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The Evolution of AML in Software: A Look at the Philippine Market

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Tookitaki
9 min
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The financial world is a complex ecosystem, intrinsically interwoven with the world's economy and the livelihoods of countless individuals and businesses. One crucial aspect of this ecosystem, particularly within the financial markets of countries such as the Philippines, is the battle against money laundering. Anti-money laundering (AML) measures have become a critical focus for financial institutions worldwide, and the Philippine market is no exception.

As the Philippines continues to embrace the digital age, the importance of AML in its burgeoning financial sector cannot be overstated. The country's financial landscape has experienced a rapid transformation spurred by technological advancements and the increased integration of digital platforms into everyday banking and financial services. This digital transformation has brought with it an increased risk of financial crime, making AML measures even more crucial.

In this context, the software is playing an increasingly significant role in AML processes. AML software serves as a powerful tool that empowers financial institutions to effectively detect, prevent, and combat money laundering and related criminal activities. When used effectively, these systems can provide comprehensive risk coverage, enhance detection accuracy, and significantly reduce false alerts.

In this blog, we'll explore the evolution of AML in software, with a specific focus on the Philippine market. We'll delve into how these software solutions have transformed over time, adapting to the needs of an ever-changing financial landscape and the role they play in the ongoing fight against financial crime.

The Early Stages of AML in the Philippines

The journey of anti-money laundering (AML) in the Philippines has been fascinating, marked by challenges and milestones. The initial steps towards a comprehensive AML framework began in earnest with the establishment of the Anti-Money Laundering Act (AMLA) in 2001. This legislation was a significant turning point, marking the country's formal commitment to combating money laundering.

In the early stages, implementing AML measures posed numerous challenges. The primary issue was the lack of awareness and understanding of money laundering practices and a dearth of technical expertise and resources. Compliance was viewed more as a regulatory obligation than a strategic imperative, leading to a limited commitment to robust AML controls.

Traditional methods of AML during this period relied heavily on manual processes. Financial institutions implemented transaction monitoring systems that flagged transactions based on predefined rules. These systems, while a necessary first step, were far from perfect. The rules-based approach was often overly simplistic, leading to a high number of false positives. This inundated compliance teams with alerts, many of which were not indicative of actual suspicious activity.

Moreover, these traditional methods had limitations in detecting complex money laundering schemes. Sophisticated launderers could often circumvent these rules-based systems by structuring their transactions to avoid triggering the set thresholds. This, coupled with the rapid advancement of technology and the growing sophistication of money launderers, highlighted the need for more advanced, dynamic, and intelligent AML solutions.

The Advent of Software in AML

As the limitations of traditional AML methods became increasingly apparent, the financial sector began to explore more innovative solutions. The advent of software in AML marked a significant shift in how financial institutions approached money laundering and related financial crimes.

The Emergence of Software Solutions in AML

In response to the escalating complexity of financial crime, anti-money laundering tracking systems began to emerge as a critical tool in the fight against money laundering. These solutions offered a way to automate processes, improve detection capabilities, and reduce the burden of false alerts on compliance teams. Key developments included:

  • Automated Monitoring: Software allowed for the automation of transaction monitoring, drastically reducing the workload of compliance teams and allowing for more transactions to be monitored.
  • Risk Scoring: Advanced software solutions introduced the concept of risk scoring, a way to rank customers based on their likelihood of engaging in illicit activities.
  • Machine Learning and AI: With the advent of more sophisticated technology, software solutions began to incorporate machine learning and artificial intelligence. These technologies enabled more dynamic and adaptive detection methods, improving the accuracy of AML efforts.

The Current State of AML Software in the Philippines

We find a dynamic and robust landscape as we delve into the present state of Anti-Money Laundering (AML) software in the Philippines. The integration of software in AML processes has now become a norm, marking a significant departure from the traditional methods that once dominated the sector.

Key Players and Their Contributions

Several key players have emerged in this evolving landscape, each contributing to the strengthening of AML measures in their unique way. While it would be imprudent to name specific companies in this discussion, we can safely say that these firms range from established global software vendors offering comprehensive AML solutions to innovative startups leveraging cutting-edge technology to disrupt the status quo. Their offerings typically include transaction monitoring systems, customer risk scoring, and automated reporting tools, among other features.

One notable trend among these providers is the use of machine learning and artificial intelligence in their solutions. These technologies enhance the detection capabilities of AML software, allowing it to learn from past data, adapt to new patterns, and identify suspicious activities with greater accuracy and efficiency.

Improvements and Advancements Brought About by AML Software

The advancements brought about by AML software in the Philippines are manifold. The ability to automate routine tasks has reduced the workload of compliance teams and minimized human error. This has resulted in more accurate detection and reporting of suspicious activities, ultimately leading to more effective prevention and mitigation of money laundering.

Furthermore, advanced technologies like machine learning and AI have greatly improved AML efforts' precision. These technologies have given software the ability to recognize complex patterns and anomalies, enabling it to detect sophisticated money laundering schemes that may have gone unnoticed by traditional methods.

The integration of software into AML processes has also improved the scalability of these efforts. With software, financial institutions can monitor a significantly larger volume of transactions in real time, a feat that would be impossible with manual methods.

In essence, the advent of AML software has revolutionized the Philippine financial sector's approach to money laundering. As we look to the future, we can only expect this trend to continue, as more advanced technologies come into play and the fight against financial crime becomes increasingly digital.

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The Role of AML Software in Overcoming Challenges

While the advent of software in AML has certainly made strides in the Philippine financial landscape, it is not without its own unique set of challenges. From dealing with a complex regulatory landscape to navigating the multifaceted aspects of customer transactions, the Philippine AML landscape presents its own share of obstacles. Fortunately, advancements in AML software technology have proven effective in addressing many of these challenges.

Leveraging AI for Enhanced Detection

One of the fundamental challenges in AML efforts is the detection of suspicious transactions. While effective to a degree, traditional rules-based systems often fall short in terms of efficiency and accuracy. However, the application of AI and machine learning (ML) in AML software has led to significant improvements in detection efforts.

Automating Processes for Cost Efficiency

Another challenge in AML efforts is the cost and time associated with manual customer onboarding processes. By leveraging robotic process automation, AML software has significantly reduced the time and cost associated with these processes.

Modernization and Digital Transformation

Finally, one of the more complex challenges in the Philippine AML landscape is the modernization of legacy systems and infrastructures. AML software solutions have played a pivotal role in facilitating digital transformation and modernization efforts in financial institutions. 

These examples underscore AML software's critical role in overcoming challenges in the Philippine AML landscape. Through leveraging AI for enhanced detection, automating processes for cost efficiency, and facilitating digital transformation, AML software is proving to be a game changer in the battle against money laundering.

Tookitaki's Role in the Evolution of AML in the Philippines

In the landscape of AML compliance, one name that is making significant strides in the Philippine market is Tookitaki. With its innovative solutions and groundbreaking approach to AML, Tookitaki is contributing notably to the country's evolution and modernization of AML processes.

Transforming Compliance with the AMLS

At the heart of Tookitaki's contributions is its pioneering Anti-Money Laundering Suite (AMLS). AMLS is an end-to-end operating system designed to modernize the compliance processes for banks and fintech businesses. It offers comprehensive risk coverage, enhanced detection accuracy, and a significant reduction in false alerts, all of which are crucial in effective AML efforts.

The AMLS consists of several key modules, each designed to address a specific aspect of AML compliance:

  • Smart Screening Solutions: They include Prospect Screening, Name Screening, and Transaction Screening. These tools enable financial institutions to screen names against various watchlists, sanction lists and adverse media with superior accuracy.
  • Dynamic Risk Scoring: This involves Prospect Risk Scoring and Customer Risk Scoring. These mechanisms provide a dynamic assessment of risk associated with prospects and customers, allowing for proactive risk management.
  • Transaction Monitoring: This module monitors transactions for any suspicious activities, providing another layer of protection against potential money laundering.
  • Case Manager: This tool facilitates the management and tracking of cases, enhancing the efficiency and effectiveness of investigations.

The modular design of the AMLS allows for flexibility and customization to suit the specific needs of each institution, further enhancing its relevance and utility in the Philippine financial sector.

AMLS modules

The Impact of Tookitaki's Solutions

Tookitaki's AMLS has brought about significant improvements and advancements in the Philippine AML landscape. Tookitaki provides a level of detection accuracy and efficiency that surpasses traditional methods. This, combined with its comprehensive coverage and reduced false alerts, contributes to a sustainable AML program.

Moreover, Tookitaki is not just about providing innovative technology; it also fosters a community of experts through its Anti-Financial Crime (AFC) Ecosystem. This unique approach ensures that financial institutions stay ahead of the curve in their AML programs and promotes a collaborative and united effort in the battle against financial crime.

The Future of AML Software in the Philippines

As we look ahead, the future of Anti-Money Laundering (AML) software in the Philippines is both exciting and promising. Emerging trends and technologies are poised to bring about significant advancements that could revolutionize AML processes and systems. Let's take a closer look at what's on the horizon and how these developments could impact the Philippine market.

  • AI and ML are not new in the realm of AML software, but their applications continue to evolve and expand. Advanced AI algorithms and ML models are being developed to improve detection accuracy, reduce false positives, and further streamline compliance processes. As these technologies become more sophisticated, we can expect even more efficient and effective AML systems.
  • Another promising development is the application of blockchain technology in AML processes. Blockchain's decentralized and transparent nature can help reduce the risk of fraud and improve traceability in financial transactions, making it a powerful tool in the fight against money laundering.
  • Regulatory Technology, or RegTech, is gaining traction as a solution to the ever-increasing complexity of regulatory compliance. RegTech solutions leverage technologies like big data analytics, cloud computing, and AI to simplify compliance management and ensure regulatory adherence.

Potential Impact and Benefits

These advancements in AML software are set to bring numerous benefits to the Philippine financial market. Improved detection capabilities and streamlined processes will enhance the effectiveness of AML efforts. The integration of technologies like blockchain and RegTech can increase transparency, improve traceability, and simplify compliance management, leading to more robust and efficient AML systems.

Moreover, these technologies could also bring economic benefits. By reducing the costs associated with compliance management and fraud losses, financial institutions could potentially realize significant savings, which could then be channelled into more productive areas.

The Role of Regulatory Bodies and Financial Institutions

Adopting these emerging technologies in AML processes will require a collaborative effort from regulatory bodies and financial institutions. Regulatory bodies must provide clear guidance and regulations to ensure these technologies are used ethically and responsibly. They also have a role to play in fostering an environment that encourages innovation and the adoption of these technologies.

On the other hand, financial institutions must be proactive in staying abreast of these developments and integrating them into their AML processes. This will involve investing in the necessary infrastructure and training to effectively use these technologies.

Navigating the Road Ahead in AML Software Evolution

As we take a step back and reflect on the journey we've traversed, the evolution of AML in software within the Philippine context is nothing short of remarkable. From the early stages, marked by traditional methods burdened with limitations, we've witnessed a transformative shift towards a more modern and sophisticated approach, thanks to the advent of software solutions in AML.

The significance of continued innovation and adoption in this field cannot be overstated. In a world where financial crime is continually evolving, staying ahead of the curve is paramount. This spirit of innovation has driven the development of more advanced and efficient systems, paving the way for a more robust and effective approach to AML in the Philippines.

Companies like Tookitaki have been instrumental in this evolution. By developing state-of-the-art solutions like the AMLS, they empower financial institutions to modernize their compliance processes, provide comprehensive risk coverage, and significantly reduce false alerts.

The future of AML software in the Philippines is promising, teeming with emerging trends and technologies that are set to bring about even more significant advancements. As we navigate this road ahead, the role of companies like Tookitaki in leading this evolution will be more crucial than ever.

We encourage all financial institutions in the Philippines to take a proactive step towards strengthening their AML efforts. Book a demo for Tookitaki’s AMLS, and witness first-hand how our innovative solutions can revolutionize your compliance processes. Join us in leading the evolution of AML in the Philippines, and together, let's make a difference in the fight against financial crime.


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Blogs
30 Apr 2026
6 min
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AML Compliance for Tier 2 Banks: What Smaller Institutions Need to Get Right

AUSTRAC publishes its examination priorities for the year. The CCO at a regional Australian bank reads the list. Calibrated alert thresholds. Documentation of alert dispositions. EDD for high-risk customers. Periodic re-screening for PEPs.

The list looks the same as last year. And the year before.

The difference is that her team is 8 people — not 80. The obligation does not scale down with the headcount.

This is the operating reality for AML compliance at Tier 2 banks across Australia, Singapore, and Malaysia. Regional banks, digital banks, foreign bank branches, credit unions with banking licences — institutions that are fully regulated, fully examined, and fully liable, but are not Commonwealth Bank, DBS, or Maybank. The same rules apply. The resources do not.

This article covers where Tier 2 AML programmes most commonly fail examination, what "proportionate" compliance actually requires in practice, and how mid-size institutions build programmes that hold up without the 50-person compliance team.

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The Regulatory Reality: Same Obligations, Different Resources

AUSTRAC, MAS, and BNM do not operate two-tier AML standards. The AML/CTF Act 2006 applies to every reporting entity in Australia regardless of asset size. MAS Notice 626 applies to every bank licensed in Singapore. BNM's AML/CFT Policy Document applies to every licensed institution in Malaysia.

The only concession regulators make is proportionality. A risk-based approach means the scale of an AML programme should reflect the scale of the risk — the volume and nature of transactions, the customer risk profile, the jurisdictions involved. But the programme must exist, be effective, and produce documentation that survives examination.

Proportionality is not a waiver.

Westpac's AUD 1.3 billion penalty in 2020 was for a major bank. But AUSTRAC has also pursued civil penalty orders against smaller ADIs and credit unions for the same category of failures: uncalibrated monitoring thresholds, inadequate EDD, insufficient transaction reporting. The regulator's methodology does not change based on the institution's size. The fine may differ; the finding does not.

For Tier 2 banks in Singapore, MAS has been direct: digital banks licensed under the 2020 digital banking framework should reach AML maturity equivalent to established banks within 2–3 years of licensing. "We are new" has a shelf life. For Tier 2 institutions in Malaysia, BNM's Policy Document draws no distinction between Maybank and a smaller licensed Islamic bank on the core obligations for CDD, transaction monitoring, and suspicious transaction reporting.

Five Gaps Where Tier 2 Banks Fail Examination

Gap 1: Default Threshold Settings on Transaction Monitoring

The most common finding across AUSTRAC and MAS examinations of smaller institutions is transaction monitoring software running on vendor-default alert thresholds.

Default thresholds are calibrated for a generic customer population. A regional Australian bank with 80% SME customers needs different alert logic than a consumer retail bank. A digital bank in Singapore whose customers are predominantly salaried individuals transferring payroll needs different parameters than a trade finance operation. When the thresholds do not reflect the institution's actual customer base, two things happen: analysts receive alerts that are irrelevant to real risk, and the transactions that represent genuine risk pass without triggering review.

AUSTRAC's published guidance on transaction monitoring is explicit on this point. MAS expects institutions to document their threshold calibration rationale and demonstrate that calibration is reviewed periodically against the institution's current risk profile. An undated configuration file from the vendor implementation three years ago does not meet that standard.

See our transaction monitoring software buyer's guide for the evaluation criteria that matter when institutions are selecting a platform — threshold configurability is one of five criteria that directly affect examination outcomes.

Gap 2: Alert Backlogs from High False Positive Rates

A Tier 2 bank running a legacy rules-only transaction monitoring system at a 97% false positive rate and processing 200 alerts per day needs 2–3 full-time analysts to do nothing except clear the alert queue. For a compliance team of 8, that is 25–37% of total capacity consumed by alert triage before a single investigation has started.

The consequence is not just inefficiency. It is a programme that cannot function as designed. Analysts clearing high-volume, low-quality alert queues develop pattern fatigue. Genuine risk signals get the same 30-second review as the 97% of alerts that will be closed as false positives. EDD interviews do not happen because there is no analyst capacity to conduct them. Examination preparation is squeezed into the two weeks before the examiner arrives.

False positive rates are not a fixed cost of running a transaction monitoring programme. Legacy rules-only systems produce high false positive rates because they apply static thresholds to dynamic customer behaviour. Typology-driven, behaviour-based detection — which incorporates how a customer's transaction patterns change over time, not just whether a single transaction crosses a threshold — consistently produces lower false positive rates. The technology gap between rule-based and behaviour-based monitoring is the single largest source of operational inefficiency for Tier 2 compliance teams.

For background on how transaction monitoring works and why the architecture matters, see what is transaction monitoring.

Gap 3: Inconsistent EDD Application

Large banks have EDD workflows automated into their CRM and compliance systems. When a customer's risk rating changes, the system triggers an EDD task, assigns it to an analyst, and tracks completion. The process is not dependent on an individual's memory.

Tier 2 banks frequently run manual EDD processes. PEP screening happens at onboarding. Periodic re-screening often does not — or it happens for some customers and not others, depending on which analyst handles the review. Corporate customers with complex beneficial ownership structures receive initial CDD at onboarding; the review when the ultimate beneficial owner changes is missed because there is no system trigger.

BNM's Policy Document, MAS Notice 626, and AUSTRAC's rules all require EDD to be applied to high-risk customers on an ongoing basis, not just at the point of relationship establishment. "Ongoing" is not annual if the customer's risk profile changes quarterly. An examination finding in this area typically cites specific customer accounts where EDD was not conducted after a risk rating change — not a policy gap, but an execution gap.

Gap 4: Inadequate Documentation of Alert Dispositions

Alert closed. No SAR filed. No written rationale recorded.

In a team under sustained volume pressure, documentation shortcuts are predictable. An analyst who closes 40 alerts in a day and writes a full rationale for 15 of them is not cutting corners deliberately — the queue does not allow otherwise.

AUSTRAC and MAS treat undocumented alert closures as programme failures. Not because the disposition decision was necessarily wrong, but because there is no evidence that a human reviewed the alert and made a considered decision. From an examination standpoint, an alert with no documented rationale is indistinguishable from an alert that was never reviewed. The regulator cannot distinguish between "reviewed and correctly closed" and "bypassed."

This is a systems problem, not a people problem. Alert documentation should be generated as part of the disposition workflow, not as a separate manual step. Every alert closure should require a rationale field — even if the rationale is a structured selection from a drop-down of standard reasons. The documentation burden should be close to zero per alert for straightforward dispositions.

Gap 5: No Model Validation for ML-Based Detection

Tier 2 banks that have moved to AI-augmented transaction monitoring frequently lack the model governance infrastructure to validate that detection models are performing correctly over time.

A model trained on transaction data from 2022 that has never been retrained is not performing at specification in 2026. Customer behaviour shifts. Payment methods change. New typologies emerge. Without periodic model validation — testing whether the model's detection performance against current transaction patterns matches its baseline specification — the institution cannot make the assertion that its monitoring programme is effective.

MAS has flagged model governance as an emerging examination area. For Tier 2 banks, the challenge is that model validation at large banks is done by internal quant teams with the expertise to run performance tests, backtesting, and drift analysis. A 10-person compliance team at a regional bank does not have that capability in-house.

The answer is not to avoid AI-augmented monitoring. It is to select platforms where model validation documentation is generated automatically, and where retraining and recalibration is a vendor-supported function, not a requirement to build internal data science capability.

ChatGPT Image Apr 30, 2026, 10_04_33 AM

What "Proportionate" AML Compliance Actually Means

Proportionality is frequently misread as a licence to do less. It is not. It is permission to concentrate compliance resources where the actual risk is — rather than spreading equal effort across all customers regardless of their risk profile.

For a Tier 2 bank, proportionate compliance means three things in practice.

Automate the process work. Alert generation, threshold calibration triggers, EDD workflow initiation, documentation of alert dispositions — none of these should require analyst decision-making at each step. Every manual step is a point where volume pressure leads to shortcuts, and shortcuts are what examination findings are made of.

Free analyst capacity for work that requires judgement. Complex alert investigations, EDD interviews, SAR filing decisions, examination preparation — these require an experienced analyst's attention and cannot be automated. A team of 8 can do this work well, but only if they are not consuming 3–4 hours per day clearing a backlog of 200 low-quality alerts.

The arithmetic is specific: at a 97% false positive rate on 200 daily alerts, an analyst spends approximately 2.5 minutes on each alert just to clear the queue — that is 500 analyst-minutes, or roughly 8.3 hours, across a team. At a 50% false positive rate on the same 200 alerts, 100 alerts require substantive review. The remaining 100 are flagged for quick closure. Total review time drops to approximately 4–5 hours — returning 3–4 hours of analyst capacity daily for investigation and EDD work. At a 10-person team, that is 30–40% of daily compliance capacity returned to meaningful work.

Build documentation in, not on. Every compliance workflow should generate examination-ready records as a byproduct of normal operation, not as a separate documentation task.

Technology Requirements Specific to Tier 2

The enterprise transaction monitoring systems built for Tier 1 banks assume implementation resources that Tier 2 banks do not have. Multi-month professional services engagements, dedicated data engineering teams, internal model governance functions — these are not realistic for a regional bank with a 5-person technology team and a compliance budget that was set before the current regulatory environment.

Four technology requirements are specific to Tier 2:

Integration simplicity. Many Tier 2 banks run legacy core banking platforms. Cloud-native transaction monitoring platforms with standard API connectivity can connect to core banking data in weeks, not months, without requiring a custom integration project.

Compliance-configurable thresholds. Compliance staff should be able to adjust alert thresholds and add detection scenarios without vendor involvement. Calibration is a compliance function. If it requires a professional services engagement every time a threshold needs updating, calibration will not happen at the frequency regulators expect.

Predictable pricing. Per-transaction pricing models become unpredictable as transaction volumes grow. Tier 2 banks should look for flat-fee or tiered pricing that is budget-predictable against their transaction volume — one less variable in a constrained budget environment.

Exam-ready documentation, automatically. Alert audit trails, calibration records, and model validation documentation should be outputs of the platform's standard operation, not custom report builds. If producing the documentation package for an examination requires a week of manual compilation, the documentation package will always be incomplete.

For a structured framework on evaluating transaction monitoring vendors against these criteria, see the TM Software Buyer's Guide.

APAC-Specific Regulatory Context for Tier 2

Australia. AUSTRAC's risk-based approach explicitly accommodates proportionality — but AUSTRAC has examined and found against credit unions and smaller ADIs for the same monitoring failures as major banks. The AUSTRAC transaction monitoring requirements cover the specific obligations that apply to all reporting entities, regardless of size.

Singapore. MAS Notice 626 applies to all banks licensed in Singapore. For digital banks — which are structurally Tier 2 in Singapore's context — MAS has set explicit expectations that AML maturity should reach equivalence with established banks within 2–3 years of licensing. The MAS transaction monitoring requirements article covers the specific MAS standards in detail.

Malaysia. BNM's AML/CFT Policy Document applies to all licensed institutions. Smaller licensed banks, Islamic banks, and regionally focused institutions have the same CDD, monitoring, and reporting obligations as the major domestic banks. BNM's examination methodology does not grade on institution size.

What an Examination-Ready Tier 2 AML Programme Looks Like

Six elements characterise programmes that hold up to examination at Tier 2 institutions:

  1. A written AML/CTF programme, Board-approved and reviewed annually
  2. Transaction monitoring thresholds documented and calibrated against the institution's own customer risk assessment — with a dated record of when calibration was last reviewed and by whom
  3. An alert investigation workflow that generates a written rationale for every closed alert, including a structured reason code for dispositions that do not result in SAR filing
  4. EDD workflows triggered automatically by risk rating changes, not by analyst memory
  5. Annual model validation or rule-set review with documented outcomes, even where the outcome is "no changes required"
  6. Staff training records, including dates, completion rates, and assessment outcomes by employee

None of these six elements require a large compliance team. They require systems configured to produce the right outputs and workflows designed to generate documentation as a byproduct of normal operation.

How Tookitaki FinCense Fits the Tier 2 Context

Tookitaki's FinCense AML suite is deployed across institution sizes, including Tier 2 banks, digital banks, and licensed challengers in Australia, Singapore, and Malaysia.

FinCense is cloud-native with standard API connectivity, which reduces integration time for institutions that do not have dedicated implementation teams. Compliance staff can configure alert thresholds and detection scenarios without vendor support — calibration happens on the institution's schedule, not when a professional services engagement can be arranged.

APAC-specific typologies and pre-built documentation for AUSTRAC, MAS Notice 626, and BNM's Policy Document are included in the platform. These are not professional services add-ons; they are part of the standard deployment.

In production deployments, FinCense has reduced false positive rates by up to 50% compared to legacy rule-based systems. At a 10-person compliance team processing 200 daily alerts, that returns approximately 3–4 hours of analyst capacity per day — enough to run substantive investigations, keep EDD current, and arrive at examination with documentation that was built during normal operations, not assembled in a panic the week before.

See FinCense in a Tier 2 Bank Context

If your institution is carrying the same AML obligations as the major banks with a fraction of the compliance resources, the question is not whether you need a programme that works — it is whether your current programme will hold up when the examiner arrives.

Book a demo to see FinCense configured for a Tier 2 bank: realistic transaction volumes, a compliance team of fewer than 20, and the documentation outputs that AUSTRAC, MAS, and BNM expect.

If you are still evaluating options, the TM Software Buyer's Guide provides a structured framework for comparing platforms on the criteria that matter most for smaller compliance teams.

AML Compliance for Tier 2 Banks: What Smaller Institutions Need to Get Right
Blogs
30 Apr 2026
6 min
read

Tranche 2 AML Reforms in Australia: What Businesses Need to Do Now

The email from your legal operations director lands on a Tuesday morning. It references something called the AML/CTF Amendment Act 2024. It asks whether your law firm is now a "reporting entity." It asks whether you need to enrol with AUSTRAC.

You are a managing partner. You run a mid-size conveyancing and commercial law practice. You have never thought of your firm as being in the same regulatory category as a bank. You do not have a compliance team. You do not have an AML programme. And somewhere in the back of your mind, you remember hearing about "Tranche 2" a few years ago — and then hearing it had been delayed again.

It has not been delayed again.

The AML/CTF Amendment Act 2024 received Royal Assent on 29 November 2024. If your firm provides designated legal services — real estate transactions, managing client funds, forming companies or trusts, managing assets on behalf of clients — you are captured. The clock is running.

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What Tranche 2 Is, and Why It Took 17 Years

Australia's Anti-Money Laundering and Counter-Terrorism Financing Act 2006 — the AML/CTF Act — came into force as Tranche 1. It regulated financial institutions: banks, credit unions, remittance dealers, casinos. Lawyers, accountants, and real estate agents were left out, with an explicit commitment that a second tranche of reforms would extend the regime to designated non-financial businesses and professions (DNFBPs).

That commitment sat largely dormant for 17 years.

The Financial Action Task Force (FATF) conducted a Mutual Evaluation of Australia in 2015 and named the absence of Tranche 2 as a major gap in Australia's AML/CTF framework. Australia's national risk assessment consistently identified real estate, legal services, and corporate structuring as channels for money laundering — yet the lawyers, accountants, and property agents facilitating those transactions had no formal AML obligations. Australia was one of the last FATF member jurisdictions to operate without DNFBP coverage.

The AML/CTF Amendment Act 2024 ends that. It amends the AML/CTF Act 2006 to extend obligations to Tranche 2 entities for the first time. Royal Assent was 29 November 2024.

Who Is Captured Under Tranche 2

Not every professional in a captured sector becomes a reporting entity. The test is whether you provide a "designated service" as defined under the amended Act. The scope matters.

Lawyers and Law Firms

Law firms are captured when providing specific services:

  • Acting in the purchase or sale of real property on behalf of a client
  • Managing client money, securities, or other assets
  • Forming companies, trusts, or other legal entities on behalf of a client
  • Acting as a director, secretary, or nominee shareholder for a client
  • Providing business sale or purchase advice involving fund transfers

Litigation is not captured. General legal advice is not captured. The obligations attach to the transaction-facing, fund-handling, and corporate-structuring work — the services most associated with money laundering risk.

Accountants

Accountants providing the following services are captured:

  • Managing client funds or financial assets
  • Forming companies, trusts, or other legal entities
  • Providing advice on business acquisition or disposal that involves fund transfers

Tax return preparation alone is not captured. The risk-based logic is the same as for lawyers: the obligations follow the money and the structural work.

Real Estate Agents

Real estate agents acting in the purchase or sale of real property are captured. Property management services are not captured. This distinction matters for agencies that carry both a sales division and a property management business — the compliance obligations attach to the former, not the latter.

Dealers in Precious Metals and Stones

Dealers conducting cash transactions at or above AUD 5,000 are captured. This threshold reflects the cash-intensity risk in this sector. Card or bank transfer transactions below that threshold are not in scope.

Trust and Company Service Providers (TCSPs)

TCSPs are captured for the full range of their entity formation, directorship, and registered office services.

What Tranche 2 Entities Must Do: The Core Obligations

Once captured, the obligations are substantive. They mirror the framework already imposed on financial institutions under the AML/CTF Act 2006, adapted to a professional services context.

Enrol with AUSTRAC. Reporting entities must register with AUSTRAC before providing designated services after the relevant commencement date. AUSTRAC maintains a public register of reporting entities.

Develop an AML/CTF programme. The programme has two parts. Part A is a board-approved risk assessment — a documented analysis of the ML/TF risks your firm faces based on the designated services you provide, the client types you serve, the jurisdictions involved, and the delivery channels used. Part B is the set of controls: customer identification procedures, ongoing monitoring, staff training, and reporting processes.

Customer identification and verification. Before providing a designated service, the entity must identify and verify the customer. For individuals, this typically means collecting and verifying name, date of birth, and address using reliable documentation. For companies and trusts, the obligations extend to beneficial ownership — understanding who ultimately controls or benefits from the entity.

Ongoing customer due diligence. The initial CDD is not a one-time exercise. Entities must monitor existing client relationships for changes in risk profile and update their CDD records accordingly.

Transaction monitoring. Entities must monitor for unusual or suspicious activity. The definition of "unusual" depends on the firm's own risk assessment — a conveyancing practice will have different baseline transaction patterns from an accounting firm that manages investment assets.

File Suspicious Matter Reports (SMRs). Where an entity has reasonable grounds to suspect that a customer or transaction is connected to money laundering or terrorism financing, an SMR must be filed with AUSTRAC within 3 business days of forming that suspicion. The 3-day clock is statutory — it is not extendable because the matter is complex.

File Threshold Transaction Reports (TTRs). Cash transactions of AUD 10,000 or more must be reported to AUSTRAC. This is the same threshold that applies to financial institutions.

Record keeping. Customer due diligence documents and transaction records must be retained for 7 years from the date of the relevant transaction or the end of the business relationship, whichever is later.

AUSTRAC annual compliance report. Reporting entities must submit an annual compliance report to AUSTRAC covering the adequacy of their AML/CTF programme and their compliance during the reporting period.

Phased Implementation: What Is Happening When

The AML/CTF Amendment Act 2024 received Royal Assent on 29 November 2024, but that date did not trigger immediate obligations for Tranche 2 entities. Commencement of specific provisions is subject to Ministerial instruments, and AUSTRAC has signalled a phased approach to give newly captured entities time to build their programmes.

AUSTRAC's published guidance indicates that enrolment obligations and AML/CTF programme development requirements are expected to commence in 2026, with the full suite of reporting and ongoing obligations to follow. However, specific commencement dates for each obligation type remain subject to confirmation through formal commencement instruments.

This is a meaningful distinction. The legislation exists. The obligation to eventually comply is not in doubt. But the date from which AUSTRAC can take enforcement action for non-compliance with a given obligation depends on the commencement date of that obligation — and those dates are being phased, not simultaneous.

What this means in practice: Firms should monitor AUSTRAC's website (austrac.gov.au) for confirmed commencement dates and guidance specific to their sector. AUSTRAC has already published Tranche 2 guidance for lawyers, accountants, real estate agents, and TCSPs. Waiting for a final date before starting programme development is not a sound approach — the lead time required to build a compliant AML/CTF programme is measured in months, not weeks.

What This Means for Banks and Existing Reporting Entities

Tranche 2 does not only affect the newly captured entities. For banks and other financial institutions already operating under the AML/CTF Act 2006, it changes the risk environment in two ways.

The counterparty risk picture changes. Law firms, accounting practices, real estate agencies, and precious metals dealers that were previously unregulated are now reporting entities with their own AML obligations. Banks that hold accounts for these businesses can factor their regulated status into CDD assessments. A law firm that has enrolled with AUSTRAC, implemented an AML/CTF programme, and is actively monitoring for suspicious activity is a materially different risk profile from one that had no such obligations.

Expectations around correspondent and professional services accounts will rise. AUSTRAC is likely to assess whether banks are reflecting the updated regulatory status of Tranche 2 sectors in their own monitoring and CDD frameworks. A bank that continues to treat a law firm client account as low-risk without considering whether that firm has enrolled and implemented its programme is exposed to questions about the adequacy of its own risk assessment.

Property-linked layering — moving proceeds of crime through sequential real estate transactions — is documented in Australia's national money laundering risk assessments as a method that has operated with relative ease due to the absence of AML controls on real estate agents and conveyancers. That gap is now being closed. Banks whose transaction monitoring is tuned to detect this pattern should review whether the new regulated status of real estate agents affects their detection logic.

For more detail on AUSTRAC's expectations for transaction monitoring at financial institutions, see our guide to AUSTRAC transaction monitoring requirements.

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Building an AML Programme from Scratch: Seven Steps

For Tranche 2 entities starting from zero, the AML/CTF programme requirement is the most substantive obligation. Here is the structure.

Step 1: Identify your designated services. Not all services a law firm or accounting practice provides are captured. Document which of your services meet the definition of a designated service under the amended Act. This is the scope boundary for everything that follows.

Step 2: Conduct a risk assessment (Part A). For each designated service, assess the money laundering and terrorism financing risks based on: client types (individuals, companies, trusts, politically exposed persons, foreign clients), delivery channels (in-person, remote, intermediary-introduced), transaction types and sizes, and the jurisdictions involved. The risk assessment must be documented and approved at board or senior management level.

Step 3: Design your customer identification procedures. Document exactly what identity information you collect from each customer type, at what point in the engagement, and how you verify it. Verification sources must be reliable and independent. Document what you do when you cannot complete verification.

Step 4: Define your ongoing monitoring approach. For your client base, define what an unusual transaction or instruction looks like. A real estate agent processing a cash contract at AUD 4,800 — just below the AUD 5,000 cash threshold — warrants scrutiny. A law firm receiving funds from an unexpected third party for a property settlement is a red flag regardless of amount. Document your red flag indicators and the escalation process.

Step 5: Establish your SMR and TTR filing process. Designate who is responsible for filing Suspicious Matter Reports. Build the 3-business-day clock into your workflow. For TTRs, create a process that captures cash transactions at or above AUD 10,000 at point of receipt — do not rely on end-of-period reconciliations.

Step 6: Train your staff. Everyone who interacts with clients or handles client funds needs AML/CTF awareness training. Training should cover: what money laundering looks like in your practice context, how to identify red flags, what to do when something feels wrong, and how to report internally without tipping off the client.

Step 7: Establish your record-keeping system. You need to retain CDD documents and transaction records for 7 years. If your firm's document management system was designed for legal file retention rather than AML compliance, you may need a separate system or process for AML records.

AUSTRAC's Enforcement Posture

AUSTRAC has a documented history of supporting newly regulated sectors through education before moving to enforcement. The regulator published Tranche 2-specific guidance and engaged with professional associations in the legal and accounting sectors during the consultation process.

That said, the context for Tranche 2 is different from previous regulatory expansions. Australia has operated without DNFBP AML coverage for 17 years, under sustained FATF scrutiny. The reputational and diplomatic pressure behind Tranche 2 is significant. AUSTRAC is unlikely to treat good-faith ignorance the same way it might have in an earlier era.

AUSTRAC's civil penalty powers apply from commencement. For body corporates, civil penalties can reach AUD 17.9 million per contravention. For individuals, penalties are lower but substantial. AUSTRAC also has the power to accept enforceable undertakings, issue infringement notices, and seek injunctions.

The enforcement risk is not theoretical. AUSTRAC has pursued major civil penalty actions against Westpac (AUD 1.3 billion), Commonwealth Bank (AUD 700 million), and SportsSuper. A newly captured entity that makes no effort to enrol or build a programme faces a different enforcement calculus from one that has enrolled, built a programme, and is working through implementation challenges.

Getting the Programme Right

For Tranche 2 entities building their first AML/CTF programme, technology makes a material difference in whether the programme works in practice. A documented policy that exists only on paper will not detect a suspicious transaction or generate a timely SMR.

For institutions already operating under the AML/CTF Act 2006 that need to review their transaction monitoring in light of Tranche 2, our transaction monitoring software buyer's guide covers what to look for in a compliant monitoring system. If you are newer to transaction monitoring concepts, our introduction to transaction monitoring sets out the fundamentals.

Tookitaki's AFC Ecosystem is built for the compliance requirements that AUSTRAC and other regulators enforce. If you are building or upgrading an AML programme for the Australian market — whether as a newly captured Tranche 2 entity or an existing reporting entity adjusting to the new environment — book a demo to see how the platform handles the specific detection and reporting requirements that apply under the AML/CTF Act.

AUSTRAC has confirmed that Tranche 2 obligations are coming. The question now is not whether to build a programme — it is whether to build one before commencement or after the first enforcement action arrives.

Tranche 2 AML Reforms in Australia: What Businesses Need to Do Now
Blogs
29 Apr 2026
6 min
read

Inside the Parañaque Scam Factory: What 48 Arrests Reveal About the Industrialisation of Online Fraud

On 20 April 2026, Philippine media reported that the National Bureau of Investigation had arrested 48 individuals after raiding an alleged online scamming hub in Parañaque City. The timing matters. This is not an old case being revisited. It is a fresh reminder that scam operations across Southeast Asia are still active, organised, and scaling fast.

When authorities entered the site, they did not just uncover another isolated scam. They walked into something far more structured — an operation that looked less like opportunistic fraud and more like a production line.

Dozens of individuals. Multiple devices. Coordinated activity. A setup that resembled a call centre more than a loose group of fraudsters.

For compliance teams, this is not just another headline. It is a signal. Modern scam networks are becoming more industrialised, and the financial trails they leave behind are becoming harder to detect with static, siloed controls.

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What Actually Happened in Parañaque

The raid exposed an online scamming hub operating at scale. Investigators found individuals actively engaged in defrauding victims, likely through a mix of social engineering tactics — investment scams, impersonation schemes, and possibly romance or job scams.

What stood out was not just the activity itself, but the structure:

  • Multiple operators working simultaneously
  • Dedicated systems and devices
  • Coordinated workflows
  • A controlled environment, almost like a call centre

This was not a loose group of fraudsters. It was organised, repeatable, and designed for volume.

That distinction matters.

Because once fraud becomes structured like this, it stops being unpredictable and starts becoming scalable.

The Shift from Scams to Scam Infrastructure

For years, fraud has often been viewed as a series of isolated incidents. A phishing email here. A social engineering case there.

That lens no longer holds.

What the Parañaque case reveals is something deeper: the rise of scam infrastructure.

These are not individuals improvising. These are networks designed with:

  • Recruitment pipelines
  • Scripted engagement models
  • Operational roles and hierarchies
  • Performance-driven execution

In many ways, these setups mirror legitimate businesses — except the product being “sold” is deception.

And like any efficient system, they optimise over time.

They test what works. They refine messaging. They reuse successful playbooks. They scale quickly.

For financial institutions, this changes the challenge entirely.

You are no longer detecting one-off fraud. You are up against systems that are constantly learning and adapting.

Why This Matters for Financial Institutions

At first glance, a physical raid in the Philippines may feel distant to a bank in Singapore or a fintech in Australia.

But the financial footprint of such operations is rarely local.

Scam proceeds move quickly — often across borders, across institutions, and across channels.

A typical flow might look like this:

  • Victim transfers funds via online banking or wallet
  • Funds are routed through mule accounts
  • Split into smaller transactions
  • Moved across jurisdictions
  • Layered further to obscure origin

By the time the money surfaces in a financial institution’s system, it often appears routine.

That is the real risk.

Not at the point of the scam, but at the point where illicit funds blend into legitimate financial flows.

The Hidden Complexity Behind “Simple” Scams

It is easy to dismiss scams as basic manipulation.

But cases like this show how layered they have become.

Behind a single victim interaction, there may be:

  • A recruitment network sourcing operators
  • A technical setup managing communication channels
  • A financial layer handling fund movement
  • A supervisory layer coordinating activity

Each layer introduces its own signals.

But those signals are rarely obvious in isolation.

A transaction might look normal.
A customer profile might appear clean.
A payment pattern may not trigger any threshold.

Yet, when viewed together, they form a pattern.

This is the daily reality for compliance teams — connecting weak, fragmented signals into something meaningful.

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Where Traditional Detection Starts to Break Down

Most financial institutions still rely, at least in part, on rule-based monitoring.

And rules do have their place.

But against structured scam operations, they begin to show limitations:

  • Static thresholds struggle against evolving behaviour
  • Isolated alerts fail to capture network patterns
  • Manual tuning cannot keep pace with changing typologies

In the Parañaque case, individual transactions may not have appeared suspicious.

What made them risky was the context — the coordination, the repetition, the connections.

This is where traditional systems fall short.

They are built to detect anomalies, not ecosystems.

The Role of Mule Networks in Scaling Fraud

No large-scale scam operation works without one critical component: money mules.

These accounts absorb, move, and disguise illicit funds.

And they are becoming increasingly sophisticated.

Some are unwitting — recruited through job offers or incentives.
Others are complicit — knowingly participating in exchange for a share.

Either way, they create a buffer between fraudsters and the financial system.

In operations like the Parañaque hub, mule networks likely operate in parallel:

  • Receiving funds from multiple victims
  • Redistributing across accounts
  • Moving funds rapidly across borders

From a compliance perspective, mule activity often appears as:

  • High-velocity transactions
  • Rapid inflows and outflows
  • Accounts with little genuine economic activity

But again, these signals are rarely conclusive on their own.

The Cross-Border Reality

Modern fraud rarely stays within one jurisdiction.

A scam initiated in one country can impact victims in another, with funds routed through multiple regions.

This creates three persistent challenges:

  1. Fragmented visibility
    No single institution sees the full transaction chain
  2. Jurisdictional differences
    Regulatory expectations and data access vary
  3. Delayed intervention
    By the time alerts are triggered, funds have already moved

The Parañaque case reinforces a simple truth: financial crime is global, even when it appears local.

What Compliance Teams Should Be Looking For

Rather than focusing on isolated red flags, institutions need to identify patterns of behaviour.

Indicators aligned with operations like this include:

  • Clusters of accounts exhibiting similar transaction flows
  • Repeated low-to-mid value transfers across multiple beneficiaries
  • Rapid movement of funds with minimal retention
  • Shared identifiers such as devices, IPs, or contact details
  • Activity inconsistent with stated customer profiles

Individually, these may not trigger concern.

Collectively, they signal coordination.

Moving from Detection to Understanding

There is a broader shift underway in financial crime prevention.

From generating alerts…
To understanding behaviour.

It is no longer enough to flag transactions.

Teams need to ask:

  • Why is this activity happening?
  • How is it connected to other behaviour?
  • What broader typology does it resemble?

This shift is not easy.

Because understanding requires context — and context requires intelligence beyond internal data.

The Role of Collaborative Intelligence

Cases like the Parañaque scam hub highlight a structural gap.

No single institution has full visibility.

Fraud patterns are distributed across:

  • Banks
  • Fintech platforms
  • Payment processors
  • Geographies

Which means detection cannot rely on isolated systems.

Collaborative intelligence becomes critical.

By sharing typologies, behavioural patterns, and risk signals without exposing sensitive data institutions can:

This is where community-driven intelligence models are gaining traction.

Where Technology Needs to Evolve

To keep pace with structured fraud operations, detection systems need to evolve in three ways:

1. From rules to adaptive intelligence
Systems must continuously learn from emerging patterns

2. From transactions to networks
Detection must capture relationships, not just events

3. From alerts to actionable insights
Outputs must support faster, clearer investigation decisions

This is not about replacing existing systems overnight.

It is about enhancing them to reflect how fraud actually operates today.

The Cost of Getting This Wrong

The impact of missing these signals goes beyond financial loss.

There are broader consequences:

  • Increased regulatory scrutiny
  • Reputational damage
  • Erosion of customer trust

In fast-growing digital markets, trust is not easily rebuilt once lost.

And fraud, left unchecked, directly undermines it.

A More Grounded Way Forward

The Parañaque case is not an anomaly. It is part of a pattern.

Fraud is becoming:

  • More organised
  • More scalable
  • More adaptive

And increasingly embedded within legitimate financial systems.

Responding to this requires a shift:

From reactive to proactive
From siloed to collaborative
From static to adaptive

For compliance teams, this is not about chasing every new scam.

It is about building the capability to recognise patterns — even as they evolve.

Conclusion: Beyond the Raid

The arrest of 48 individuals is a meaningful enforcement action.

But it is not the end of the story.

Operations like these rarely disappear. They adapt, relocate, and re-emerge.

For financial institutions, the real question is not whether such scams exist.

It is whether their systems can detect the financial signals these operations inevitably leave behind.

Because while enforcement can shut down a physical hub, the financial trails continue to move.

And that is where the real battle is being fought.

Inside the Parañaque Scam Factory: What 48 Arrests Reveal About the Industrialisation of Online Fraud