Success Tale: Setting a New Benchmark for AI-based AML Compliance
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Tookitaki achieved a rare and historic milestone as our Anti-Money Laundering Suite (AMLS) solution went live within the premises of United Overseas Bank (UOB), one of the top 3 banks in Singapore. We became the first in the APAC region to deploy a complete AI-powered anti-money laundering (AML) solution in production concurrently to two AML risk dimensions, namely transaction monitoring (TM) and name screening (NS). By deploying Tookitaki’s AI-enabled AMLS, UOB could effectively create workflows for prioritizing TM and NS alerts based on their risk levels to help the compliance team focus on those alerts that matter the most. Vindicating the efficacy, robustness and sustainability of the machine learning models involved, AMLS underwent multiple rounds of rigorous testing, validation and evaluation, involving third-party consultants, before going live in full scale.
Compliance Challenges That Prompted us to be Innovators
Combating money laundering has become an enormous task for financial institutions, and it comes with substantial costs and risks, including but not limited to regulatory, reputational and financial crime risks. During the first half of 2020, APAC regulators imposed almost USD 4 billion in fines for AML violations, according to a report. Ineffective risk-based frameworks, deficient monitoring systems, inadequate review of suspicious activity, and unoptimized resources allocation are some of the widely cited AML compliance problems for financial institutions.
A leading bank in Southeast Asia with a global network of more than 500 offices in 19 countries and territories in Asia Pacific, Europe and North America, UOB wanted to have a holistic view of money laundering risks and the threat-scape across various banking segments such as corporate, retail and private. Existing static and granular rules-based approaches, which are oblivious of the holistic trend with a narrow and uni-dimensional focus, were not capable of doing the same. For UOB, which is handling about 30 million transactions and more than 5,700 TM alerts per month, existing rules-based systems produced a significant volume of false positives. The situation was not different in the case of the NS process, where the bank screened about 60,000 account names on a monthly basis. These false leads are a drain on productivity as they take significant time and resources to be disposed of. In the AML compliance space, 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, according to research.
Undoubtedly, using limited resources to close off non-material and unimportant alerts is manual and onerous, resulting in huge backlogs for both processes and missed/delayed Suspicious Activity Report (SAR) filings. Furthermore, the ballooning costs of AML compliance coupled with the high volume of backlog alerts swamp compliance teams and potentially distract them from ‘true’ high-risk events and customer circumstances. Alert investigation was a time-consuming and labour-intensive affair as the compliance team spent significant time in gathering data and analysing it to differentiate illegitimate activities from legitimate ones. Disparate data sources and highly complex business processes added to the difficulty of the investigation team in analysing the links between parties and transactions.
These issues prompted the bank to leverage innovation and next-generation technology to enhance existing AML compliance processes, surveillance systems, and alert handling practices. In specific, UOB wanted a next-gen solution that can do the following:
- Identification of non-material false positives for both TM and NS using data from disparate sources.
- Accurate grouping of high-risk alerts for increased focus by compliance personnel.
- Advanced analytics combining data from existing financial crime systems and numerous disparate data sources.
- Faster investigation and resolution of all alerts by connecting the dots within the data, and constructing a more holistic global view of accounts, counterparties and transactions, effectively reducing the high volume of alert backlogs.
AMLS: An Innovation Proven for Robustness, Agility and Sustainability
As part of its ‘AML/CFT Technology Roadmap’ to harness next-generation AI and machine learning-driven technologies to combat money laundering, UOB teamed up with Tookitaki. The bank’s aspiration was to shift beyond rules-based systems to achieve higher performance with machine learning models and other disciplines of AI. Tookitaki’s ability to seamlessly connect with existing AML systems at UOB for data ingestion hastened the bank’s decision to onboard us.
As such, Tookitaki developed AMLS, an end-to-end AML compliance solution that combines supervised and unsupervised machine learning techniques to detect suspicious activities and identify high-risk clients quicker and more accurately. We use a combination of machine learning algorithms to build highly accurate and stable models and techniques such as dynamic clustering which does behavioural segmentation based on composite features. AMLS TM module can prioritise known alerts based on their risk scores and detect new, unknown suspicious patterns. The NS module has three core components – enhanced name matching through a wider range of complex name permutations, reduction of undetermined hits through inference features and accurate alert detection through primary and secondary information. These capabilities help accurately distinguish between false hits and true hits. The major innovative features of the solution are:
- Smart Alert Triage: The solution offers a smart way to triage TM and NS alerts by segregating them into three risk buckets – L1, L2 and L3 – where L3 is the highest-risk bucket. The highly accurate alert classification helps UOB’s compliance team 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 make the right decision faster.
- Champion–Challenger Approach: A core component of our data science platform, this approach enables machine learning models to continuously learn from data shifts and data additions. It helps ensure that the model remains effective and unbiased amid incremental changes in data.
- Explainable AI (XAI) Framework: Our patent-pending XAI framework provides transparent machine learning models, and explainable and documentable predictions to ensure thorough understanding and to conduct quality investigations along with aligning users with the compliance model transparency requirements of regulators.
- Scalability: AMLS uses a combination of distributed data-parallel architecture and machine learning to ensure scalability across the bank’s multiple business lines and complex layers of existing technologies and systems.
Unique Implementation Approach Resulting in Sustained Model Performance
UOB had tested the effectiveness of AMLS in terms of alert prioritization in a six-month pilot started in early 2018. After receiving successful results, which Deloitte validated, the bank tested the solution again with a unique data set and performed another round of model validation. The subsequent machine-learning models outperformed the results we achieved during the pilot. The successful results gave UOB the confidence to move the machine learning models to production and build a tailored solution. Based on the bank’s feedback, Tookitaki introduced various enhancements and additional features into its solution.
While deploying AMLS on UOB premises, we took a unique approach of augmenting existing systems with AI-based smart alert management where our solution would sit on top of existing TM and NS solutions and accurately group alerts for faster closure. In the model training phase, our solution’s powerful integration layer extracted data from existing product systems and primary TM and NS systems, transformed them and then loaded them to our platform. This used to be a process that requires considerable effort and time, however, Tookitaki solution’s pre-packed connectors made it easier for us to adapt to the bank’s various enterprise architectures and up-stream systems.
For TM execution, we integrated historical data for three years (customer, accounts, transactions, primary system alerts, etc.) in the learning phase. In NS, which is used to identify individuals and entities that are involved in AML activities, our advanced name matching algorithms compared individual names and business names with the bank’s internal and external watch lists. Our solution could effectively handle multiple attributes such as typos, transliteration limitations, cultural differences for accurate hits detection.
After validating the accuracy and stability of the training models, we moved to the execution mode where we integrated additional data from source systems. The final models used in TM and NS processes helped execute alert prioritization accurately and investigate alerts in a faster manner. AMLS consolidated all source data to provide a holistic view of customers, accounts and transactions and brought in enhanced network analysis and intelligent cluster analysis to aid investigative functions across various business units within the bank.
The business interface of AMLS provides easy-to-use and highly customizable dashboards for both TM and NS processes, enabling efficient work allocation, exploratory analysis, link analysis, prediction interpretation and management reporting.
The following are the quantitative business benefits we received from the project.
- Name Screening: 70% reduction in false positives for individual names and 60% reduction in false positives for corporate names.
- Transaction Monitoring: 50% reduction in false positives with less than 1% misclassification, 5% increase in true positives (file-able SARs) and an overall true positive prediction rate of 96% in the high-priority category.
Other benefits we achieved are:
- Increased effectiveness in identifying suspicious activities
- A sharper focus on data anomalies rather than depending on threshold triggering
- Easier customisation of data features to target specific risks accurately
- Ability to enable longer look-back periods to detect complex scenarios
Protecting against model biases, our platform’s Champion-Challenger module automatically and continuously incorporates data shifts and data additions and informs users of the availability of any ‘Challenger’ model. Users may validate the vitals of the newly created ‘Challenger’ and replace it with the existing ‘Champion’ effortlessly. This unique feature helps financial institutions avoid time-consuming and costly model upgrades, ensuring faster ROI realization and sustained and effective performance of AML compliance programs.
The deployment of AMLS at UOB with stellar results marks the end of the AI experimentation phase in AML compliance. It is another example of how Tookitaki, as a fast-growing AI startup, sets new standards for the regulatory compliance industry’s fight against money laundering. Our success is noteworthy given that many enterprise AI projects are dying within laboratories. AMLS went through multiple rounds of testing and validation and our machine learning models have been proven to provide stable results and remain agile to the cause in dynamic situations. At the same time, it could effectively explain the decision-making process of machine learning models in a comprehensive yet simple manner through our patent-pending Explainable AI framework. Through this project, we also validated that our AI processes are effective, efficient and set to be applied in a responsible and ethical manner.
A complete revamp of existing AML compliance processes is imperative for financial institutions, given that money laundering strategies are becoming more and more sophisticated. It is time to embrace modern-era intelligent technology to enhance efficiency and effectiveness in AML compliance programs, establish next-gen financial crime surveillance and ensure robust risk management practices.
For more details about our partnerships with UOB and many other big banks across the globe, please contact us.
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Australia’s AML Overhaul: What AUSTRAC’s New Rules Mean for Compliance Teams
AUSTRAC’s latest draft rules signal a defining moment for AML compliance in Australia.
With growing pressure to address regulatory gaps and align with global standards, AUSTRAC has released a second exposure draft of AML/CTF rules that could reshape how financial institutions approach compliance. These proposed updates are more than routine tweaks, they are part of a strategic pivot aimed at strengthening Australia’s financial crime defences following international scrutiny and domestic lapses.
Background: Why AUSTRAC Is Updating the Rules
AUSTRAC’s policy overhaul comes at a critical time for the Australian financial sector. After years of industry feedback, regulatory incidents, and repeated warnings from the Financial Action Task Force (FATF), Australia has faced growing pressure to modernise its AML/CTF framework. This pressure intensified after the Royal Commission findings and the high-profile Crown Resorts case, which exposed systemic failures in detecting and reporting suspicious transactions.
The second exposure draft released in July 2025 reflects AUSTRAC’s intent to close key compliance loopholes and bring the current system in line with global best practices. It expands on the earlier draft by incorporating industry consultation and focuses on more granular obligations for customer due diligence, ongoing monitoring, and sanctions screening. These changes aim to strengthen Australia’s position in the face of a rapidly evolving threat landscape driven by digital finance, cross-border transactions, and sophisticated laundering techniques.
What’s Changing: Key Highlights from the Exposure Draft Rules
The second exposure draft introduces several new requirements that directly impact how reporting entities manage risk and monitor customers:
1. Clarified PEP Obligations
The draft now defines a broader set of politically exposed persons (PEPs), including foreign and domestic roles, and mandates enhanced due diligence regardless of source of funds.
2. Expanded Ongoing Monitoring
Entities must now monitor customers continuously, not just at onboarding, using both transaction and behavioural data. This shift pushes compliance teams to move from static checks to dynamic, risk-based reviews.
3. Third-Party Reliance Rules
The draft clarifies when and how financial institutions can rely on third parties for KYC processes. This includes more specific provisions for responsibility and liability in case of failure.
4. Sanctions Screening Expectations
AUSTRAC has proposed more stringent guidelines for sanctions screening, especially around name-matching and periodic list updates. There is also an increased focus on ultimate beneficial ownership.
5. Obligations for Fintechs and Digital Wallet Providers
The draft recognises the role of digital services and imposes tighter onboarding and monitoring standards for high-risk products and cross-border offerings.
Comparing ED2 with Tranche 2 Reforms
While Tranche 2 reforms remain on the horizon with a broader mandate to include lawyers, accountants, and real estate agents under the AML/CTF regime, the second exposure draft zeroes in on tightening the compliance expectations for existing reporting entities.
Unlike Tranche 2, which aims to expand the scope of regulated professions, the exposure draft rules focus on strengthening operational practices such as ongoing monitoring, customer segmentation, and enhanced due diligence for existing covered sectors. The rules also go deeper into technological expectations, such as maintaining audit trails and validating third-party service providers.
In short, ED2 is more about modernising the how of AML compliance, whereas Tranche 2 will eventually reshape the who of the regulated ecosystem.
Why It Matters for Financial Institutions
For compliance officers and risk managers, these proposed changes translate to increased scrutiny, more granular documentation, and an urgent need to improve monitoring practices. Institutions will be expected to maintain stronger evidence trails, adopt real-time monitoring tools, and improve their ability to detect behavioural anomalies across customer life cycles.
Moreover, the clear emphasis on risk-based ongoing due diligence means firms can no longer rely on periodic checks alone. Dynamic updates to risk profiles, responsive escalation triggers, and cross-channel data analysis will become critical components of future-ready compliance programs.
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Tookitaki’s Perspective and Solution Fit
At Tookitaki, we believe AUSTRAC’s second exposure draft offers an opportunity for Australian institutions to build more resilient, intelligence-driven compliance programs.
Our flagship platform, FinCense, is built to adapt to evolving AML obligations through its scenario-driven detection engine, AI-led transaction monitoring, and federated learning capabilities. Financial institutions can seamlessly adopt continuous risk monitoring, generate audit-ready investigation trails, and integrate sanctions screening workflows, all while maintaining high levels of precision.
Importantly, Tookitaki’s federated intelligence model draws from a community of AML experts to anticipate emerging threats and codify new typologies. This ensures institutions stay ahead of bad actors who are constantly evolving their methods.
What’s Next: Preparing for the New Rules
AUSTRAC is expected to finalise the rules following this round of industry consultation, with phased implementation timelines to be announced. Financial institutions should begin by assessing gaps in their existing AML controls, especially around ongoing monitoring, PEP screening, and documentation processes.
This is also a good time to evaluate technology infrastructure. Solutions that enable scalable monitoring, natural language audit logs, and flexible rule design will give institutions a distinct advantage in meeting the new compliance bar.
Conclusion
AUSTRAC’s second exposure draft marks a pivotal shift from checkbox compliance to intelligent, risk-driven AML practices. For financial institutions, the future of compliance lies in adopting flexible, technology-powered solutions that can evolve with the regulatory landscape.
The message is clear, compliance is no longer a static requirement. It is a dynamic, strategic pillar that demands agility, insight, and collaboration.
Agentic AI Is Here: The Future of Financial Crime Compliance Is Smarter, Safer, and Audit-Ready
The financial crime compliance landscape is evolving rapidly, and so are the tools required to keep up.
As criminal tactics become more sophisticated and regulatory expectations more demanding, compliance teams need AI systems that do more than detect anomalies. They must explain their decisions, prove their accuracy, and demonstrate responsible governance at every step.
At Tookitaki, we are building an Agentic Framework - a network of intelligent agents which are auditable and explainable for each action they take. These agents don’t just make recommendations - they work across the entire compliance lifecycle, supporting real-time detection, guiding investigations, and reinforcing regulatory alignment.
This blog introduces Tookitaki’s agentic approach, grounded in collaborative intelligence and designed to help financial institutions take control, not just of detection accuracy, but of trust.
The Compliance Challenge: Accuracy Isn’t Enough
Traditional AI systems are built to optimise performance. But in regulated environments, performance is only half the story.
Regulators now expect AI systems to be:
- Fully explainable and traceable
- Free from hidden biases
- Secure by design
- Governed with clear human oversight
Frameworks like the Federal Reserve’s SR 11-7, MAS TRM, and GDPR are clear: If a system impacts a regulated decision, whether it’s flagging suspicious transactions, filing reports, or escalating investigations, then institutions must be able to validate, explain, and defend those outcomes.
This is where most AI platforms struggle.
Tookitaki’s Answer: A Trust Layer Powered by Agentic AI
Tookitaki’s platform is built to meet these challenges head-on. It combines two powerful engines:
- The AFC Ecosystem: A global community of financial crime experts who contribute real-world scenarios forming the industry’s most robust collaborative intelligence network.
- FinCense: Our end-to-end compliance platform, which integrates these scenarios into dynamic workflows powered by AI agents, all aligned with regulatory best practices.
Together, these components form Tookitaki’s Trust Layer for Financial Services — enabling financial institutions to reduce risk, improve compliance operations, and increase confidence across every investigation.
Built on Collaborative Intelligence, Tested in Your Environment
At the heart of Tookitaki’s approach is the AFC Ecosystem, a global community of compliance experts who contribute a growing library of real-world typologies spanning dozens of financial crime risk categories. These are not hypothetical constructs. They are tested, peer-reviewed patterns that reflect how financial crime plays out in practice from money mule networks to account take over and social engineering.
Instead of relying on static rules or black-box models, financial institutions using Tookitaki gain access to this dynamic intelligence. And before anything is deployed, scenarios can be tested against the institution’s own historical data using our Simulation Agent, giving teams complete control, visibility, and confidence in performance.
AI Agents That Power Compliance Intelligence
Tookitaki’s Agentic AI framework is built on specialised agents, each designed to improve efficiency, accuracy, and explainability across the investigation lifecycle:
- Simulation Agent: Tests new detection scenarios against historical data, helping teams fine-tune thresholds and understand performance before going live.
- Alert Prioritization Agent: Ranks alerts by risk relevance using a regulatory-weighted model, reducing false positives and enabling faster triage with over 94% alignment to expert decisions.
- Smart Disposition Agent: It’s an agent that lets compliance teams codify their Standard Operating Procedures (SOPs) as advanced rules — so that eligible alerts are automatically closed without human intervention.
- Smart Narration Agent: An agent powered by large language models that auto-generates a natural language narrative for each alert.
- FinMate (Investigation Copilot): Assists investigators with case context, risk indicators, and typology insights, improving evidence collection and reducing handling time by over 60%.
These agents operate within Tookitaki’s compliance-native orchestration layer — ensuring every action is explainable, governed, and aligned with regulatory frameworks.
Setting a Benchmark in AI Governance
Tookitaki is proud to be the first RegTech company validated under Singapore’s national AI Verify programme, establishing a new standard for auditable, explainable, and responsible AI in compliance.
Our Agentic AI framework, specifically its AI-powered narration capabilities, underwent rigorous independent validation, which included:
- Accuracy testing across 400+ real-world AML scenarios
- Multi-language validation in complex cases involving English and Mandarin
- Zero tolerance for hallucinations, with protocols ensuring all outputs are grounded in verifiable data
- Compliance assurance, proving the system adheres to financial regulations and prevents misuse
This milestone reinforces Tookitaki’s position as a RegTech innovator that blends AI performance with governance - by incorporating guardrails to prevent AI hallucinations, ensuring that every narrative generated is accurate, auditable, and actionable - a critical requirement for financial institutions operating under increasing regulatory scrutiny.
A New Standard for AI in Compliance
Agentic AI is not about replacing human investigators — it’s about equipping them with the intelligence, speed, and context they need to work smarter.
By combining collaborative intelligence-driven detection, real-time simulation, and agentic automation, Tookitaki offers a future-ready model for the entire reg-tech lifecycle - one that’s grounded in transparency, is auditable and capable of learning with every new pattern, case, and risk.
In a world where compliance is no longer just about rules, but about resilience and trust, Tookitaki’s Agentic AI is setting a new standard.
What’s Next in This Blog Series
In the upcoming blogs, we’ll dive deeper into Tookitaki’s flagship AI agents — exploring how each one is designed, validated, and deployed in production environments to deliver compliance-grade performance.
Stay tuned.
Thailand’s AML/CFT Wake-Up Call: What Banks and Fintechs Must Prepare for in 2025
Thailand’s financial system is entering a defining era for anti-money laundering and counter-terrorism financing.
As the country deepens its regional trade and digital finance ambitions, it also faces mounting pressure to confront evolving financial crime threats, ranging from cross-border laundering to high-velocity scams and informal value transfers. With the FATF eyeing gaps in oversight and regulators tightening expectations, AML/CFT compliance is no longer just a back-office responsibility. It's a front-line defence for trust and competitiveness.
In this blog, we break down the current AML/CFT regulatory framework in Thailand, highlight key risks and real-world threats, explore upcoming reform pressures, and share how banks and fintechs can strengthen their compliance strategy through both innovation and collaboration.
The Regulatory Landscape in Thailand
Thailand’s AML/CFT framework is governed by the Anti-Money Laundering Office (AMLO), established in 1999. AMLO functions as both the financial intelligence unit (FIU) and the key enforcement agency overseeing compliance and investigations related to illicit finance.
The two core laws forming the backbone of the regulatory regime are:
- The Anti-Money Laundering Act (AMLA), B.E. 2542 (1999)
- The Counter-Terrorism and Proliferation of Weapons of Mass Destruction Financing Act, B.E. 2559 (2016)
Entities subject to AML/CTF obligations include:
- Commercial banks and financial institutions
- Money service businesses (MSBs), e-wallets, and fintech platforms
- Securities companies and insurance providers
- Real estate developers and dealers in precious stones/metals
- Legal professionals and notaries (in limited contexts)
Reporting entities must:
- Conduct customer due diligence (CDD) and enhanced due diligence (EDD)
- File suspicious transaction reports (STRs) and cash transaction reports (CTRs) with AMLO
- Retain records for a minimum of 5 years
- Establish internal AML programs, risk assessments, and staff training
FATF and Grey List Pressures
Thailand has had a complicated relationship with the Financial Action Task Force (FATF). After being grey-listed in 2011 due to strategic deficiencies in its AML regime, it made significant reforms and was removed in 2015.
However, FATF’s most recent mutual evaluation pointed to new challenges:
- Limited oversight of certain non-financial sectors
- Gaps in beneficial ownership transparency
- Uneven application of risk-based approaches
- Under-reporting of suspicious transactions by fintech and digital players
Why it matters: FATF grey-listing carries serious consequences. It can deter foreign investment, slow correspondent banking relationships, and increase the cost of doing business internationally. For Thai banks and fintechs, staying aligned with FATF expectations is not just about compliance—it’s about global competitiveness.
Real-World Threats: What’s Fueling Financial Crime in Thailand
Thailand’s economy, geographic location, and strong informal networks make it vulnerable to a wide range of predicate offences. Some of the most prominent financial crime threats include:
🔹 Drug Trafficking and Organised Crime
Transnational criminal groups exploit Thailand’s location in the Mekong subregion to launder drug proceeds through shell companies, property purchases, and trade channels.
🔹 Public Sector Corruption and Tax Crimes
Illicit enrichment and VAT fraud are common predicate offences, with funds often laundered via nominee accounts and luxury assets.
🔹 Cross-Border Laundering
Money mules, informal remittance systems, and trade-based money laundering (TBML) remain significant threats. Syndicates frequently layer funds through multiple jurisdictions.
🔹 Investment and Romance Scams
Thailand is increasingly being used as both a staging ground and a destination for proceeds from international fraud, including pig butchering scams and tech support frauds targeting foreign victims.
AMLO has flagged the rising use of e-wallets, digital platforms, and cash-intensive businesses as high-risk vehicles for laundering.
Challenges for Banks and Fintechs
Despite progress, many institutions face real hurdles when it comes to AML/CFT execution.
- Legacy Systems and Manual Workflows
Traditional rule-based systems often generate high false positives and miss nuanced patterns, especially in real-time transactions. - Fragmented Intelligence
Limited cross-institutional data sharing weakens the detection of syndicated risks, such as mule networks operating across multiple banks. - High Compliance Costs
Smaller fintechs and non-bank financial institutions struggle to meet regulatory requirements without draining operational resources. - Speed vs Safety in Payments
Instant payment rails (e.g., PromptPay) have made fund movement frictionless, but also difficult to trace once fraud or laundering occurs.
Thailand’s Push Toward RegTech and AI
Recognising these challenges, regulators and industry players are increasingly turning to RegTech to strengthen compliance without stifling innovation.
Notable trends:
- AI-driven transaction monitoring is gaining traction for detecting suspicious behaviour across vast datasets in real time.
- Automated screening tools are being used to process watchlists, sanctions, and politically exposed person (PEP) data more efficiently.
- Digital KYC and eKYB (Know Your Business) solutions are helping fintechs onboard customers with less friction and more accuracy.
AMLO itself has been vocal about the importance of technology in enhancing reporting accuracy and enabling real-time intelligence. Collaboration between regulators and the private sector on typology sharing and case-based learning is also gaining momentum.
How Tookitaki Supports Smarter Compliance in Thailand
Tookitaki’s FinCense platform is well-positioned to help Thai banks and fintechs overcome both regulatory and operational AML/CFT challenges.
Here’s how:
🔹 Scenario-Based Detection
FinCense leverages typologies contributed by global experts through the AFC Ecosystem. These include real-world cases such as QR-code laundering, mule account recruitment, and shell invoicing many of which mirror red flags identified by AMLO.
🔹 Smart Screening
Advanced screening tools that support multi-lingual names, alias logic, and national ID handling—critical in jurisdictions like Thailand.
🔹 AI-Powered Risk Scoring
Dynamic customer risk scoring and automated threshold tuning reduce false positives and allow institutions to focus on the most relevant alerts.
🔹 FinMate: AI Copilot for Compliance Teams
FinMate assists investigators by summarising alerts, surfacing behavioural insights, and recommending next steps, reducing the average case investigation time.
Whether you're dealing with fraud from romance scams or laundering via e-wallet networks, FinCense offers a flexible, modular approach that’s ready for Thailand’s fast-evolving risk environment.
Key Takeaways for Compliance Teams
✅ Thailand’s AML/CFT ecosystem is evolving, but financial crime threats are getting more sophisticated.
✅ FATF scrutiny and regulatory reform will intensify over the next 12–18 months.
✅ Manual systems are no longer sustainable—technology is a must-have.
✅ Cross-border risk requires cross-sector intelligence—collaboration is key.
✅ Institutions that prioritise adaptive compliance now will gain a strategic edge in the future.
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Conclusion: Thailand’s Next Chapter in AML/CFT Compliance
Thailand has made significant progress in building its AML/CFT regime, but the fight is far from over. As financial crime networks grow more organised and tech-savvy, regulators and institutions must respond in kind—with smarter systems, stronger collaboration, and a proactive mindset.
The future of compliance in Thailand isn’t just about ticking regulatory boxes. It’s about building trust, resilience, and readiness—not just for the next audit, but for the next threat.
Australia’s AML Overhaul: What AUSTRAC’s New Rules Mean for Compliance Teams
AUSTRAC’s latest draft rules signal a defining moment for AML compliance in Australia.
With growing pressure to address regulatory gaps and align with global standards, AUSTRAC has released a second exposure draft of AML/CTF rules that could reshape how financial institutions approach compliance. These proposed updates are more than routine tweaks, they are part of a strategic pivot aimed at strengthening Australia’s financial crime defences following international scrutiny and domestic lapses.
Background: Why AUSTRAC Is Updating the Rules
AUSTRAC’s policy overhaul comes at a critical time for the Australian financial sector. After years of industry feedback, regulatory incidents, and repeated warnings from the Financial Action Task Force (FATF), Australia has faced growing pressure to modernise its AML/CTF framework. This pressure intensified after the Royal Commission findings and the high-profile Crown Resorts case, which exposed systemic failures in detecting and reporting suspicious transactions.
The second exposure draft released in July 2025 reflects AUSTRAC’s intent to close key compliance loopholes and bring the current system in line with global best practices. It expands on the earlier draft by incorporating industry consultation and focuses on more granular obligations for customer due diligence, ongoing monitoring, and sanctions screening. These changes aim to strengthen Australia’s position in the face of a rapidly evolving threat landscape driven by digital finance, cross-border transactions, and sophisticated laundering techniques.
What’s Changing: Key Highlights from the Exposure Draft Rules
The second exposure draft introduces several new requirements that directly impact how reporting entities manage risk and monitor customers:
1. Clarified PEP Obligations
The draft now defines a broader set of politically exposed persons (PEPs), including foreign and domestic roles, and mandates enhanced due diligence regardless of source of funds.
2. Expanded Ongoing Monitoring
Entities must now monitor customers continuously, not just at onboarding, using both transaction and behavioural data. This shift pushes compliance teams to move from static checks to dynamic, risk-based reviews.
3. Third-Party Reliance Rules
The draft clarifies when and how financial institutions can rely on third parties for KYC processes. This includes more specific provisions for responsibility and liability in case of failure.
4. Sanctions Screening Expectations
AUSTRAC has proposed more stringent guidelines for sanctions screening, especially around name-matching and periodic list updates. There is also an increased focus on ultimate beneficial ownership.
5. Obligations for Fintechs and Digital Wallet Providers
The draft recognises the role of digital services and imposes tighter onboarding and monitoring standards for high-risk products and cross-border offerings.
Comparing ED2 with Tranche 2 Reforms
While Tranche 2 reforms remain on the horizon with a broader mandate to include lawyers, accountants, and real estate agents under the AML/CTF regime, the second exposure draft zeroes in on tightening the compliance expectations for existing reporting entities.
Unlike Tranche 2, which aims to expand the scope of regulated professions, the exposure draft rules focus on strengthening operational practices such as ongoing monitoring, customer segmentation, and enhanced due diligence for existing covered sectors. The rules also go deeper into technological expectations, such as maintaining audit trails and validating third-party service providers.
In short, ED2 is more about modernising the how of AML compliance, whereas Tranche 2 will eventually reshape the who of the regulated ecosystem.
Why It Matters for Financial Institutions
For compliance officers and risk managers, these proposed changes translate to increased scrutiny, more granular documentation, and an urgent need to improve monitoring practices. Institutions will be expected to maintain stronger evidence trails, adopt real-time monitoring tools, and improve their ability to detect behavioural anomalies across customer life cycles.
Moreover, the clear emphasis on risk-based ongoing due diligence means firms can no longer rely on periodic checks alone. Dynamic updates to risk profiles, responsive escalation triggers, and cross-channel data analysis will become critical components of future-ready compliance programs.
{{cta-first}}
Tookitaki’s Perspective and Solution Fit
At Tookitaki, we believe AUSTRAC’s second exposure draft offers an opportunity for Australian institutions to build more resilient, intelligence-driven compliance programs.
Our flagship platform, FinCense, is built to adapt to evolving AML obligations through its scenario-driven detection engine, AI-led transaction monitoring, and federated learning capabilities. Financial institutions can seamlessly adopt continuous risk monitoring, generate audit-ready investigation trails, and integrate sanctions screening workflows, all while maintaining high levels of precision.
Importantly, Tookitaki’s federated intelligence model draws from a community of AML experts to anticipate emerging threats and codify new typologies. This ensures institutions stay ahead of bad actors who are constantly evolving their methods.
What’s Next: Preparing for the New Rules
AUSTRAC is expected to finalise the rules following this round of industry consultation, with phased implementation timelines to be announced. Financial institutions should begin by assessing gaps in their existing AML controls, especially around ongoing monitoring, PEP screening, and documentation processes.
This is also a good time to evaluate technology infrastructure. Solutions that enable scalable monitoring, natural language audit logs, and flexible rule design will give institutions a distinct advantage in meeting the new compliance bar.
Conclusion
AUSTRAC’s second exposure draft marks a pivotal shift from checkbox compliance to intelligent, risk-driven AML practices. For financial institutions, the future of compliance lies in adopting flexible, technology-powered solutions that can evolve with the regulatory landscape.
The message is clear, compliance is no longer a static requirement. It is a dynamic, strategic pillar that demands agility, insight, and collaboration.
Agentic AI Is Here: The Future of Financial Crime Compliance Is Smarter, Safer, and Audit-Ready
The financial crime compliance landscape is evolving rapidly, and so are the tools required to keep up.
As criminal tactics become more sophisticated and regulatory expectations more demanding, compliance teams need AI systems that do more than detect anomalies. They must explain their decisions, prove their accuracy, and demonstrate responsible governance at every step.
At Tookitaki, we are building an Agentic Framework - a network of intelligent agents which are auditable and explainable for each action they take. These agents don’t just make recommendations - they work across the entire compliance lifecycle, supporting real-time detection, guiding investigations, and reinforcing regulatory alignment.
This blog introduces Tookitaki’s agentic approach, grounded in collaborative intelligence and designed to help financial institutions take control, not just of detection accuracy, but of trust.
The Compliance Challenge: Accuracy Isn’t Enough
Traditional AI systems are built to optimise performance. But in regulated environments, performance is only half the story.
Regulators now expect AI systems to be:
- Fully explainable and traceable
- Free from hidden biases
- Secure by design
- Governed with clear human oversight
Frameworks like the Federal Reserve’s SR 11-7, MAS TRM, and GDPR are clear: If a system impacts a regulated decision, whether it’s flagging suspicious transactions, filing reports, or escalating investigations, then institutions must be able to validate, explain, and defend those outcomes.
This is where most AI platforms struggle.
Tookitaki’s Answer: A Trust Layer Powered by Agentic AI
Tookitaki’s platform is built to meet these challenges head-on. It combines two powerful engines:
- The AFC Ecosystem: A global community of financial crime experts who contribute real-world scenarios forming the industry’s most robust collaborative intelligence network.
- FinCense: Our end-to-end compliance platform, which integrates these scenarios into dynamic workflows powered by AI agents, all aligned with regulatory best practices.
Together, these components form Tookitaki’s Trust Layer for Financial Services — enabling financial institutions to reduce risk, improve compliance operations, and increase confidence across every investigation.
Built on Collaborative Intelligence, Tested in Your Environment
At the heart of Tookitaki’s approach is the AFC Ecosystem, a global community of compliance experts who contribute a growing library of real-world typologies spanning dozens of financial crime risk categories. These are not hypothetical constructs. They are tested, peer-reviewed patterns that reflect how financial crime plays out in practice from money mule networks to account take over and social engineering.
Instead of relying on static rules or black-box models, financial institutions using Tookitaki gain access to this dynamic intelligence. And before anything is deployed, scenarios can be tested against the institution’s own historical data using our Simulation Agent, giving teams complete control, visibility, and confidence in performance.
AI Agents That Power Compliance Intelligence
Tookitaki’s Agentic AI framework is built on specialised agents, each designed to improve efficiency, accuracy, and explainability across the investigation lifecycle:
- Simulation Agent: Tests new detection scenarios against historical data, helping teams fine-tune thresholds and understand performance before going live.
- Alert Prioritization Agent: Ranks alerts by risk relevance using a regulatory-weighted model, reducing false positives and enabling faster triage with over 94% alignment to expert decisions.
- Smart Disposition Agent: It’s an agent that lets compliance teams codify their Standard Operating Procedures (SOPs) as advanced rules — so that eligible alerts are automatically closed without human intervention.
- Smart Narration Agent: An agent powered by large language models that auto-generates a natural language narrative for each alert.
- FinMate (Investigation Copilot): Assists investigators with case context, risk indicators, and typology insights, improving evidence collection and reducing handling time by over 60%.
These agents operate within Tookitaki’s compliance-native orchestration layer — ensuring every action is explainable, governed, and aligned with regulatory frameworks.
Setting a Benchmark in AI Governance
Tookitaki is proud to be the first RegTech company validated under Singapore’s national AI Verify programme, establishing a new standard for auditable, explainable, and responsible AI in compliance.
Our Agentic AI framework, specifically its AI-powered narration capabilities, underwent rigorous independent validation, which included:
- Accuracy testing across 400+ real-world AML scenarios
- Multi-language validation in complex cases involving English and Mandarin
- Zero tolerance for hallucinations, with protocols ensuring all outputs are grounded in verifiable data
- Compliance assurance, proving the system adheres to financial regulations and prevents misuse
This milestone reinforces Tookitaki’s position as a RegTech innovator that blends AI performance with governance - by incorporating guardrails to prevent AI hallucinations, ensuring that every narrative generated is accurate, auditable, and actionable - a critical requirement for financial institutions operating under increasing regulatory scrutiny.
A New Standard for AI in Compliance
Agentic AI is not about replacing human investigators — it’s about equipping them with the intelligence, speed, and context they need to work smarter.
By combining collaborative intelligence-driven detection, real-time simulation, and agentic automation, Tookitaki offers a future-ready model for the entire reg-tech lifecycle - one that’s grounded in transparency, is auditable and capable of learning with every new pattern, case, and risk.
In a world where compliance is no longer just about rules, but about resilience and trust, Tookitaki’s Agentic AI is setting a new standard.
What’s Next in This Blog Series
In the upcoming blogs, we’ll dive deeper into Tookitaki’s flagship AI agents — exploring how each one is designed, validated, and deployed in production environments to deliver compliance-grade performance.
Stay tuned.
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.
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:
- Detect faster
- Collaborate smarter
- Reduce false positives
- Stay regulator-ready
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.
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.
