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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Busted in Bangsar South: Inside Malaysia’s Largest Scam Call Centre Raid
In August 2025, Malaysian police stormed a five-storey office in Bangsar South, Kuala Lumpur, arresting more than 400 people linked to what is now called the country’s largest scam call centre operation.
The raid made headlines worldwide, not only for its scale but also because of its alleged link to Doo Group, a Singapore-based fintech that sponsors English football giant Manchester United. The case has cast a harsh spotlight on the industrial scale of financial crime in Southeast Asia and the reputational risks it poses for both financial institutions and global brands.
Background of the Scam
The dramatic raid took place on 26 August 2025, when Malaysian authorities swept into a commercial tower in Bangsar South, a thriving business district in Kuala Lumpur. Inside, they discovered a massive call centre allegedly set up to defraud victims across multiple countries.
Over 400 individuals were arrested. Videos of employees being escorted into police vans quickly went viral, symbolising the scale and industrial nature of the operation.
Initial reports linked the call centre to Doo Group, a global financial services provider with operations across Singapore, Hong Kong, London, Sydney, and Dubai. While the company has insisted that its operations remain unaffected and that it is cooperating fully with investigators, the reputational damage was already significant.
The Bangsar South raid is part of Malaysia’s wider anti-scam campaign. By mid-2025, authorities had arrested over 11,800 suspects in similar cases, with financial losses amounting to RM 1.5 billion (USD 355 million). The Bangsar South case, however, stands out because of its size, its international profile, and its link to a company with a global brand presence.
What the Case Revealed
The raid revealed troubling insights into how financial crime networks operate in the region:
1. Industrialised Fraud
A workforce of over 400 suggests this was not a small, fly-by-night scam but a structured enterprise. Staff were reportedly trained to follow scripts, handle objections, and target victims methodically, mirroring the efficiency of legitimate customer service operations.
2. Global Targeting
Reports indicate the call centre targeted victims not just in Malaysia but also overseas, raising questions about how funds were laundered across borders. The multilingual capabilities of employees further suggest international reach.
3. Reputation at Risk
The alleged connection to Doo Group highlights how reputable financial companies can be pulled into fraud narratives. Even if not directly complicit, the association underscores how thin the line can be between legitimate fintech operations and the shadow economy.
4. Oversight Gaps
The case also points to challenges regulators face in monitoring sprawling call centre operations and cross-border financial flows. By the time raids occur, thousands of victims may already have been defrauded.
Impact on Financial Institutions and Corporates
The Bangsar South raid is not just a law enforcement victory. It is a warning signal for the financial industry.
1. Reputational Fallout
When a Manchester United sponsor is linked to scams, it is not just the company that suffers. Brand trust in fintech, sports, and banking becomes collateral damage. This raises the stakes for due diligence in sponsorships and partnerships.
2. Investor and Customer Confidence
Digital finance thrives on trust. When fintechs are tied to scandals, investors hesitate and customers second-guess their safety. The Bangsar South case risks dampening enthusiasm for fintech adoption in Malaysia and the wider region.
3. Operational Risks for Banks
For financial institutions, call centre scams translate into suspicious transaction flows, mule account proliferation, and higher compliance costs. Traditional transaction monitoring often struggles to flag layered, cross-border flows connected to scams of this scale.
4. Regional Implications
Malaysia’s crackdown shows commendable resolve, but it also exposes the country as a hub for organised scam activity. This dual image, both a problem centre and an enforcement leader, will shape how regional regulators approach financial crime.
Lessons Learned from the Scam
- Scale ≠ Legitimacy
A large workforce and polished infrastructure do not guarantee a legitimate business. Regulators and partners must look beyond appearances. - Due Diligence is Non-Negotiable
Global brands and institutions need deeper checks before partnerships. A sponsorship or corporate tie-up can quickly become a reputational liability. - Regulatory Vigilance Matters
The Bangsar South raid shows what decisive enforcement looks like, but it also reveals how long such scams can operate before being stopped. - Cross-Border Cooperation is Critical
Victims were likely spread across multiple jurisdictions. Without international collaboration, enforcement remains reactive. - Public Awareness is Essential
Scam call centres thrive because victims are unaware. Public education campaigns must go hand-in-hand with enforcement.
The Role of Technology in Prevention
Conventional compliance methods, such as simple blacklist checks or static rules, are no match for scam call centres operating at an industrial scale. To counter them, financial institutions need adaptive, intelligence-driven defences.
This is where Tookitaki’s FinCense and the AFC Ecosystem come in:
- Typology-Driven Detection
FinCense continuously updates detection logic based on real scam scenarios contributed by 200+ global financial crime experts in the AFC Ecosystem. This means emerging call centre scam patterns can be identified faster. - Agentic AI
At the heart of FinCense is an Agentic AI framework, a network of intelligent agents that not only detect suspicious activity but also explain every decision in plain language. This reduces investigation time and builds regulator confidence. - Federated Learning
Through federated learning, FinCense enables banks to share insights on scam flows and mule account behaviours without compromising sensitive data. It is collective intelligence at scale. - Smart Case Disposition
When alerts are triggered, FinCense’s Agentic AI generates natural-language summaries, helping investigators prioritise critical cases quickly and accurately.
Moving Forward: The Future of Scam Call Centres
The Bangsar South raid may have shut down one operation, but the fight against scam call centres is far from over. As enforcement improves, fraudsters will adopt AI-driven tools, deepfake impersonations, and more sophisticated laundering methods.
For financial institutions, the path forward is clear:
- Strengthen collaboration with regulators and peers to track cross-border scam flows.
- Invest in adaptive technology like FinCense to stay ahead of criminal innovation.
- Educate customers relentlessly about new fraud tactics.
The raid was a victory, but it was also a warning.
If one call centre with 400 employees can operate in plain sight, imagine how many others remain hidden. The only safe strategy for financial institutions is to stay one step ahead with collaboration, intelligence, and next-generation technology.
Inside the New Payments Platform (NPP): How Australia’s Real-Time Payments Are Changing Finance and Fraud
Australia’s real-time payments revolution is reshaping finance, but it also brings new risks and compliance challenges.
Imagine sending money to a friend, paying a bill, or receiving your salary in seconds, no matter the day or hour. That vision became reality in Australia with the launch of the New Payments Platform (NPP) in 2018. Since then, the NPP has transformed how Australians transact, powering faster, smarter, and more flexible payments.
But while the benefits are undeniable, the NPP has also introduced fresh risks. Fraudsters and money launderers now exploit the speed of real-time payments, forcing banks, fintechs, and regulators to rethink how they approach compliance. In this blog, we take a deep look at the NPP, exploring its origins, features, benefits, risks, and what the future holds.
What is the New Payments Platform (NPP)?
The NPP is Australia’s real-time payments infrastructure, designed to allow funds to be transferred between bank accounts in seconds. Unlike traditional bank transfers, which could take hours or days, the NPP settles payments instantly, around the clock, 365 days a year.
A Collaborative Effort
The NPP was launched in February 2018 as a collaborative initiative between the Reserve Bank of Australia (RBA), major banks, and key financial institutions. It was developed to modernise Australia’s payments infrastructure and to match the expectations of a digital-first economy.
Core Components of the NPP
- Fast Settlement Service (FSS): Operated by the RBA, this ensures transactions settle instantly across participating banks.
- Overlay Services: Products built on top of the NPP to offer tailored use cases, such as Osko by BPAY for fast peer-to-peer payments.
- PayID: A feature that allows customers to link easy identifiers such as email addresses or phone numbers to bank accounts for faster payments.
- ISO 20022 Data Standard: Enables rich data to travel with payments, improving transparency and reporting.
The NPP is not just a new payment rail. It is an entirely new ecosystem designed to support innovation, competition, and efficiency.
Key Features of the NPP
- Speed: Transactions settle in less than 60 seconds.
- Availability: Operates 24/7/365, unlike traditional settlement systems.
- Rich Data: ISO 20022 messaging allows businesses to include detailed payment references.
- Flexibility: Overlay services enable innovative new use cases, from consumer-to-business payments to government disbursements.
- Ease of Use: PayID removes the need for remembering BSB and account numbers.
Benefits of the NPP for Australia
1. Consumer Convenience
Everyday Australians can send and receive money instantly. Whether splitting a dinner bill or paying rent, transactions are seamless and fast.
2. Business Efficiency
Businesses benefit from faster supplier payments, real-time payroll, and improved cash flow management. For SMEs, this reduces dependency on costly credit.
3. Government Services
Government agencies can issue refunds, grants, and welfare payments in real time, improving citizen experience and efficiency.
4. Financial Inclusion and Innovation
The NPP creates opportunities for fintechs to build new payment products and services, driving competition and giving consumers more choice.
5. Enhanced Transparency
The rich data standards improve reconciliation and reduce errors, saving time and cost for businesses.
The Risks and Challenges of Real-Time Payments
As with any innovation, the NPP comes with challenges. The very features that make it attractive to consumers also make it attractive to fraudsters and money launderers.
1. Authorised Push Payment (APP) Scams
Fraudsters use social engineering to trick customers into sending money themselves. Because NPP payments are instant, victims often cannot recover funds once transferred.
2. Money Mule Networks
Criminals exploit mule accounts to move illicit funds quickly. Dormant accounts or those opened with synthetic identities are often used as conduits.
3. Increased Operational Pressure
Compliance teams that once had hours to review suspicious transactions now have seconds. This shift requires entirely new approaches to monitoring.
4. False Positives and Noise
Traditional systems generate vast numbers of false positives, which overwhelm investigators. With NPP volumes growing, this problem is magnified.
5. Cyber and Identity Risks
Fraudsters use phishing, malware, and stolen credentials to take over accounts and push funds instantly.
Regulatory and Industry Response
Australian regulators have moved swiftly to address these risks.
- AUSTRAC: Expects banks and payment providers to implement effective real-time monitoring and suspicious matter reporting tailored to NPP risks.
- ASIC: Focuses on consumer protection and ensuring victims of scams are treated fairly.
- Industry Initiatives: The Australian Banking Association has been working on scam-reporting frameworks and shared fraud detection systems across banks.
- Government Action: Proposals to make banks reimburse scam victims are under consideration, following models in the UK.
The message is clear: institutions must invest in smarter compliance and fraud prevention tools.
Fraud and AML in the NPP Era
Why Legacy Systems Fall Short
Legacy monitoring systems were built for batch processing. They cannot keep up with the millisecond-level requirements of real-time payments. By the time a suspicious transaction is flagged, the funds are gone.
What Next-Gen Solutions Look Like
Modern systems use AI and machine learning to:
- Detect anomalies in real time.
- Link suspicious activity across accounts, devices, and geographies.
- Reduce false positives by learning from investigator feedback.
- Provide regulator-ready explanations for every alert.
Key Fraud Red Flags in NPP Transactions
- Large transfers to newly created accounts.
- Multiple small payments designed to avoid thresholds.
- Sudden changes in device or login behaviour.
- Beneficiaries in high-risk jurisdictions.
- Rapid pass-through activity with no balance retention.
Spotlight on Technology: Tookitaki’s Role
As the risks around NPP accelerate, technology providers are stepping up. Tookitaki’s FinCense is purpose-built for the demands of real-time payments.
How FinCense Helps
- Real-Time Monitoring: Detects suspicious activity in milliseconds.
- Agentic AI: Continuously adapts to new scam typologies, reducing false positives.
- Federated Intelligence: Accesses insights from the AFC Ecosystem, a global compliance community, while preserving privacy.
- FinMate AI Copilot: Assists investigators with summaries, recommendations, and regulator-ready narratives.
- AUSTRAC-Ready Compliance: Built-in reporting for SMRs, TTRs, and detailed audit trails.
Local Adoption
FinCense is already being used by community-owned banks like Regional Australia Bank and Beyond Bank. These partnerships demonstrate that even mid-sized institutions can meet AUSTRAC’s expectations while delivering excellent customer experiences.
The Future of NPP in Australia
The NPP is still evolving. Several developments will shape its future:
1. PayTo Expansion
PayTo, a digital alternative to direct debit, is gaining traction. It allows consumers to authorise payments directly from their accounts, offering flexibility but also new fraud vectors.
2. Cross-Border Potential
Future integration with Asia-Pacific payment systems could expand NPP beyond Australia, increasing both opportunities and risks.
3. Smarter Fraud Typologies
Criminals are already exploring ways to exploit deepfake technology, synthetic identities, and AI-driven scams. Fraud prevention must evolve just as quickly.
4. Industry Collaboration
Expect stronger cooperation between banks, fintechs, regulators, and technology vendors. Shared fraud databases and federated intelligence models will be crucial.
Conclusion
The New Payments Platform has reshaped Australia’s payments landscape. It delivers speed, convenience, and innovation that benefit consumers, businesses, and government agencies. But with opportunity comes risk.
Fraudsters have been quick to exploit the instant nature of NPP, forcing institutions to rethink how they detect and prevent financial crime. The solution lies in real-time, AI-powered monitoring platforms that adapt to new typologies and reduce compliance costs.
For Australian institutions, the NPP is more than a payment rail. It is the foundation of a new financial ecosystem. The winners will be those who embrace innovation, partner with the right AML vendors, and build trust through smarter compliance.
Pro tip: If your institution still relies on batch monitoring, you are already behind. Now is the time to modernise and future-proof your compliance with intelligent fraud and AML platforms.
Locked on Video: Inside India’s Chilling Digital Arrest Scam
It began with a phone call. A senior citizen in Navi Mumbai answered a number that appeared to belong to the police. Within hours, she was trapped on a video call with men in uniforms, accused of laundering money for terrorists. Terrified, she wired ₹21 lakh into what she believed was a government-controlled account.
She was not alone. In August 2025, cases of “digital arrest” scams surged across India. An elderly couple in Madhya Pradesh drained nearly ₹50 lakh of their life savings after spending 13 days under constant video surveillance by fraudsters posing as investigators. In Rajkot, criminals used the pretext of a real anti-terror operation to extort money from a student.
These scams are not crude phishing attempts. They are meticulously staged psychological operations, exploiting people’s deepest fears of authority and social disgrace. Victims are not tricked into handing over passwords. They are coerced, minute by minute, into making transfers themselves. The results are devastating, both for individuals and the wider financial system.
Background of the Scam
The anatomy of a digital arrest scam follows a chillingly consistent script.
1. The Call of Fear
Fraudsters begin with a phone call, often masked to resemble an official number. The caller claims the victim’s details have surfaced in a serious crime: drug trafficking, terror financing, or money laundering. The consequences are presented as immediate arrest, frozen accounts, or ruined reputations.
2. Escalation to Video
To heighten credibility, the fraudster insists on switching to a video call. Victims are connected to people wearing uniforms, holding forged identity cards, or even sitting before backdrops resembling police stations and courtrooms.
3. Isolation and Control
Once on video, the victim is told they cannot disconnect. In some cases, they are monitored round the clock, ordered not to use their phone for any purpose other than the call. Contact with family or friends is prohibited, under the guise of “confidential investigations.”
4. The Transfer of Funds
The victim is then directed to transfer money into so-called “secure accounts” to prove their innocence or pay bail. These accounts are controlled by criminals and serve as the first layer in complex laundering networks. Victims, believing they are cooperating with the law, empty fixed deposits, break retirement savings, and transfer sums that can take a lifetime to earn.
The method blends social engineering with coercive control. It is not the theft of data, but the hijacking of human behaviour.
What the Case Revealed
The 2025 wave of digital arrest scams in India exposed three critical truths about modern fraud.
1. Video Calls Are No Longer a Guarantee of Authenticity
For years, people considered video more secure than phone calls or emails. If you could see someone’s face, the assumption was that they were genuine. These scams demolished that trust. Fraudsters showed that live video, like written messages, can be staged, manipulated, and weaponised.
2. Authority Bias is a Fraudster’s Greatest Weapon
Humans are hardwired to respect authority, especially law enforcement. By impersonating police or investigators, criminals bypass the victim’s critical reasoning. Fear of prison or social disgrace outweighs logical checks.
3. Coercion Multiplies the Damage
Unlike phishing or one-time deceptions, digital arrests involve prolonged psychological manipulation. Victims are kept online for days, bombarded with threats and false evidence. Under this pressure, even cautious individuals break down. The results are not minor losses, but catastrophic financial wipe-outs.
4. Organised Networks Are Behind the Scenes
The professionalism and scale suggest syndicates, not lone operators. From forged documents to layered mule accounts, the fraud points to criminal hubs capable of running scripted operations across borders.
Impact on Financial Institutions and Corporates
Though victims are individuals, the implications extend far into the financial and corporate world.
1. Reputational Risk
When victims lose life savings through accounts within the banking system, they often blame their bank as much as the fraudster. Even if technically blameless, institutions suffer a hit to public trust.
2. Pressure on Fraud Systems
Digital arrest scams exploit authorised transactions. Victims themselves make the transfers. Traditional detection tools that focus on unauthorised access or password breaches cannot easily flag these cases.
3. Global Movement of Funds
Money from scams rarely stays local. Transfers are routed across borders within hours, layered through mule accounts, e-wallets, and fintech platforms. This complicates recovery and exposes gaps in international coordination.
4. Corporate Vulnerability
The threat is not limited to retirees or individuals. In Singapore earlier this year, a finance director was tricked into wiring half a million dollars during a deepfake board call. Digital arrest tactics could just as easily target corporate employees handling high-value transactions.
5. Regulatory Expectations
As scams multiply, regulators are pressing institutions to demonstrate stronger customer protections, more resilient monitoring, and greater collaboration. Failure to act risks not only reputational damage but also regulatory penalties.
Lessons Learned from the Scam
For Individuals
- Treat unsolicited calls from law enforcement with suspicion. Real investigations do not begin on the phone.
- Verify independently by calling the published numbers of agencies.
- Watch for signs of manipulation, such as demands for secrecy or threats of immediate arrest.
- Educate vulnerable groups, particularly senior citizens, about how these scams operate.
For Corporates
- Train employees, especially those in finance roles, to recognise coercion tactics.
- Require secondary verification for urgent, high-value transfers, especially when directed to new accounts.
- Encourage a speak-up culture where staff can challenge suspicious instructions without fear of reprimand.
For Financial Institutions
- Monitor for mule account activity. Unexplained inflows followed by rapid withdrawals are a red flag.
- Run customer awareness campaigns, explaining how digital arrest scams work.
- Share intelligence with peers and regulators to prevent repeat incidents across institutions.
The Role of Technology in Prevention
Digital arrest scams prove that traditional safeguards are insufficient. Fraudsters are not stealing credentials but manipulating behaviour. Prevention requires smarter, adaptive systems.
1. Behavioural Monitoring
Transactions made under duress often differ from normal patterns. Advanced analytics can detect anomalies, such as sudden large transfers from accounts with low historical activity.
2. Typology-Driven Detection
Platforms like Tookitaki’s FinCense leverage the AFC Ecosystem to encode real-world scam scenarios into detection logic. As digital arrest typologies are identified, they can be integrated quickly to improve monitoring.
3. AI-Powered Simulations
Institutions can run simulations of coercion-based scams to test whether their processes would withstand them. These exercises reveal gaps in escalation and verification controls.
4. Federated Learning for Collective Defence
With federated learning, insights from one bank can be shared across many without exposing sensitive data. If one institution sees a pattern in digital arrest cases, others can benefit almost instantly.
5. Smarter Alert Management
Agentic AI can review and narrate the context of alerts, allowing investigators to understand whether unusual activity stems from duress. This speeds up response times and prevents irreversible losses.
Conclusion
The digital arrest scam is not just a fraud. It is a form of psychological captivity, where victims are imprisoned through fear on their own devices. In 2025, India saw a surge of such cases, stripping people of their savings and shaking trust in digital communications.
The message is clear: scams no longer rely on technical breaches. They rely on exploiting human trust. For individuals, the defence is awareness and verification. For corporates, it is embedding strong protocols and encouraging a culture of questioning. For financial institutions, the challenge is profound. They must detect authorised transfers made under coercion, collaborate across borders, and deploy AI-powered defences that learn as fast as the criminals do.
If 2024 was the year of deepfake deception, 2025 is becoming the year of coercion-based fraud. The industry’s response will determine whether scams like digital arrests remain isolated tragedies or become a systemic crisis. Protecting trust is no longer optional. It is the frontline of financial crime prevention.
Busted in Bangsar South: Inside Malaysia’s Largest Scam Call Centre Raid
In August 2025, Malaysian police stormed a five-storey office in Bangsar South, Kuala Lumpur, arresting more than 400 people linked to what is now called the country’s largest scam call centre operation.
The raid made headlines worldwide, not only for its scale but also because of its alleged link to Doo Group, a Singapore-based fintech that sponsors English football giant Manchester United. The case has cast a harsh spotlight on the industrial scale of financial crime in Southeast Asia and the reputational risks it poses for both financial institutions and global brands.
Background of the Scam
The dramatic raid took place on 26 August 2025, when Malaysian authorities swept into a commercial tower in Bangsar South, a thriving business district in Kuala Lumpur. Inside, they discovered a massive call centre allegedly set up to defraud victims across multiple countries.
Over 400 individuals were arrested. Videos of employees being escorted into police vans quickly went viral, symbolising the scale and industrial nature of the operation.
Initial reports linked the call centre to Doo Group, a global financial services provider with operations across Singapore, Hong Kong, London, Sydney, and Dubai. While the company has insisted that its operations remain unaffected and that it is cooperating fully with investigators, the reputational damage was already significant.
The Bangsar South raid is part of Malaysia’s wider anti-scam campaign. By mid-2025, authorities had arrested over 11,800 suspects in similar cases, with financial losses amounting to RM 1.5 billion (USD 355 million). The Bangsar South case, however, stands out because of its size, its international profile, and its link to a company with a global brand presence.
What the Case Revealed
The raid revealed troubling insights into how financial crime networks operate in the region:
1. Industrialised Fraud
A workforce of over 400 suggests this was not a small, fly-by-night scam but a structured enterprise. Staff were reportedly trained to follow scripts, handle objections, and target victims methodically, mirroring the efficiency of legitimate customer service operations.
2. Global Targeting
Reports indicate the call centre targeted victims not just in Malaysia but also overseas, raising questions about how funds were laundered across borders. The multilingual capabilities of employees further suggest international reach.
3. Reputation at Risk
The alleged connection to Doo Group highlights how reputable financial companies can be pulled into fraud narratives. Even if not directly complicit, the association underscores how thin the line can be between legitimate fintech operations and the shadow economy.
4. Oversight Gaps
The case also points to challenges regulators face in monitoring sprawling call centre operations and cross-border financial flows. By the time raids occur, thousands of victims may already have been defrauded.
Impact on Financial Institutions and Corporates
The Bangsar South raid is not just a law enforcement victory. It is a warning signal for the financial industry.
1. Reputational Fallout
When a Manchester United sponsor is linked to scams, it is not just the company that suffers. Brand trust in fintech, sports, and banking becomes collateral damage. This raises the stakes for due diligence in sponsorships and partnerships.
2. Investor and Customer Confidence
Digital finance thrives on trust. When fintechs are tied to scandals, investors hesitate and customers second-guess their safety. The Bangsar South case risks dampening enthusiasm for fintech adoption in Malaysia and the wider region.
3. Operational Risks for Banks
For financial institutions, call centre scams translate into suspicious transaction flows, mule account proliferation, and higher compliance costs. Traditional transaction monitoring often struggles to flag layered, cross-border flows connected to scams of this scale.
4. Regional Implications
Malaysia’s crackdown shows commendable resolve, but it also exposes the country as a hub for organised scam activity. This dual image, both a problem centre and an enforcement leader, will shape how regional regulators approach financial crime.
Lessons Learned from the Scam
- Scale ≠ Legitimacy
A large workforce and polished infrastructure do not guarantee a legitimate business. Regulators and partners must look beyond appearances. - Due Diligence is Non-Negotiable
Global brands and institutions need deeper checks before partnerships. A sponsorship or corporate tie-up can quickly become a reputational liability. - Regulatory Vigilance Matters
The Bangsar South raid shows what decisive enforcement looks like, but it also reveals how long such scams can operate before being stopped. - Cross-Border Cooperation is Critical
Victims were likely spread across multiple jurisdictions. Without international collaboration, enforcement remains reactive. - Public Awareness is Essential
Scam call centres thrive because victims are unaware. Public education campaigns must go hand-in-hand with enforcement.
The Role of Technology in Prevention
Conventional compliance methods, such as simple blacklist checks or static rules, are no match for scam call centres operating at an industrial scale. To counter them, financial institutions need adaptive, intelligence-driven defences.
This is where Tookitaki’s FinCense and the AFC Ecosystem come in:
- Typology-Driven Detection
FinCense continuously updates detection logic based on real scam scenarios contributed by 200+ global financial crime experts in the AFC Ecosystem. This means emerging call centre scam patterns can be identified faster. - Agentic AI
At the heart of FinCense is an Agentic AI framework, a network of intelligent agents that not only detect suspicious activity but also explain every decision in plain language. This reduces investigation time and builds regulator confidence. - Federated Learning
Through federated learning, FinCense enables banks to share insights on scam flows and mule account behaviours without compromising sensitive data. It is collective intelligence at scale. - Smart Case Disposition
When alerts are triggered, FinCense’s Agentic AI generates natural-language summaries, helping investigators prioritise critical cases quickly and accurately.
Moving Forward: The Future of Scam Call Centres
The Bangsar South raid may have shut down one operation, but the fight against scam call centres is far from over. As enforcement improves, fraudsters will adopt AI-driven tools, deepfake impersonations, and more sophisticated laundering methods.
For financial institutions, the path forward is clear:
- Strengthen collaboration with regulators and peers to track cross-border scam flows.
- Invest in adaptive technology like FinCense to stay ahead of criminal innovation.
- Educate customers relentlessly about new fraud tactics.
The raid was a victory, but it was also a warning.
If one call centre with 400 employees can operate in plain sight, imagine how many others remain hidden. The only safe strategy for financial institutions is to stay one step ahead with collaboration, intelligence, and next-generation technology.
Locked on Video: Inside India’s Chilling Digital Arrest Scam
It began with a phone call. A senior citizen in Navi Mumbai answered a number that appeared to belong to the police. Within hours, she was trapped on a video call with men in uniforms, accused of laundering money for terrorists. Terrified, she wired ₹21 lakh into what she believed was a government-controlled account.
She was not alone. In August 2025, cases of “digital arrest” scams surged across India. An elderly couple in Madhya Pradesh drained nearly ₹50 lakh of their life savings after spending 13 days under constant video surveillance by fraudsters posing as investigators. In Rajkot, criminals used the pretext of a real anti-terror operation to extort money from a student.
These scams are not crude phishing attempts. They are meticulously staged psychological operations, exploiting people’s deepest fears of authority and social disgrace. Victims are not tricked into handing over passwords. They are coerced, minute by minute, into making transfers themselves. The results are devastating, both for individuals and the wider financial system.
Background of the Scam
The anatomy of a digital arrest scam follows a chillingly consistent script.
1. The Call of Fear
Fraudsters begin with a phone call, often masked to resemble an official number. The caller claims the victim’s details have surfaced in a serious crime: drug trafficking, terror financing, or money laundering. The consequences are presented as immediate arrest, frozen accounts, or ruined reputations.
2. Escalation to Video
To heighten credibility, the fraudster insists on switching to a video call. Victims are connected to people wearing uniforms, holding forged identity cards, or even sitting before backdrops resembling police stations and courtrooms.
3. Isolation and Control
Once on video, the victim is told they cannot disconnect. In some cases, they are monitored round the clock, ordered not to use their phone for any purpose other than the call. Contact with family or friends is prohibited, under the guise of “confidential investigations.”
4. The Transfer of Funds
The victim is then directed to transfer money into so-called “secure accounts” to prove their innocence or pay bail. These accounts are controlled by criminals and serve as the first layer in complex laundering networks. Victims, believing they are cooperating with the law, empty fixed deposits, break retirement savings, and transfer sums that can take a lifetime to earn.
The method blends social engineering with coercive control. It is not the theft of data, but the hijacking of human behaviour.
What the Case Revealed
The 2025 wave of digital arrest scams in India exposed three critical truths about modern fraud.
1. Video Calls Are No Longer a Guarantee of Authenticity
For years, people considered video more secure than phone calls or emails. If you could see someone’s face, the assumption was that they were genuine. These scams demolished that trust. Fraudsters showed that live video, like written messages, can be staged, manipulated, and weaponised.
2. Authority Bias is a Fraudster’s Greatest Weapon
Humans are hardwired to respect authority, especially law enforcement. By impersonating police or investigators, criminals bypass the victim’s critical reasoning. Fear of prison or social disgrace outweighs logical checks.
3. Coercion Multiplies the Damage
Unlike phishing or one-time deceptions, digital arrests involve prolonged psychological manipulation. Victims are kept online for days, bombarded with threats and false evidence. Under this pressure, even cautious individuals break down. The results are not minor losses, but catastrophic financial wipe-outs.
4. Organised Networks Are Behind the Scenes
The professionalism and scale suggest syndicates, not lone operators. From forged documents to layered mule accounts, the fraud points to criminal hubs capable of running scripted operations across borders.
Impact on Financial Institutions and Corporates
Though victims are individuals, the implications extend far into the financial and corporate world.
1. Reputational Risk
When victims lose life savings through accounts within the banking system, they often blame their bank as much as the fraudster. Even if technically blameless, institutions suffer a hit to public trust.
2. Pressure on Fraud Systems
Digital arrest scams exploit authorised transactions. Victims themselves make the transfers. Traditional detection tools that focus on unauthorised access or password breaches cannot easily flag these cases.
3. Global Movement of Funds
Money from scams rarely stays local. Transfers are routed across borders within hours, layered through mule accounts, e-wallets, and fintech platforms. This complicates recovery and exposes gaps in international coordination.
4. Corporate Vulnerability
The threat is not limited to retirees or individuals. In Singapore earlier this year, a finance director was tricked into wiring half a million dollars during a deepfake board call. Digital arrest tactics could just as easily target corporate employees handling high-value transactions.
5. Regulatory Expectations
As scams multiply, regulators are pressing institutions to demonstrate stronger customer protections, more resilient monitoring, and greater collaboration. Failure to act risks not only reputational damage but also regulatory penalties.
Lessons Learned from the Scam
For Individuals
- Treat unsolicited calls from law enforcement with suspicion. Real investigations do not begin on the phone.
- Verify independently by calling the published numbers of agencies.
- Watch for signs of manipulation, such as demands for secrecy or threats of immediate arrest.
- Educate vulnerable groups, particularly senior citizens, about how these scams operate.
For Corporates
- Train employees, especially those in finance roles, to recognise coercion tactics.
- Require secondary verification for urgent, high-value transfers, especially when directed to new accounts.
- Encourage a speak-up culture where staff can challenge suspicious instructions without fear of reprimand.
For Financial Institutions
- Monitor for mule account activity. Unexplained inflows followed by rapid withdrawals are a red flag.
- Run customer awareness campaigns, explaining how digital arrest scams work.
- Share intelligence with peers and regulators to prevent repeat incidents across institutions.
The Role of Technology in Prevention
Digital arrest scams prove that traditional safeguards are insufficient. Fraudsters are not stealing credentials but manipulating behaviour. Prevention requires smarter, adaptive systems.
1. Behavioural Monitoring
Transactions made under duress often differ from normal patterns. Advanced analytics can detect anomalies, such as sudden large transfers from accounts with low historical activity.
2. Typology-Driven Detection
Platforms like Tookitaki’s FinCense leverage the AFC Ecosystem to encode real-world scam scenarios into detection logic. As digital arrest typologies are identified, they can be integrated quickly to improve monitoring.
3. AI-Powered Simulations
Institutions can run simulations of coercion-based scams to test whether their processes would withstand them. These exercises reveal gaps in escalation and verification controls.
4. Federated Learning for Collective Defence
With federated learning, insights from one bank can be shared across many without exposing sensitive data. If one institution sees a pattern in digital arrest cases, others can benefit almost instantly.
5. Smarter Alert Management
Agentic AI can review and narrate the context of alerts, allowing investigators to understand whether unusual activity stems from duress. This speeds up response times and prevents irreversible losses.
Conclusion
The digital arrest scam is not just a fraud. It is a form of psychological captivity, where victims are imprisoned through fear on their own devices. In 2025, India saw a surge of such cases, stripping people of their savings and shaking trust in digital communications.
The message is clear: scams no longer rely on technical breaches. They rely on exploiting human trust. For individuals, the defence is awareness and verification. For corporates, it is embedding strong protocols and encouraging a culture of questioning. For financial institutions, the challenge is profound. They must detect authorised transfers made under coercion, collaborate across borders, and deploy AI-powered defences that learn as fast as the criminals do.
If 2024 was the year of deepfake deception, 2025 is becoming the year of coercion-based fraud. The industry’s response will determine whether scams like digital arrests remain isolated tragedies or become a systemic crisis. Protecting trust is no longer optional. It is the frontline of financial crime prevention.
Inside the New Payments Platform (NPP): How Australia’s Real-Time Payments Are Changing Finance and Fraud
Australia’s real-time payments revolution is reshaping finance, but it also brings new risks and compliance challenges.
Imagine sending money to a friend, paying a bill, or receiving your salary in seconds, no matter the day or hour. That vision became reality in Australia with the launch of the New Payments Platform (NPP) in 2018. Since then, the NPP has transformed how Australians transact, powering faster, smarter, and more flexible payments.
But while the benefits are undeniable, the NPP has also introduced fresh risks. Fraudsters and money launderers now exploit the speed of real-time payments, forcing banks, fintechs, and regulators to rethink how they approach compliance. In this blog, we take a deep look at the NPP, exploring its origins, features, benefits, risks, and what the future holds.
What is the New Payments Platform (NPP)?
The NPP is Australia’s real-time payments infrastructure, designed to allow funds to be transferred between bank accounts in seconds. Unlike traditional bank transfers, which could take hours or days, the NPP settles payments instantly, around the clock, 365 days a year.
A Collaborative Effort
The NPP was launched in February 2018 as a collaborative initiative between the Reserve Bank of Australia (RBA), major banks, and key financial institutions. It was developed to modernise Australia’s payments infrastructure and to match the expectations of a digital-first economy.
Core Components of the NPP
- Fast Settlement Service (FSS): Operated by the RBA, this ensures transactions settle instantly across participating banks.
- Overlay Services: Products built on top of the NPP to offer tailored use cases, such as Osko by BPAY for fast peer-to-peer payments.
- PayID: A feature that allows customers to link easy identifiers such as email addresses or phone numbers to bank accounts for faster payments.
- ISO 20022 Data Standard: Enables rich data to travel with payments, improving transparency and reporting.
The NPP is not just a new payment rail. It is an entirely new ecosystem designed to support innovation, competition, and efficiency.
Key Features of the NPP
- Speed: Transactions settle in less than 60 seconds.
- Availability: Operates 24/7/365, unlike traditional settlement systems.
- Rich Data: ISO 20022 messaging allows businesses to include detailed payment references.
- Flexibility: Overlay services enable innovative new use cases, from consumer-to-business payments to government disbursements.
- Ease of Use: PayID removes the need for remembering BSB and account numbers.
Benefits of the NPP for Australia
1. Consumer Convenience
Everyday Australians can send and receive money instantly. Whether splitting a dinner bill or paying rent, transactions are seamless and fast.
2. Business Efficiency
Businesses benefit from faster supplier payments, real-time payroll, and improved cash flow management. For SMEs, this reduces dependency on costly credit.
3. Government Services
Government agencies can issue refunds, grants, and welfare payments in real time, improving citizen experience and efficiency.
4. Financial Inclusion and Innovation
The NPP creates opportunities for fintechs to build new payment products and services, driving competition and giving consumers more choice.
5. Enhanced Transparency
The rich data standards improve reconciliation and reduce errors, saving time and cost for businesses.
The Risks and Challenges of Real-Time Payments
As with any innovation, the NPP comes with challenges. The very features that make it attractive to consumers also make it attractive to fraudsters and money launderers.
1. Authorised Push Payment (APP) Scams
Fraudsters use social engineering to trick customers into sending money themselves. Because NPP payments are instant, victims often cannot recover funds once transferred.
2. Money Mule Networks
Criminals exploit mule accounts to move illicit funds quickly. Dormant accounts or those opened with synthetic identities are often used as conduits.
3. Increased Operational Pressure
Compliance teams that once had hours to review suspicious transactions now have seconds. This shift requires entirely new approaches to monitoring.
4. False Positives and Noise
Traditional systems generate vast numbers of false positives, which overwhelm investigators. With NPP volumes growing, this problem is magnified.
5. Cyber and Identity Risks
Fraudsters use phishing, malware, and stolen credentials to take over accounts and push funds instantly.
Regulatory and Industry Response
Australian regulators have moved swiftly to address these risks.
- AUSTRAC: Expects banks and payment providers to implement effective real-time monitoring and suspicious matter reporting tailored to NPP risks.
- ASIC: Focuses on consumer protection and ensuring victims of scams are treated fairly.
- Industry Initiatives: The Australian Banking Association has been working on scam-reporting frameworks and shared fraud detection systems across banks.
- Government Action: Proposals to make banks reimburse scam victims are under consideration, following models in the UK.
The message is clear: institutions must invest in smarter compliance and fraud prevention tools.
Fraud and AML in the NPP Era
Why Legacy Systems Fall Short
Legacy monitoring systems were built for batch processing. They cannot keep up with the millisecond-level requirements of real-time payments. By the time a suspicious transaction is flagged, the funds are gone.
What Next-Gen Solutions Look Like
Modern systems use AI and machine learning to:
- Detect anomalies in real time.
- Link suspicious activity across accounts, devices, and geographies.
- Reduce false positives by learning from investigator feedback.
- Provide regulator-ready explanations for every alert.
Key Fraud Red Flags in NPP Transactions
- Large transfers to newly created accounts.
- Multiple small payments designed to avoid thresholds.
- Sudden changes in device or login behaviour.
- Beneficiaries in high-risk jurisdictions.
- Rapid pass-through activity with no balance retention.
Spotlight on Technology: Tookitaki’s Role
As the risks around NPP accelerate, technology providers are stepping up. Tookitaki’s FinCense is purpose-built for the demands of real-time payments.
How FinCense Helps
- Real-Time Monitoring: Detects suspicious activity in milliseconds.
- Agentic AI: Continuously adapts to new scam typologies, reducing false positives.
- Federated Intelligence: Accesses insights from the AFC Ecosystem, a global compliance community, while preserving privacy.
- FinMate AI Copilot: Assists investigators with summaries, recommendations, and regulator-ready narratives.
- AUSTRAC-Ready Compliance: Built-in reporting for SMRs, TTRs, and detailed audit trails.
Local Adoption
FinCense is already being used by community-owned banks like Regional Australia Bank and Beyond Bank. These partnerships demonstrate that even mid-sized institutions can meet AUSTRAC’s expectations while delivering excellent customer experiences.
The Future of NPP in Australia
The NPP is still evolving. Several developments will shape its future:
1. PayTo Expansion
PayTo, a digital alternative to direct debit, is gaining traction. It allows consumers to authorise payments directly from their accounts, offering flexibility but also new fraud vectors.
2. Cross-Border Potential
Future integration with Asia-Pacific payment systems could expand NPP beyond Australia, increasing both opportunities and risks.
3. Smarter Fraud Typologies
Criminals are already exploring ways to exploit deepfake technology, synthetic identities, and AI-driven scams. Fraud prevention must evolve just as quickly.
4. Industry Collaboration
Expect stronger cooperation between banks, fintechs, regulators, and technology vendors. Shared fraud databases and federated intelligence models will be crucial.
Conclusion
The New Payments Platform has reshaped Australia’s payments landscape. It delivers speed, convenience, and innovation that benefit consumers, businesses, and government agencies. But with opportunity comes risk.
Fraudsters have been quick to exploit the instant nature of NPP, forcing institutions to rethink how they detect and prevent financial crime. The solution lies in real-time, AI-powered monitoring platforms that adapt to new typologies and reduce compliance costs.
For Australian institutions, the NPP is more than a payment rail. It is the foundation of a new financial ecosystem. The winners will be those who embrace innovation, partner with the right AML vendors, and build trust through smarter compliance.
Pro tip: If your institution still relies on batch monitoring, you are already behind. Now is the time to modernise and future-proof your compliance with intelligent fraud and AML platforms.