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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BSP Proposes Tougher Penalties for Reporting Lapses: What Payment Operators Need to Know
The payments landscape in the Philippines has transformed rapidly in recent years. Digital payments now account for more than half of all retail transactions in the country, and uptake continues to grow as consumers and businesses turn to mobile wallets, online transfers, QR payments, and instant fund movements.
This shift has also brought new expectations from regulators. As digital transactions scale, the integrity of data, the accuracy of reporting, and the ability of payment system operators to maintain strong compliance controls have become non negotiable. The Bangko Sentral ng Pilipinas (BSP) has repeatedly emphasised that a safe and reliable digital payments ecosystem requires timely and accurate regulatory submissions.
This is the backdrop of the BSP’s newly proposed penalty framework for reporting lapses among payment system operators. It is a significant development. The proposal introduces daily monetary penalties for inaccurate or late submissions, along with potential non monetary sanctions for responsible officers. While the circular is still open for industry comments, its message is clear. Reporting lapses are no longer administrative oversights. They are operational weaknesses that can create systemic risk.
This blog unpacks what the proposal means, why it matters, and how financial institutions can strengthen their compliance and reporting environment in preparation for a more stringent regulatory era.
Why BSP Is Tightening Its Penalty Framework
The Philippines payments environment has seen rapid adoption of digital technologies, driven by financial inclusion goals and customer expectations for speed and convenience. With this acceleration comes a larger volume of data that financial institutions must capture, analyse, and report to regulators.
Several factors explain why BSP is moving towards stricter penalties:
1. Reporting is foundational to systemic stability
Regulators rely on accurate data to assess risks in the payment system. Gaps, inaccuracies, or delays can compromise oversight and create blind spots in areas such as liquidity flows, settlement patterns, operational disruptions, fraud, and unusual transaction activity.
2. Growth of non bank players
Many payment functions are now driven by fintechs, payment service providers, and other non bank operators. While this innovation expands access, it also requires a higher level of supervisory vigilance.
3. Increasing use of instant payments
With real real time payment channels becoming mainstream, reporting integrity becomes more critical. A single faulty dataset can affect risk assessments across multiple institutions.
4. Rise in financial crime and operational risk
Fraud, mule activity, phishing, account takeovers, and cross border scams have all increased. Accurate reporting helps regulators track patterns and intervene quickly.
5. Alignment with data governance expectations globally
Across ASEAN and beyond, regulators are raising standards for data quality, governance, and reporting. BSP’s proposal follows this global trend.
In short, accurate reporting is no longer just compliance housekeeping. It is central to maintaining trust and stability in a digital financial system.
What the BSP’s Proposed Penalty Framework Includes
The draft circular introduces several new enforcement mechanisms that significantly raise the stakes for reporting lapses.
1. Daily monetary penalties
Instead of one time fines, penalties may accrue daily until the issue is corrected. The amounts vary by institution type:
- Large banks: up to PHP 3,000 per day
- Digital banks: up to PHP 2,000 per day
- Thrift banks: up to PHP 1,500 per day
- Rural and cooperative banks: PHP 450 per day
- Non bank payment system operators: up to PHP 1,000 per day
These penalties apply after the first resubmission window. If the revised report still fails to meet BSP’s standards, the daily penalty starts accumulating.
2. Potential non monetary sanctions
Beyond fines, responsible directors or officers may face:
- Suspension
- Disqualification
- Other administrative measures
This signals that reporting lapses are now viewed as governance failures, not just operational issues.
3. Covers accuracy, completeness, and timeliness
Reporting lapses include:
- Late submissions
- Incorrect data
- Missing fields
- Inconsistent formatting
- Incomplete reports
BSP is emphasising the importance of end to end data integrity.
4. Applies to all payment system operators
This includes banks and non bank entities engaged in:
- E wallets
- Remittance services
- Payment gateways
- Digital payment rails
- Card networks
- Clearing and settlement participants
The message is clear. Every participant in the payments ecosystem has a responsibility to ensure accurate reporting.
Why Reporting Lapses Are Becoming a Serious Compliance Risk
Reporting lapses may seem minor compared to fraud, AML breaches, or cybersecurity threats. However, in a digital financial system, they can trigger serious operational and reputational consequences.
1. Reporting inaccuracies can mask suspicious patterns
Poor quality data can hide indicators of financial crime, mule activity, unusual flows, or cross channel fraud.
2. Delays affect systemic risk monitoring
In real time payments, regulators need timely data to detect anomalies and protect end users.
3. Data discrepancies create regulatory red flags
Repeated corrections or inconsistencies may suggest weak controls, insufficient oversight, or internal process failures.
4. Poor reporting signals weak operational governance
BSP views reporting as a reflection of an institution’s internal controls, risk management capability, and overall compliance culture.
5. Reputational risk for institutions
Long term credibility with regulators is tied to consistent compliance performance.
In environments like the Philippines, where digital adoption is growing quickly, institutions that fall behind on reporting standards face increasing supervisory pressure.
How Payment Operators Can Strengthen Their Reporting Framework
To operate confidently in this environment, organisations need strong internal processes, data governance frameworks, and technology that supports accurate, timely reporting.
Here are key steps financial institutions can take.
1. Strengthen internal governance for reporting
Institutions should formalise clear roles and ownership for reporting accuracy, including:
- Defined reporting workflows
- Documented data lineage
- Internal sign offs before submission
- Review and escalation protocols
- Consistent internal audit coverage
Treating reporting as a governance function rather than a technical task helps reduce errors.
2. Improve data quality controls
Reporting issues often stem from weak data foundations. Institutions should invest in:
- Data validation at source
- Automated quality checks
- Consistency rules across systems
- Deduplication and formatting controls
- Stronger reconciliation processes
Accurate reporting starts with clean, validated data.
3. Reduce manual dependencies
Manual processing increases the risk of:
- Typos
- Formatting errors
- Wrong values
- Missing fields
- Late submissions
Automation can significantly improve accuracy and speed.
4. Establish real time monitoring for data readiness
Real time payments require real time visibility. Institutions should build dashboards that track:
- Submission deadlines
- Pending validations
- Data anomalies
- Report generation status
- Submission completeness
Proactive monitoring helps prevent last minute errors.
5. Build a reporting culture
Compliance culture is not limited to the AML or risk team. Reporting accuracy must be part of the organisation’s broader mindset.
This includes:
- Leadership awareness
- Cross functional coordination
- Regular staff training
- Internal awareness of BSP standards
A strong culture reduces repeat errors and supports sustainable compliance.
Where Technology Plays a Transformative Role
Payment operators in the Philippines face growing expectations from regulators, customers, and partners. Manual systems will struggle to keep pace with the increasing volume, speed, and complexity of payments and reporting requirements.
Advanced compliance technology offers significant advantages in this environment.
1. Automated data validation and enrichment
Technology can continuously clean, check, and normalise data, reducing errors at source.
2. Stronger reporting accuracy with AI powered checks
Modern systems detect anomalies and provide real time alerts before submission.
3. Integrated risk and reporting environment
Unified platforms reduce fragmentation, helping ensure data consistency across AML, payments, and reporting functions.
4. Faster submission cycles
Automated generation and submission reduce operational delays.
5. Lower compliance cost per transaction
Technology reduces manual dependency and improves investigator productivity.
This is where Tookitaki’s approach provides strong value to institutions in the Philippines.
How Tookitaki Helps Strengthen Reporting and Compliance in the Philippines
Tookitaki supports financial institutions through a combination of its Trust Layer, federated intelligence, and advanced compliance platform, FinCense. These capabilities help institutions reduce reporting lapses and elevate overall governance.
Importantly, several leading digital financial institutions in the Philippines already work with Tookitaki to strengthen their AML and compliance foundations. Customers like Maya and PayMongo use Tookitaki solutions to build cleaner data pipelines, enhance risk analysis, and maintain strong reporting resilience in a rapidly evolving regulatory environment.
1. FinCense improves data integrity and monitoring
FinCense provides automated data checks, risk analysis, and validation across AML, fraud, and compliance domains. This ensures that institutions operate with cleaner and more accurate datasets, which flow directly into reporting.
2. Agentic AI enhances investigation quality
Tookitaki’s AI powered investigation tools help identify inconsistencies, suspicious patterns, or data gaps early. This reduces the risk of incorrect reporting and strengthens audit readiness.
3. Better governance through the Trust Layer
Tookitaki’s Trust Layer enables consistency, transparency, and explainability across decisions and reporting. Institutions gain a clear record of how data is processed, how decisions are made, and how controls are applied.
4. Federated intelligence helps identify systemic risks
Through the AFC Ecosystem, member institutions benefit from shared insights on emerging typologies, reporting vulnerabilities, and financial crime risks. This community driven model enhances awareness and strengthens reporting standards.
5. Configurable reporting and audit tools
FinCense supports financial institutions with structured reporting exports, audit logs, and compliance dashboards that help generate accurate and complete reports aligned with regulatory expectations.
For organisations preparing for a tighter penalty regime, these capabilities help elevate reporting from reactive to proactive.
What This Regulatory Shift Means for the Future
The BSP’s proposed penalties are part of a larger trend shaping financial regulation:
1. Data governance is becoming a compliance priority
Institutions will need full visibility into where data comes from, how it is transformed, and who is responsible for each reporting field.
2. Expect more scrutiny on non banks
Fintechs and payment providers will face higher regulatory expectations as their role in the ecosystem grows.
3. Technology adoption will accelerate
Manual reporting processes will not scale. Institutions will need automation and advanced analytics to meet higher standards.
4. Reporting accuracy will influence regulatory trust
Organisations that demonstrate consistent accuracy will gain smoother interactions, fewer supervisory interventions, and more regulatory confidence.
5. Strong compliance will help drive competitive advantage
In the digital payments era, trust is a business asset. Institutions that demonstrate reliability and transparency will attract more customers and partners.
Conclusion
The BSP’s proposed penalty framework is more than a compliance update. It is a signal that the Philippines is strengthening its digital payments ecosystem and aligning financial regulation with global standards.
For payment system operators, the message is clear. Reporting lapses must be addressed through better governance, stronger data quality, and robust technology. Institutions that invest early will be better positioned to operate with confidence, reduce regulatory risk, and build long term trust with stakeholders.
Tookitaki remains committed to supporting financial institutions in the Philippines with advanced, trusted, and future ready compliance technology that strengthens reporting, reduces operational risk, and enhances governance across the payments ecosystem.
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Trapped on Camera: Inside Australia’s Chilling Live-Stream Extortion Scam
Introduction: A Crime That Played Out in Real Time
It began like a scene from a psychological thriller — a phone call, a voice claiming to be law enforcement, and an accusation that turned an ordinary life upside down.
In mid-2025, an Australian nurse found herself ensnared in a chilling scam that spanned months and borders. Fraudsters posing as Chinese police convinced her she was implicated in a criminal investigation and demanded proof of innocence.
What followed was a nightmare: she was monitored through live-stream video calls, coerced into isolation, and ultimately forced to transfer over AU$320,000 through multiple accounts.
This was no ordinary scam. It was psychological imprisonment, engineered through fear, surveillance, and cross-border financial manipulation.
The “live-stream extortion scam,” as investigators later called it, revealed how far organised networks have evolved — blending digital coercion, impersonation, and complex laundering pipelines that exploit modern payment systems.
The Anatomy of the Scam
According to reports from Australian authorities and news.com.au, the scam followed a terrifyingly systematic pattern — part emotional manipulation, part logistical precision.
- Initial Contact – The victim received a call from individuals claiming to be from the Chinese Embassy in Canberra. They alleged that her identity had been used in a major crime.
- Transfer to ‘Police’ – The call was escalated to supposed Chinese police officers. These fraudsters used uniforms and badges in video calls, making the impersonation feel authentic.
- Psychological Entrapment – The victim was told she was under investigation and must cooperate to avoid arrest. She was ordered to isolate herself, communicate only via encrypted apps, and follow their “procedures.”
- The Live-Stream Surveillance – For weeks, scammers demanded she keep her webcam on for long hours daily so they could “monitor her compliance.” This tactic ensured she remained isolated, fearful, and completely controlled.
- The Transfers Begin – Under threat of criminal charges, she was instructed to transfer her savings into “safe accounts” for verification. Over AU$320,000 was moved in multiple transactions to mule accounts across the region.
By the time she realised the deception, the money had vanished through layers of transfers and withdrawals — routed across several countries within hours.
Why Victims Fall for It: The Psychology of Control
This scam wasn’t built on greed. It was built on fear and authority — two of the most powerful levers in human behaviour.
Four manipulation techniques stood out:
- Authority Bias – The impersonation of police officials leveraged fear of government power. Victims were too intimidated to question legitimacy.
- Isolation – By cutting victims off from family and friends, scammers removed all sources of doubt.
- Surveillance and Shame – Continuous live-stream monitoring reinforced compliance, making victims believe they were truly under investigation.
- Incremental Compliance – The fraudsters didn’t demand the full amount upfront. Small “verification transfers” escalated gradually, conditioning obedience.
What made this case disturbing wasn’t just the financial loss — but how it weaponised digital presence to achieve psychological captivity.
The Laundering Playbook: From Fear to Finance
Behind the emotional manipulation lay a highly organised laundering operation. The scammers moved funds with near-institutional precision.
- Placement – Victims deposited funds into local accounts controlled by money mules — individuals recruited under false pretences through job ads or online chats.
- Layering – Within hours, the funds were fragmented and channelled:
- Through fintech payment apps and remittance platforms with fast settlement speeds.
- Into business accounts of shell entities posing as logistics or consulting firms.
- Partially converted into cryptocurrency to obscure traceability.
- Integration – Once the trail cooled, the money re-entered legitimate financial channels through overseas investments and asset purchases.
This progression from coercion to laundering highlights why scams like this aren’t merely consumer fraud — they’re full-fledged financial crime pipelines that demand a compliance response.
A Broader Pattern Across the Region
The live-stream extortion scam is part of a growing web of cross-jurisdictional deception sweeping Asia-Pacific:
- Taiwan: Victims have been forced to record “confession videos” as supposed proof of innocence.
- Malaysia and the Philippines: Scam centres dismantled in 2025 revealed money-mule networks used to channel proceeds into offshore accounts.
- Australia: The Australian Federal Police continues to warn about rising “safe account” scams where victims are tricked into transferring funds to supposed law enforcement agencies.
The convergence of social engineering and real-time payments has created a fraud ecosystem where emotional manipulation and transaction velocity fuel each other.
Red Flags for Banks and Fintechs
Financial institutions sit at the frontline of disruption.
Here are critical red flags across transaction, customer, and behavioural levels:
1. Transaction-Level Indicators
- Multiple mid-value transfers to new recipients within short intervals.
- Descriptions referencing “case,” “verification,” or “safe account.”
- Rapid withdrawals or inter-account transfers following large credits.
- Sudden surges in international transfers from previously dormant accounts.
2. KYC/CDD Risk Indicators
- Recently opened accounts with minimal transaction history receiving large inflows.
- Personal accounts funnelling funds through multiple unrelated third parties.
- Connections to high-risk jurisdictions or crypto exchanges.
3. Customer Behaviour Red Flags
- Customers reporting that police or embassy officials instructed them to move funds.
- Individuals appearing fearful, rushed, or evasive when explaining transfer reasons.
- Seniors or migrants suddenly sending large sums overseas without clear purpose.
When combined, these signals form the behavioural typologies that transaction-monitoring systems must be trained to identify in real time.
Regulatory and Industry Response
Authorities across Australia have intensified efforts to disrupt the networks enabling such scams:
- Australian Federal Police (AFP): Launched dedicated taskforces to trace mule accounts and intercept funds mid-transfer.
- Australian Competition and Consumer Commission (ACCC): Through Scamwatch, continues to warn consumers about escalating impersonation scams.
- Financial Institutions: Major banks are now introducing confirmation-of-payee systems and inbound-payment monitoring to flag suspicious deposits before funds are moved onward.
- Cross-Border Coordination: Collaboration with ASEAN financial-crime units has strengthened typology sharing and asset-recovery efforts for transnational cases.
Despite progress, the challenge remains scale — scams evolve faster than traditional manual detection methods. The solution lies in shared intelligence and adaptive technology.
How Tookitaki Strengthens Defences
Tookitaki’s ecosystem of AI-driven compliance tools directly addresses these evolving, multi-channel threats.
1. AFC Ecosystem: Shared Typologies for Faster Detection
The AFC Ecosystem aggregates real-world scenarios contributed by compliance professionals worldwide.
Typologies covering impersonation, coercion, and extortion scams help financial institutions across Australia and Asia detect similar behavioural patterns early.
2. FinCense: Scenario-Driven Monitoring
FinCense operationalises these typologies into live detection rules. It can flag:
- Victim-to-mule account flows linked to extortion scams.
- Rapid outbound transfers inconsistent with customer behaviour.
- Multi-channel layering patterns across bank and fintech rails.
Its federated-learning architecture allows institutions to learn collectively from global patterns without exposing customer data — turning local insight into regional strength.
3. FinMate: AI Copilot for Investigations
FinMate, Tookitaki’s investigation copilot, connects entities across multiple transactions, surfaces hidden relationships, and auto-summarises alert context.
This empowers compliance teams to act before funds disappear, drastically reducing investigation time and false positives.
4. The Trust Layer
Together, Tookitaki’s systems form The Trust Layer — an integrated framework of intelligence, AI, and collaboration that protects the integrity of financial systems and restores confidence in every transaction.
Conclusion: From Fear to Trust
The live-stream extortion scam in Australia exposes how digital manipulation has entered a new frontier — one where fraudsters don’t just deceive victims, they control them.
For individuals, the impact is devastating. For financial institutions, it’s a wake-up call to detect emotional-behavioural anomalies before they translate into cross-border fund flows.
Prevention now depends on collaboration: between banks, regulators, fintechs, and technology partners who can turn intelligence into action.
With platforms like FinCense and the AFC Ecosystem, Tookitaki helps transform fragmented detection into coordinated defence — ensuring trust remains stronger than fear.
Because when fraud thrives on control, the answer lies in intelligence that empowers.
Eliminating AI Hallucinations in Financial Crime Detection: A Governance-First Approach
Introduction: When AI Makes It Up — The High Stakes of “Hallucinations” in AML
This is the third instalment in our series, Governance-First AI Strategy: The Future of Financial Crime Detection.
- In Part 1, we explored the governance crisis created by compliance-heavy frameworks.
- In Part 2, we highlighted how Singapore’s AI Verify program is pioneering independent validation as the new standard.
In this post, we turn to one of the most urgent challenges in AI-driven compliance: AI hallucinations.
Imagine an AML analyst starting their day, greeted by a queue of urgent alerts. One, flagged as “high risk,” is generated by the newest AI tool. But as the analyst investigates, it becomes clear that some transactions cited by the AI never actually happened. The explanation, while plausible, is fabricated: a textbook case of AI hallucination.
Time is wasted. Trust in the AI system is shaken. And worse, while chasing a phantom, a genuine criminal scheme may slip through.
As artificial intelligence becomes the core engine for financial crime detection, the problem of hallucinations, outputs not grounded in real data or facts, poses a serious threat to compliance, regulatory trust, and operational efficiency.
What Are AI Hallucinations and Why Are They So Risky in Finance?
AI hallucinations occur when a model produces statements or explanations that sound correct but are not grounded in real data.
In financial crime compliance, this can lead to:
- Wild goose chases: Analysts waste valuable time chasing non-existent threats.
- Regulatory risk: Fabricated outputs increase the chance of audit failures or penalties.
- Customer harm: Legitimate clients may be incorrectly flagged, damaging trust and relationships.
Generative AI systems are especially vulnerable. Designed to create coherent responses, they can unintentionally invent entire scenarios. In finance, where every “fact” matters to reputations, livelihoods, and regulatory standing, there is no room for guesswork.
Why Do AI Hallucinations Happen?
The drivers are well understood:
- Gaps or bias in training data: Incomplete or outdated records force models to “fill in the blanks” with speculation.
- Overly creative design: Generative models excel at narrative-building but can fabricate plausible-sounding explanations without constraints.
- Ambiguous prompts or unchecked logic: Vague inputs encourage speculation, diverting the model from factual data.
Real-World Misfire: A Costly False Alarm
At a large bank, an AI-powered monitoring tool flagged accounts for “suspicious round-dollar transactions,” producing a detailed narrative about potential laundering.
The problem? Those transactions never occurred.
The AI had hallucinated the explanation, stitching together fragments of unrelated historical data. The result: a week-long audit, wasted resources, and an urgent reminder of the need for stronger governance over AI outputs.
A Governance-First Playbook to Stop Hallucinations
Forward-looking compliance teams are embedding anti-hallucination measures into their AI governance frameworks. Key practices include:
1. Rigorous, Real-World Model Training
AI models must be trained on thousands of verified AML cases, including edge cases and emerging typologies. Exposure to operational complexity reduces speculative outputs.At Tookitaki, scenario-driven drills such as deepfake scam simulations and laundering typologies continuously stress-test the system to identify risks before they reach investigators or regulators.
2. Evidence-Based Outputs, Not Vague Alerts
Traditional systems often produce alerts like: “Possible layering activity detected in account X.” Analysts are left to guess at the reasoning.Governance-first systems enforce data-anchored outputs:“Layering risk detected: five transactions on 20/06/25 match FATF typology #3. See attached evidence.”
This creates traceable, auditable insights, building efficiency and trust.
3. Human-in-the-Loop (HITL) Validation
Even advanced models require human oversight. High-stakes outputs, such as risk narratives or new typology detections, must pass through expert validation.At Tookitaki, HITL ensures:
- Analytical transparency
- Reduced false positives
- No unexplained “black box” reasoning
4. Prompt Engineering and Retrieval-Augmented Generation (RAG)Ambiguity invites hallucinations. Precision prompts, combined with RAG techniques, ensure outputs are tied to verified databases and transaction logs, making fabrication nearly impossible.
Spotlight: Tookitaki’s Precision-First AI Philosophy
Tookitaki’s compliance platform is built on a governance-first architecture that treats hallucination prevention as a measurable objective.
- Scenario-Driven Simulations: Rare typologies and evolving crime patterns are continuously tested to surface potential weaknesses before deployment.
- Community-Powered Validation: Detection logic is refined in real time through feedback from a global network of financial crime experts.
- Mandatory Fact Citations: Every AI-generated narrative is backed by case data and audit references, accelerating compliance reviews and strengthening regulatory confidence.
At Tookitaki, we recognise that no AI system can be infallible. As leading research highlights, some real-world questions are inherently unanswerable. That is why our goal is not absolute perfection, but precision-driven AI that makes hallucinations statistically negligible and fully traceable — delivering factual integrity at scale.
Conclusion: Factual Integrity Is the Foundation of Trust
Eliminating hallucinations is not just a technical safeguard. It is a governance imperative. Compliance teams that embed evidence-based outputs, rigorous training, human-in-the-loop validation, and retrieval-anchored design will not only reduce wasted effort but also strengthen regulatory confidence and market reputation.
Key Takeaways from Part 3:
- AI hallucinations erode trust, waste resources, and expose firms to regulatory risk.
- Governance-first frameworks prevent hallucinations by enforcing evidence-backed, auditable outputs.
- Zero-hallucination AI is not optional. It is the foundation of responsible financial crime detection.
Are you asking your AI to show its data?
If not, you may be chasing ghosts.
In the next blog, we will explore how building an integrated, agentic AI strategy, linking model creation to real-time risk detection, can shift compliance from reactive to resilient.
BSP Proposes Tougher Penalties for Reporting Lapses: What Payment Operators Need to Know
The payments landscape in the Philippines has transformed rapidly in recent years. Digital payments now account for more than half of all retail transactions in the country, and uptake continues to grow as consumers and businesses turn to mobile wallets, online transfers, QR payments, and instant fund movements.
This shift has also brought new expectations from regulators. As digital transactions scale, the integrity of data, the accuracy of reporting, and the ability of payment system operators to maintain strong compliance controls have become non negotiable. The Bangko Sentral ng Pilipinas (BSP) has repeatedly emphasised that a safe and reliable digital payments ecosystem requires timely and accurate regulatory submissions.
This is the backdrop of the BSP’s newly proposed penalty framework for reporting lapses among payment system operators. It is a significant development. The proposal introduces daily monetary penalties for inaccurate or late submissions, along with potential non monetary sanctions for responsible officers. While the circular is still open for industry comments, its message is clear. Reporting lapses are no longer administrative oversights. They are operational weaknesses that can create systemic risk.
This blog unpacks what the proposal means, why it matters, and how financial institutions can strengthen their compliance and reporting environment in preparation for a more stringent regulatory era.
Why BSP Is Tightening Its Penalty Framework
The Philippines payments environment has seen rapid adoption of digital technologies, driven by financial inclusion goals and customer expectations for speed and convenience. With this acceleration comes a larger volume of data that financial institutions must capture, analyse, and report to regulators.
Several factors explain why BSP is moving towards stricter penalties:
1. Reporting is foundational to systemic stability
Regulators rely on accurate data to assess risks in the payment system. Gaps, inaccuracies, or delays can compromise oversight and create blind spots in areas such as liquidity flows, settlement patterns, operational disruptions, fraud, and unusual transaction activity.
2. Growth of non bank players
Many payment functions are now driven by fintechs, payment service providers, and other non bank operators. While this innovation expands access, it also requires a higher level of supervisory vigilance.
3. Increasing use of instant payments
With real real time payment channels becoming mainstream, reporting integrity becomes more critical. A single faulty dataset can affect risk assessments across multiple institutions.
4. Rise in financial crime and operational risk
Fraud, mule activity, phishing, account takeovers, and cross border scams have all increased. Accurate reporting helps regulators track patterns and intervene quickly.
5. Alignment with data governance expectations globally
Across ASEAN and beyond, regulators are raising standards for data quality, governance, and reporting. BSP’s proposal follows this global trend.
In short, accurate reporting is no longer just compliance housekeeping. It is central to maintaining trust and stability in a digital financial system.
What the BSP’s Proposed Penalty Framework Includes
The draft circular introduces several new enforcement mechanisms that significantly raise the stakes for reporting lapses.
1. Daily monetary penalties
Instead of one time fines, penalties may accrue daily until the issue is corrected. The amounts vary by institution type:
- Large banks: up to PHP 3,000 per day
- Digital banks: up to PHP 2,000 per day
- Thrift banks: up to PHP 1,500 per day
- Rural and cooperative banks: PHP 450 per day
- Non bank payment system operators: up to PHP 1,000 per day
These penalties apply after the first resubmission window. If the revised report still fails to meet BSP’s standards, the daily penalty starts accumulating.
2. Potential non monetary sanctions
Beyond fines, responsible directors or officers may face:
- Suspension
- Disqualification
- Other administrative measures
This signals that reporting lapses are now viewed as governance failures, not just operational issues.
3. Covers accuracy, completeness, and timeliness
Reporting lapses include:
- Late submissions
- Incorrect data
- Missing fields
- Inconsistent formatting
- Incomplete reports
BSP is emphasising the importance of end to end data integrity.
4. Applies to all payment system operators
This includes banks and non bank entities engaged in:
- E wallets
- Remittance services
- Payment gateways
- Digital payment rails
- Card networks
- Clearing and settlement participants
The message is clear. Every participant in the payments ecosystem has a responsibility to ensure accurate reporting.
Why Reporting Lapses Are Becoming a Serious Compliance Risk
Reporting lapses may seem minor compared to fraud, AML breaches, or cybersecurity threats. However, in a digital financial system, they can trigger serious operational and reputational consequences.
1. Reporting inaccuracies can mask suspicious patterns
Poor quality data can hide indicators of financial crime, mule activity, unusual flows, or cross channel fraud.
2. Delays affect systemic risk monitoring
In real time payments, regulators need timely data to detect anomalies and protect end users.
3. Data discrepancies create regulatory red flags
Repeated corrections or inconsistencies may suggest weak controls, insufficient oversight, or internal process failures.
4. Poor reporting signals weak operational governance
BSP views reporting as a reflection of an institution’s internal controls, risk management capability, and overall compliance culture.
5. Reputational risk for institutions
Long term credibility with regulators is tied to consistent compliance performance.
In environments like the Philippines, where digital adoption is growing quickly, institutions that fall behind on reporting standards face increasing supervisory pressure.
How Payment Operators Can Strengthen Their Reporting Framework
To operate confidently in this environment, organisations need strong internal processes, data governance frameworks, and technology that supports accurate, timely reporting.
Here are key steps financial institutions can take.
1. Strengthen internal governance for reporting
Institutions should formalise clear roles and ownership for reporting accuracy, including:
- Defined reporting workflows
- Documented data lineage
- Internal sign offs before submission
- Review and escalation protocols
- Consistent internal audit coverage
Treating reporting as a governance function rather than a technical task helps reduce errors.
2. Improve data quality controls
Reporting issues often stem from weak data foundations. Institutions should invest in:
- Data validation at source
- Automated quality checks
- Consistency rules across systems
- Deduplication and formatting controls
- Stronger reconciliation processes
Accurate reporting starts with clean, validated data.
3. Reduce manual dependencies
Manual processing increases the risk of:
- Typos
- Formatting errors
- Wrong values
- Missing fields
- Late submissions
Automation can significantly improve accuracy and speed.
4. Establish real time monitoring for data readiness
Real time payments require real time visibility. Institutions should build dashboards that track:
- Submission deadlines
- Pending validations
- Data anomalies
- Report generation status
- Submission completeness
Proactive monitoring helps prevent last minute errors.
5. Build a reporting culture
Compliance culture is not limited to the AML or risk team. Reporting accuracy must be part of the organisation’s broader mindset.
This includes:
- Leadership awareness
- Cross functional coordination
- Regular staff training
- Internal awareness of BSP standards
A strong culture reduces repeat errors and supports sustainable compliance.
Where Technology Plays a Transformative Role
Payment operators in the Philippines face growing expectations from regulators, customers, and partners. Manual systems will struggle to keep pace with the increasing volume, speed, and complexity of payments and reporting requirements.
Advanced compliance technology offers significant advantages in this environment.
1. Automated data validation and enrichment
Technology can continuously clean, check, and normalise data, reducing errors at source.
2. Stronger reporting accuracy with AI powered checks
Modern systems detect anomalies and provide real time alerts before submission.
3. Integrated risk and reporting environment
Unified platforms reduce fragmentation, helping ensure data consistency across AML, payments, and reporting functions.
4. Faster submission cycles
Automated generation and submission reduce operational delays.
5. Lower compliance cost per transaction
Technology reduces manual dependency and improves investigator productivity.
This is where Tookitaki’s approach provides strong value to institutions in the Philippines.
How Tookitaki Helps Strengthen Reporting and Compliance in the Philippines
Tookitaki supports financial institutions through a combination of its Trust Layer, federated intelligence, and advanced compliance platform, FinCense. These capabilities help institutions reduce reporting lapses and elevate overall governance.
Importantly, several leading digital financial institutions in the Philippines already work with Tookitaki to strengthen their AML and compliance foundations. Customers like Maya and PayMongo use Tookitaki solutions to build cleaner data pipelines, enhance risk analysis, and maintain strong reporting resilience in a rapidly evolving regulatory environment.
1. FinCense improves data integrity and monitoring
FinCense provides automated data checks, risk analysis, and validation across AML, fraud, and compliance domains. This ensures that institutions operate with cleaner and more accurate datasets, which flow directly into reporting.
2. Agentic AI enhances investigation quality
Tookitaki’s AI powered investigation tools help identify inconsistencies, suspicious patterns, or data gaps early. This reduces the risk of incorrect reporting and strengthens audit readiness.
3. Better governance through the Trust Layer
Tookitaki’s Trust Layer enables consistency, transparency, and explainability across decisions and reporting. Institutions gain a clear record of how data is processed, how decisions are made, and how controls are applied.
4. Federated intelligence helps identify systemic risks
Through the AFC Ecosystem, member institutions benefit from shared insights on emerging typologies, reporting vulnerabilities, and financial crime risks. This community driven model enhances awareness and strengthens reporting standards.
5. Configurable reporting and audit tools
FinCense supports financial institutions with structured reporting exports, audit logs, and compliance dashboards that help generate accurate and complete reports aligned with regulatory expectations.
For organisations preparing for a tighter penalty regime, these capabilities help elevate reporting from reactive to proactive.
What This Regulatory Shift Means for the Future
The BSP’s proposed penalties are part of a larger trend shaping financial regulation:
1. Data governance is becoming a compliance priority
Institutions will need full visibility into where data comes from, how it is transformed, and who is responsible for each reporting field.
2. Expect more scrutiny on non banks
Fintechs and payment providers will face higher regulatory expectations as their role in the ecosystem grows.
3. Technology adoption will accelerate
Manual reporting processes will not scale. Institutions will need automation and advanced analytics to meet higher standards.
4. Reporting accuracy will influence regulatory trust
Organisations that demonstrate consistent accuracy will gain smoother interactions, fewer supervisory interventions, and more regulatory confidence.
5. Strong compliance will help drive competitive advantage
In the digital payments era, trust is a business asset. Institutions that demonstrate reliability and transparency will attract more customers and partners.
Conclusion
The BSP’s proposed penalty framework is more than a compliance update. It is a signal that the Philippines is strengthening its digital payments ecosystem and aligning financial regulation with global standards.
For payment system operators, the message is clear. Reporting lapses must be addressed through better governance, stronger data quality, and robust technology. Institutions that invest early will be better positioned to operate with confidence, reduce regulatory risk, and build long term trust with stakeholders.
Tookitaki remains committed to supporting financial institutions in the Philippines with advanced, trusted, and future ready compliance technology that strengthens reporting, reduces operational risk, and enhances governance across the payments ecosystem.
Trapped on Camera: Inside Australia’s Chilling Live-Stream Extortion Scam
Introduction: A Crime That Played Out in Real Time
It began like a scene from a psychological thriller — a phone call, a voice claiming to be law enforcement, and an accusation that turned an ordinary life upside down.
In mid-2025, an Australian nurse found herself ensnared in a chilling scam that spanned months and borders. Fraudsters posing as Chinese police convinced her she was implicated in a criminal investigation and demanded proof of innocence.
What followed was a nightmare: she was monitored through live-stream video calls, coerced into isolation, and ultimately forced to transfer over AU$320,000 through multiple accounts.
This was no ordinary scam. It was psychological imprisonment, engineered through fear, surveillance, and cross-border financial manipulation.
The “live-stream extortion scam,” as investigators later called it, revealed how far organised networks have evolved — blending digital coercion, impersonation, and complex laundering pipelines that exploit modern payment systems.
The Anatomy of the Scam
According to reports from Australian authorities and news.com.au, the scam followed a terrifyingly systematic pattern — part emotional manipulation, part logistical precision.
- Initial Contact – The victim received a call from individuals claiming to be from the Chinese Embassy in Canberra. They alleged that her identity had been used in a major crime.
- Transfer to ‘Police’ – The call was escalated to supposed Chinese police officers. These fraudsters used uniforms and badges in video calls, making the impersonation feel authentic.
- Psychological Entrapment – The victim was told she was under investigation and must cooperate to avoid arrest. She was ordered to isolate herself, communicate only via encrypted apps, and follow their “procedures.”
- The Live-Stream Surveillance – For weeks, scammers demanded she keep her webcam on for long hours daily so they could “monitor her compliance.” This tactic ensured she remained isolated, fearful, and completely controlled.
- The Transfers Begin – Under threat of criminal charges, she was instructed to transfer her savings into “safe accounts” for verification. Over AU$320,000 was moved in multiple transactions to mule accounts across the region.
By the time she realised the deception, the money had vanished through layers of transfers and withdrawals — routed across several countries within hours.
Why Victims Fall for It: The Psychology of Control
This scam wasn’t built on greed. It was built on fear and authority — two of the most powerful levers in human behaviour.
Four manipulation techniques stood out:
- Authority Bias – The impersonation of police officials leveraged fear of government power. Victims were too intimidated to question legitimacy.
- Isolation – By cutting victims off from family and friends, scammers removed all sources of doubt.
- Surveillance and Shame – Continuous live-stream monitoring reinforced compliance, making victims believe they were truly under investigation.
- Incremental Compliance – The fraudsters didn’t demand the full amount upfront. Small “verification transfers” escalated gradually, conditioning obedience.
What made this case disturbing wasn’t just the financial loss — but how it weaponised digital presence to achieve psychological captivity.
The Laundering Playbook: From Fear to Finance
Behind the emotional manipulation lay a highly organised laundering operation. The scammers moved funds with near-institutional precision.
- Placement – Victims deposited funds into local accounts controlled by money mules — individuals recruited under false pretences through job ads or online chats.
- Layering – Within hours, the funds were fragmented and channelled:
- Through fintech payment apps and remittance platforms with fast settlement speeds.
- Into business accounts of shell entities posing as logistics or consulting firms.
- Partially converted into cryptocurrency to obscure traceability.
- Integration – Once the trail cooled, the money re-entered legitimate financial channels through overseas investments and asset purchases.
This progression from coercion to laundering highlights why scams like this aren’t merely consumer fraud — they’re full-fledged financial crime pipelines that demand a compliance response.
A Broader Pattern Across the Region
The live-stream extortion scam is part of a growing web of cross-jurisdictional deception sweeping Asia-Pacific:
- Taiwan: Victims have been forced to record “confession videos” as supposed proof of innocence.
- Malaysia and the Philippines: Scam centres dismantled in 2025 revealed money-mule networks used to channel proceeds into offshore accounts.
- Australia: The Australian Federal Police continues to warn about rising “safe account” scams where victims are tricked into transferring funds to supposed law enforcement agencies.
The convergence of social engineering and real-time payments has created a fraud ecosystem where emotional manipulation and transaction velocity fuel each other.
Red Flags for Banks and Fintechs
Financial institutions sit at the frontline of disruption.
Here are critical red flags across transaction, customer, and behavioural levels:
1. Transaction-Level Indicators
- Multiple mid-value transfers to new recipients within short intervals.
- Descriptions referencing “case,” “verification,” or “safe account.”
- Rapid withdrawals or inter-account transfers following large credits.
- Sudden surges in international transfers from previously dormant accounts.
2. KYC/CDD Risk Indicators
- Recently opened accounts with minimal transaction history receiving large inflows.
- Personal accounts funnelling funds through multiple unrelated third parties.
- Connections to high-risk jurisdictions or crypto exchanges.
3. Customer Behaviour Red Flags
- Customers reporting that police or embassy officials instructed them to move funds.
- Individuals appearing fearful, rushed, or evasive when explaining transfer reasons.
- Seniors or migrants suddenly sending large sums overseas without clear purpose.
When combined, these signals form the behavioural typologies that transaction-monitoring systems must be trained to identify in real time.
Regulatory and Industry Response
Authorities across Australia have intensified efforts to disrupt the networks enabling such scams:
- Australian Federal Police (AFP): Launched dedicated taskforces to trace mule accounts and intercept funds mid-transfer.
- Australian Competition and Consumer Commission (ACCC): Through Scamwatch, continues to warn consumers about escalating impersonation scams.
- Financial Institutions: Major banks are now introducing confirmation-of-payee systems and inbound-payment monitoring to flag suspicious deposits before funds are moved onward.
- Cross-Border Coordination: Collaboration with ASEAN financial-crime units has strengthened typology sharing and asset-recovery efforts for transnational cases.
Despite progress, the challenge remains scale — scams evolve faster than traditional manual detection methods. The solution lies in shared intelligence and adaptive technology.
How Tookitaki Strengthens Defences
Tookitaki’s ecosystem of AI-driven compliance tools directly addresses these evolving, multi-channel threats.
1. AFC Ecosystem: Shared Typologies for Faster Detection
The AFC Ecosystem aggregates real-world scenarios contributed by compliance professionals worldwide.
Typologies covering impersonation, coercion, and extortion scams help financial institutions across Australia and Asia detect similar behavioural patterns early.
2. FinCense: Scenario-Driven Monitoring
FinCense operationalises these typologies into live detection rules. It can flag:
- Victim-to-mule account flows linked to extortion scams.
- Rapid outbound transfers inconsistent with customer behaviour.
- Multi-channel layering patterns across bank and fintech rails.
Its federated-learning architecture allows institutions to learn collectively from global patterns without exposing customer data — turning local insight into regional strength.
3. FinMate: AI Copilot for Investigations
FinMate, Tookitaki’s investigation copilot, connects entities across multiple transactions, surfaces hidden relationships, and auto-summarises alert context.
This empowers compliance teams to act before funds disappear, drastically reducing investigation time and false positives.
4. The Trust Layer
Together, Tookitaki’s systems form The Trust Layer — an integrated framework of intelligence, AI, and collaboration that protects the integrity of financial systems and restores confidence in every transaction.
Conclusion: From Fear to Trust
The live-stream extortion scam in Australia exposes how digital manipulation has entered a new frontier — one where fraudsters don’t just deceive victims, they control them.
For individuals, the impact is devastating. For financial institutions, it’s a wake-up call to detect emotional-behavioural anomalies before they translate into cross-border fund flows.
Prevention now depends on collaboration: between banks, regulators, fintechs, and technology partners who can turn intelligence into action.
With platforms like FinCense and the AFC Ecosystem, Tookitaki helps transform fragmented detection into coordinated defence — ensuring trust remains stronger than fear.
Because when fraud thrives on control, the answer lies in intelligence that empowers.
Eliminating AI Hallucinations in Financial Crime Detection: A Governance-First Approach
Introduction: When AI Makes It Up — The High Stakes of “Hallucinations” in AML
This is the third instalment in our series, Governance-First AI Strategy: The Future of Financial Crime Detection.
- In Part 1, we explored the governance crisis created by compliance-heavy frameworks.
- In Part 2, we highlighted how Singapore’s AI Verify program is pioneering independent validation as the new standard.
In this post, we turn to one of the most urgent challenges in AI-driven compliance: AI hallucinations.
Imagine an AML analyst starting their day, greeted by a queue of urgent alerts. One, flagged as “high risk,” is generated by the newest AI tool. But as the analyst investigates, it becomes clear that some transactions cited by the AI never actually happened. The explanation, while plausible, is fabricated: a textbook case of AI hallucination.
Time is wasted. Trust in the AI system is shaken. And worse, while chasing a phantom, a genuine criminal scheme may slip through.
As artificial intelligence becomes the core engine for financial crime detection, the problem of hallucinations, outputs not grounded in real data or facts, poses a serious threat to compliance, regulatory trust, and operational efficiency.
What Are AI Hallucinations and Why Are They So Risky in Finance?
AI hallucinations occur when a model produces statements or explanations that sound correct but are not grounded in real data.
In financial crime compliance, this can lead to:
- Wild goose chases: Analysts waste valuable time chasing non-existent threats.
- Regulatory risk: Fabricated outputs increase the chance of audit failures or penalties.
- Customer harm: Legitimate clients may be incorrectly flagged, damaging trust and relationships.
Generative AI systems are especially vulnerable. Designed to create coherent responses, they can unintentionally invent entire scenarios. In finance, where every “fact” matters to reputations, livelihoods, and regulatory standing, there is no room for guesswork.
Why Do AI Hallucinations Happen?
The drivers are well understood:
- Gaps or bias in training data: Incomplete or outdated records force models to “fill in the blanks” with speculation.
- Overly creative design: Generative models excel at narrative-building but can fabricate plausible-sounding explanations without constraints.
- Ambiguous prompts or unchecked logic: Vague inputs encourage speculation, diverting the model from factual data.
Real-World Misfire: A Costly False Alarm
At a large bank, an AI-powered monitoring tool flagged accounts for “suspicious round-dollar transactions,” producing a detailed narrative about potential laundering.
The problem? Those transactions never occurred.
The AI had hallucinated the explanation, stitching together fragments of unrelated historical data. The result: a week-long audit, wasted resources, and an urgent reminder of the need for stronger governance over AI outputs.
A Governance-First Playbook to Stop Hallucinations
Forward-looking compliance teams are embedding anti-hallucination measures into their AI governance frameworks. Key practices include:
1. Rigorous, Real-World Model Training
AI models must be trained on thousands of verified AML cases, including edge cases and emerging typologies. Exposure to operational complexity reduces speculative outputs.At Tookitaki, scenario-driven drills such as deepfake scam simulations and laundering typologies continuously stress-test the system to identify risks before they reach investigators or regulators.
2. Evidence-Based Outputs, Not Vague Alerts
Traditional systems often produce alerts like: “Possible layering activity detected in account X.” Analysts are left to guess at the reasoning.Governance-first systems enforce data-anchored outputs:“Layering risk detected: five transactions on 20/06/25 match FATF typology #3. See attached evidence.”
This creates traceable, auditable insights, building efficiency and trust.
3. Human-in-the-Loop (HITL) Validation
Even advanced models require human oversight. High-stakes outputs, such as risk narratives or new typology detections, must pass through expert validation.At Tookitaki, HITL ensures:
- Analytical transparency
- Reduced false positives
- No unexplained “black box” reasoning
4. Prompt Engineering and Retrieval-Augmented Generation (RAG)Ambiguity invites hallucinations. Precision prompts, combined with RAG techniques, ensure outputs are tied to verified databases and transaction logs, making fabrication nearly impossible.
Spotlight: Tookitaki’s Precision-First AI Philosophy
Tookitaki’s compliance platform is built on a governance-first architecture that treats hallucination prevention as a measurable objective.
- Scenario-Driven Simulations: Rare typologies and evolving crime patterns are continuously tested to surface potential weaknesses before deployment.
- Community-Powered Validation: Detection logic is refined in real time through feedback from a global network of financial crime experts.
- Mandatory Fact Citations: Every AI-generated narrative is backed by case data and audit references, accelerating compliance reviews and strengthening regulatory confidence.
At Tookitaki, we recognise that no AI system can be infallible. As leading research highlights, some real-world questions are inherently unanswerable. That is why our goal is not absolute perfection, but precision-driven AI that makes hallucinations statistically negligible and fully traceable — delivering factual integrity at scale.
Conclusion: Factual Integrity Is the Foundation of Trust
Eliminating hallucinations is not just a technical safeguard. It is a governance imperative. Compliance teams that embed evidence-based outputs, rigorous training, human-in-the-loop validation, and retrieval-anchored design will not only reduce wasted effort but also strengthen regulatory confidence and market reputation.
Key Takeaways from Part 3:
- AI hallucinations erode trust, waste resources, and expose firms to regulatory risk.
- Governance-first frameworks prevent hallucinations by enforcing evidence-backed, auditable outputs.
- Zero-hallucination AI is not optional. It is the foundation of responsible financial crime detection.
Are you asking your AI to show its data?
If not, you may be chasing ghosts.
In the next blog, we will explore how building an integrated, agentic AI strategy, linking model creation to real-time risk detection, can shift compliance from reactive to resilient.