technology, urgency, or capability to respond in real time.

More worryingly, Australia’s AML legal framework—particularly its coverage of non-financial sectors like lawyers, accountants, real estate agents, and high-value dealers—remains incomplete. This gap in regulatory coverage continues to raise red flags with global watchdogs, especially the Financial Action Task Force (FATF).

The Tranche 2 Reforms: Closing the Gaps or Buying Time?

For nearly two decades, Australia has delayed implementing the so-called Tranche 2 reforms, which would bring designated non-financial businesses and professions (DNFBPs) into the AML regulatory net.

What Tranche 2 Proposes:

  • AML obligations for real estate professionals, lawyers, accountants, and company service providers.
  • Stronger beneficial ownership transparency.
  • Enhanced customer due diligence and reporting mechanisms across non-financial channels.

Yet, while successive governments have pledged action, progress has been sluggish. Industry bodies have raised concerns about cost, feasibility, and regulatory overreach. But international momentum is building, and patience is wearing thin.

In its 2023 follow-up review, FATF explicitly called out Australia’s delayed reforms. Without Tranche 2, the country faces increased scrutiny—and potential reputational damage that could affect correspondent banking relationships and investor trust.

AUS blog

The Tech Factor: How Modern AML Looks in 2025

Even where regulations exist, legacy compliance systems are struggling to keep up with today’s threats. Financial crime has evolved. So must the tools to fight it.

What’s Changed:

  • Speed: Real-time payments and digital wallets mean funds can be layered, split, and moved across jurisdictions in seconds.
  • Complexity: Fraudsters are using mules, shell companies, and social engineering to blend illicit flows with legitimate ones.
  • Volume: Transaction volumes are rising, making manual reviews and static rules increasingly unviable.

Modern AML compliance now demands real-time monitoring, behavioural analysis, and AI-driven detection engines that adapt to new patterns as they emerge. This is where advanced platforms like Tookitaki’s FinCense come in—offering scenario-driven intelligence and federated learning capabilities tailored for high-risk markets like Australia.

Case Insight: Where Detection Failed—and Where Tech Could Have Helped

Consider the AUSTRAC case against Crown Resorts. Red flags—such as large, unexplained cash deposits, transactions linked to politically exposed persons (PEPs), and high-risk jurisdictions—were not acted upon for months, sometimes years.

The problem wasn’t a lack of data. It was a failure to connect the dots in real time.

With an adaptive AML system like FinCense in place, the scenario might have looked different:

  • Suspicious transaction patterns would have triggered real-time alerts.
  • Beneficiary risk scoring could have flagged high-risk links earlier.
  • AI-based learning could have surfaced anomalous activity invisible to static rule sets.

The outcome? Faster intervention, reduced institutional risk, and regulatory confidence.

Building the Future: Tookitaki’s Role in Strengthening Australia’s AML Defences

Tookitaki’s FinCense platform is designed for the complexity of modern financial ecosystems—especially those navigating regulatory reform and reputational pressure, like Australia.

Key Features That Matter:

  • Federated Learning Engine: Enables institutions to learn from emerging typologies across the region—without sharing sensitive data.
  • Real-Time Transaction Monitoring: Uses AI to surface anomalous patterns and risk indicators at the speed of today’s financial crime.
  • Scenario-Based Approach: Combines regulatory intelligence with real-world cases to keep detection capabilities relevant and context-rich.
  • Audit-Ready Investigations: Helps compliance teams manage alerts, document findings, and demonstrate control effectiveness.

As Tranche 2 looms and regulatory expectations rise, FinCense can help banks and fintechs in Australia stay ahead of both criminal innovation and regulatory demand.

What Compliance Teams Must Do Now

✅ Prepare for Tranche 2 (Even If It’s Not Here Yet)

  • Map exposure to DNFBPs.
  • Engage with vendors and consultants to scope out necessary controls.

✅ Build for Agility and Resilience

  • Invest in dynamic risk-scoring engines and AI-powered analytics.
  • Integrate systems that can adapt, not just flag transactions.

✅ Collaborate and Learn

  • Participate in intelligence-sharing platforms like the AFC Ecosystem.
  • Use scenario libraries to anticipate typologies before they strike.

✅ Rethink ROI from an AML Lens

  • With regulators now tracking the effectiveness (not just existence) of AML systems, demonstrate real-time capability, reduced false positives, and improved investigation turnaround.
Strengthening AML Compliance Through Technology and Collaboration

Conclusion: The Curtain’s Up—What Will Australia Do Next?

Australia stands at a crossroads. Behind the curtain of its legacy AML system lies both risk and opportunity.

The risk is clear: continued global scrutiny, regulatory gaps, and potential grey listing if reforms stall.
But the opportunity is greater: to lead the region with tech-driven, intelligence-led compliance that’s faster, smarter, and more collaborative than ever.

As the regulatory environment evolves, so must the institutions within it. With the right partners, like Tookitaki, and a commitment to real-time defences, Australia can transform its AML posture from reactive to revolutionary.

Because in the fight against financial crime, detection is no longer enough. It’s time to defend.

Behind the Compliance Curtain: The Future of AML in Australia
Blogs
02 Jul 2025
4 min
read

Inside AUSTRAC: Navigating Australia’s AML/CTF Regulations in a High-Risk Era

As money laundering methods grow more sophisticated, the pressure on financial institutions to detect, report, and prevent financial crime is intensifying — and AUSTRAC is at the centre of it all.
In an era where financial ecosystems are rapidly digitising, AUSTRAC’s role in overseeing Anti-Money Laundering (AML) and Counter-Terrorism Financing (CTF) compliance has become mission-critical. For banks, fintechs, and other reporting entities, staying ahead of regulatory expectations is no longer just a compliance issue — it’s a matter of reputation, trust, and long-term viability.

In this blog, we explore:

  • AUSTRAC’s mandate and structure
  • Key AML/CTF obligations under Australian law
  • Landmark enforcement cases
  • Upcoming reforms, including Tranche 2
  • FATF scrutiny and global compliance pressures
  • How tech-forward compliance strategies are reshaping the future
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What is AUSTRAC and Why Does It Matter?

AUSTRAC — the Australian Transaction Reports and Analysis Centre — is the government body responsible for detecting and disrupting criminal abuse of Australia’s financial system.

AUSTRAC has a dual mandate:

  • Regulator: Supervises compliance with AML/CTF obligations.
  • Financial Intelligence Unit (FIU): Collects and analyses data to support law enforcement, national security, and international counterparts.

It works with over 17,000 reporting entities, ranging from traditional banks to digital wallets, remittance providers, gaming platforms, and more. As both a data collector and enforcer, AUSTRAC is uniquely positioned to uncover illicit financial activity at scale.

A Brief History of AML/CTF Regulation in Australia

Australia’s journey in strengthening its anti-money laundering and counter-terrorism financing framework began in earnest with the passage of the AML/CTF Act in 2006. This legislation introduced foundational obligations such as KYC procedures, transaction monitoring, and reporting requirements for a wide range of financial institutions and service providers.

Over time, the regime has evolved significantly. In 2014, AUSTRAC formalised the risk-based approach, requiring entities to tailor their AML programs based on their specific exposure to financial crime risks.

The period between 2018 and 2020 marked a turning point in enforcement, with AUSTRAC taking decisive action against some of Australia’s largest institutions — including Tabcorp, the Commonwealth Bank, and Westpac — for major compliance failures.

In the years that followed, Tranche 2 reforms were proposed to expand AML/CTF obligations to include professions such as lawyers, accountants, and real estate agents, which are known to be exploited for laundering illicit funds.

As of 2024, these reforms remain under active discussion, with the Australian government under growing pressure from international bodies such as the FATF to close regulatory gaps. The expected passage of Tranche 2 in 2025 would significantly broaden AUSTRAC’s regulatory reach and bring Australia closer in line with global AML standards.

AUSTRAC


Understanding Your AML/CTF Obligations

If your institution provides “designated services” under the AML/CTF Act, here’s what you’re required to do:

🔹 AML/CTF Program (Part A and Part B)

  • Part A: Institutional risk assessments, governance, reporting, and training
  • Part B: Customer identification and verification procedures (KYC)

🔹 Reporting Requirements

  • Suspicious Matter Reports (SMRs)
    Must be submitted when the activity raises suspicion, regardless of the amount.
  • Threshold Transaction Reports (TTRs)
    For cash transactions of AUD 10,000 or more.
  • International Funds Transfer Instructions (IFTIs)
    Mandatory for cross-border fund movements.

🔹 Customer Due Diligence (CDD)

  • Verify customer identity at onboarding
  • Apply Enhanced Due Diligence (EDD) for high-risk customers or transactions
  • Conduct ongoing monitoring

🔹 Record Keeping

  • Maintain transaction and identity verification records for at least 7 years.

AUSTRAC’s Enforcement Power: Learning from Past Failures

AUSTRAC is not just a passive regulator. When institutions fall short, the consequences are severe and public.

The Crown Resorts Case

In 2022, Crown Melbourne and Crown Perth were found guilty of systemic AML/CTF program failures. AUSTRAC investigations revealed:

  • Inadequate risk assessments of high-risk customers and junket operators
  • Poor transaction monitoring
  • Weak governance and oversight

Penalty: AUD 450 million settlement
Impact: Major reputational damage and licence scrutiny

The Westpac Case

Arguably, the most consequential case in Australia’s AML history. In 2020, Westpac was fined AUD 1.3 billion — the largest civil penalty in Australian corporate history — for:

  • Failing to report over 23 million IFTIs
  • Inadequate transaction monitoring
  • Enabling transactions linked to child exploitation networks

These cases underscore the high expectations placed on financial institutions — not just to comply, but to detect, investigate, and prevent abuse of their services.

Australia’s AML Pain Points and What Tranche 2 Means

Unregulated Professions: The Tranche 2 Gap

Australia’s AML/CTF regime currently does not cover “gatekeeper” professions — lawyers, accountants, real estate agents, and company service providers. This gap has drawn criticism from both the FATF and domestic watchdogs.

Tranche 2, expected to be legislated in 2025, will:

  • Extend AML obligations to these sectors
  • Close critical vulnerabilities exploited for shell companies, illicit property purchases, and tax evasion
  • Align Australia with global AML standards

For fintechs and financial institutions, this will mean greater scrutiny of third-party relationships and new customer categories.

FATF Evaluation: Australia Under the Global Lens

The Financial Action Task Force (FATF) — the global AML watchdog — is expected to conduct its next mutual evaluation of Australia soon. In its last review, Australia was flagged for:

  • Delays in enacting Tranche 2 reforms
  • Over-reliance on self-regulation in some sectors
  • Inconsistent enforcement levels

AUSTRAC and the government are now under pressure to demonstrate tangible improvements, including:

  • Broader coverage of at-risk sectors
  • Better risk-based supervision
  • More tech-led compliance outcomes

How Fintechs Can Stay Ahead

For fintechs, the AML/CTF journey can seem overwhelming, especially when scaling across regions. Here are five key steps to staying ahead:

  1. Invest Early in AML Infrastructure
    Don’t wait until licensing or audits to build compliance controls.
  2. Use Technology to Monitor in Real-Time
    Especially for high-velocity, small-value transactions common in wallets or P2P services.
  3. Customise Risk Scoring
    A high-risk customer in lending may not be the same as one in gaming or cross-border remittances.
  4. Build for Scalability
    Choose AML platforms that can grow with you, not patchwork solutions.
  5. Stay Informed on Regional Variations
    AUSTRAC’s expectations differ from MAS (Singapore) or BSP (Philippines); know your market.

Why AML Tech Is No Longer Optional

In today’s landscape, manual reviews and static rules don’t cut it. Criminals move faster — and so must compliance teams.

Key advantages of modern AML platforms:

  • Machine learning-based transaction monitoring
  • Dynamic threshold calibration to reduce false positives
  • Real-time alerting and case triage
  • Behavioural profiling and pattern recognition
  • Audit-ready investigation trails

How Tookitaki Helps You Stay Ahead

Tookitaki’s FinCense platform is purpose-built to tackle the real challenges banks and fintechs face in Australia and across APAC.

Key Modules:

🔹 Customer Onboarding Suite
Seamlessly integrates KYC, risk profiling, and watchlist screening

🔹 Transaction Monitoring
Scenario-based detection using patterns from the AFC Ecosystem

🔹 Smart Screening
Covers national ID, aliases, and local nuances — built to minimise false positives

🔹 FinMate (AI Copilot)
Assists investigators with summarised case narratives, red flags, and recommendations

Collaborative Advantage:

FinCense is powered by the AFC Ecosystem — a global community where financial institutions share typologies and red flags anonymously. This collective intelligence improves detection and reduces blind spots for all members.

For institutions facing rising risks from cross-border scams, shell company abuse, and real-time laundering, Tookitaki offers a smarter, community-driven alternative to traditional rule engines.

Strengthening AML Compliance Through Technology and Collaboration


Final Thoughts: A Smarter Future Starts Now

AUSTRAC’s expanding role and the upcoming Tranche 2 reforms signal a future where compliance will be more inclusive, tech-powered, and intelligence-driven.

For banks and fintechs, the opportunity lies not just in complying, but in leading. With the right tools, collaborative frameworks, and forward-thinking partners like Tookitaki, staying ahead of both regulation and risk is no longer an aspiration — it’s an expectation.

Inside AUSTRAC: Navigating Australia’s AML/CTF Regulations in a High-Risk Era