When money moves instantly, detection must think in scenarios, not thresholds.

Introduction

Real-time payments have changed what “too late” means.

In traditional payment systems, transaction monitoring had time on its side. Alerts could be reviewed after settlement. Suspicious patterns could be pieced together over hours or days. Interventions, while imperfect, were still possible.

In Australia’s real-time payments environment, that margin no longer exists.

Funds move in seconds. Customers expect immediate execution. Fraudsters exploit speed, social engineering, and behavioural blind spots. Many high-risk transactions look legitimate when viewed in isolation.

This is why scenario-based transaction monitoring has become critical for real-time payments in Australia.

Rules alone cannot keep pace. What institutions need is the ability to recognise patterns of behaviour unfolding in real time, guided by scenarios grounded in how financial crime actually happens.

Why Real-Time Payments Break Traditional Monitoring Models

Most transaction monitoring systems were designed for a slower world.

They rely heavily on:

• Static thresholds
• Single-transaction checks
• Retrospective pattern analysis

Real-time payments expose the limits of this approach.

Speed removes recovery windows

Once a real-time payment is executed, funds are often irretrievable. Detection must occur before or during execution, not after.

Fraud increasingly appears authorised

Many real-time payment fraud cases involve customers who initiate transactions themselves after being manipulated. Traditional red flags tied to unauthorised access often fail.

Transactions look normal in isolation

Amounts stay within typical ranges. Destinations are new but not obviously suspicious. Timing appears reasonable.

Risk only becomes visible when transactions are viewed as part of a broader behavioural narrative.

Volume amplifies noise

Real-time rails increase transaction volumes. Rule-based systems struggle to separate meaningful risk from routine activity without overwhelming operations.

Why Rules Alone Are Not Enough

Rules are still necessary. They provide guardrails and baseline coverage.

But in real-time payments, rules suffer from structural limitations.

• They react to known patterns
• They struggle with subtle behavioural change
• They generate high false positives when tuned aggressively
• They miss emerging fraud tactics until after damage occurs

Rules answer the question:
“Did this transaction breach a predefined condition?”

They do not answer:
“What story is unfolding right now?”

That is where scenarios come in.

What Scenario-Based Transaction Monitoring Really Means

Scenario-based monitoring is often misunderstood as simply grouping rules together.

In practice, it is much more than that.

A scenario represents a real-world risk narrative, capturing how fraud or laundering actually unfolds across time, accounts, and behaviours.

Scenarios focus on:

• Sequences, not single events
• Behavioural change, not static thresholds
• Context, not isolated attributes

In real-time payments, scenarios provide the structure needed to detect risk early without flooding systems with alerts.

How Scenario-Based Monitoring Works in Real Time

Scenario-based transaction monitoring shifts the unit of analysis from transactions to behaviour.

From transactions to sequences

Instead of evaluating transactions one by one, scenarios track:

• Rapid changes in transaction frequency
• First-time payment behaviour
• Sudden shifts in counterparties
• Escalation patterns following customer interactions

Fraud often reveals itself through how behaviour evolves, not through any single transaction.

Contextual evaluation

Scenarios evaluate transactions alongside:

• Customer risk profiles
• Historical transaction behaviour
• Channel usage patterns
• Time-based indicators

Context allows systems to distinguish between legitimate urgency and suspicious escalation.

Real-time decisioning

Scenarios are designed to surface risk early enough to:

• Pause transactions
• Trigger step-up controls
• Route cases for immediate review

This is essential in environments where seconds matter.

Why Scenarios Reduce False Positives in Real-Time Payments

One of the biggest operational challenges in real-time monitoring is false positives.

Scenario-based monitoring addresses this at the design level.

Fewer isolated triggers

Scenarios do not react to single anomalies. They require patterns to emerge, reducing noise from benign one-off activity.

Risk is assessed holistically

A transaction that triggers a rule may not trigger a scenario if surrounding behaviour remains consistent and low risk.

Alerts are more meaningful

When a scenario triggers, it already reflects a narrative. Analysts receive alerts that explain why risk is emerging, not just that a rule fired.

This improves efficiency and decision quality simultaneously.

The Role of Scenarios in Detecting Modern Fraud Types

Scenario-based monitoring is particularly effective against fraud types common in real-time payments.

Social engineering and scam payments

Scenarios can detect:

• Sudden urgency following customer contact
• First-time high-risk payments
• Behavioural changes inconsistent with prior history

These signals are difficult to codify reliably using rules alone.

Mule-like behaviour

Scenario logic can identify:

• Rapid pass-through of funds
• New accounts receiving and dispersing payments quickly
• Structured activity across multiple transactions

Layered laundering patterns

Scenarios capture how funds move across accounts and time, even when individual transactions appear normal.

Why Scenarios Must Be Continuously Evolved

Fraud scenarios are not static.

New tactics emerge as criminals adapt to controls. This makes scenario governance critical.

Effective programmes:

• Continuously refine scenarios based on outcomes
• Incorporate insights from investigations
• Learn from industry-wide patterns rather than operating in isolation

This is where collaborative intelligence becomes valuable.

Scenarios as Part of a Trust Layer

Scenario-based monitoring delivers the most value when embedded into a broader Trust Layer.

In this model:

• Scenarios surface meaningful risk
• Customer risk scoring provides context
• Alert prioritisation sequences attention
• Case management enforces consistent investigation
• Outcomes feed back into scenario refinement

This closed loop ensures monitoring improves over time rather than stagnates.

Operational Challenges Institutions Still Face

Even with scenario-based approaches, challenges remain.

• Poorly defined scenarios that mimic rules
• Lack of explainability in why scenarios triggered
• Disconnected investigation workflows
• Failure to retire or update ineffective scenarios

Scenario quality matters more than scenario quantity.

Where Tookitaki Fits

Tookitaki approaches scenario-based transaction monitoring as a core capability of its Trust Layer.

Within the FinCense platform:

• Scenarios reflect real-world financial crime narratives
• Real-time transaction monitoring operates at scale
• Scenario intelligence is enriched by community insights
• Alerts are prioritised and consolidated at the customer level
• Investigations feed outcomes back into scenario learning

This enables financial institutions to manage real-time payment risk proactively rather than reactively.

Measuring Success in Scenario-Based Monitoring

Success should be measured beyond alert counts.

Key indicators include:

• Time to risk detection
• Reduction in false positives
• Analyst decision confidence
• Intervention effectiveness
• Regulatory defensibility

Strong scenarios improve outcomes across all five dimensions.

The Future of Transaction Monitoring for Real-Time Payments in Australia

As real-time payments continue to expand, transaction monitoring must evolve with them.

Future-ready monitoring will focus on:

• Behavioural intelligence over static thresholds
• Scenario-driven detection
• Faster, more proportionate intervention
• Continuous learning from outcomes
• Strong explainability

Scenarios will become the language through which risk is understood and managed in real time.

Conclusion

Real-time payments demand a new way of thinking about transaction monitoring.

Rules remain necessary, but they are no longer sufficient. Scenario-based transaction monitoring provides the structure needed to detect behavioural risk early, reduce noise, and act within shrinking decision windows.

For financial institutions in Australia, the shift to scenario-based monitoring is not optional. It is the foundation of effective, sustainable control in a real-time payments world.

When money moves instantly, monitoring must understand the story, not just the transaction.

When risk concentrates in geography, detection must widen its lens.

Introduction

Transaction monitoring becomes significantly more complex when money moves through high-risk jurisdictions. What may appear as routine cross-border activity often carries layered exposure tied to geography, regulatory divergence, and fragmented visibility. For financial institutions operating in the Philippines, this challenge is no longer occasional. It is structural.

The Philippines sits at the intersection of major remittance corridors, regional trade routes, and rapidly expanding digital payment ecosystems. Funds move in and out of the country constantly, supporting families, businesses, and economic growth. At the same time, these same channels are exploited by organised crime, fraud syndicates, and laundering networks that deliberately route transactions through higher-risk jurisdictions to disguise illicit origins.

This makes transaction monitoring for high-risk jurisdictions in the Philippines one of the most critical pillars of AML compliance today. Institutions must detect meaningful risk without relying on blunt country lists, slowing legitimate activity, or overwhelming compliance teams with false positives.

Traditional monitoring approaches struggle in this environment. Modern compliance requires a more nuanced, intelligence-driven approach that understands how geographic risk interacts with behaviour, networks, and scale.

Why Jurisdictional Risk Still Matters

Despite advances in analytics and automation, jurisdictional risk remains central to money laundering and financial crime.

Certain jurisdictions continue to present higher exposure due to regulatory gaps, inconsistent enforcement, economic structures that enable opacity, or known organised crime activity. Criminal networks exploit these weaknesses by routing funds through multiple locations, creating distance between illicit sources and final destinations.

For Philippine financial institutions, this risk is embedded in daily operations. Cross-border activity often involves jurisdictions with varying AML maturity, fragmented data availability, and different supervisory expectations. When combined with real-time payments and high transaction volumes, these factors significantly increase detection complexity.

However, jurisdiction alone is no longer a sufficient indicator of risk. Simply flagging transactions because they involve a higher-risk country results in excessive alerts and weak outcomes. The real challenge lies in understanding how geographic exposure intersects with customer behaviour and transaction patterns.

The Problem With Country-Based Rules

Many institutions still rely heavily on country risk lists as the backbone of their transaction monitoring logic. While these lists serve as an important baseline, they are increasingly blunt instruments.

One major issue is alert overload. Transactions involving higher-risk jurisdictions are often legitimate, especially in remittance-heavy economies like the Philippines. Static country rules generate large volumes of alerts that consume investigative capacity without improving detection.

Another challenge is rigidity. Country risk profiles evolve due to geopolitical events, regulatory reforms, or enforcement actions. Static configurations struggle to adapt quickly, leaving monitoring frameworks misaligned with reality.

Most importantly, country-based rules lack behavioural context. They treat all transactions involving a jurisdiction the same way, regardless of customer profile, transaction history, or network relationships. This makes it difficult to distinguish routine activity from genuinely suspicious patterns.

Effective transaction monitoring for high-risk jurisdictions requires moving beyond geography as a trigger and toward geography as a risk dimension.

How High-Risk Jurisdiction Exposure Actually Appears in Practice

Jurisdictional risk rarely presents itself through a single large transaction. It emerges through patterns.

These patterns often include rapid pass-through behaviour, where funds enter an account domestically and are quickly transferred to multiple foreign destinations. In other cases, customers suddenly begin using new corridors that do not align with their historical activity or stated purpose.

In digital payment environments, risk may surface through wallets or accounts that act as transit points, receiving and distributing funds across jurisdictions with minimal retention. Networks of accounts may work together to distribute funds across multiple locations, obscuring the original source.

These behaviours are rarely captured by simple country rules. They require systems capable of analysing geography in conjunction with time, behaviour, and relationships.

What Effective Monitoring for High-Risk Jurisdictions Really Requires

Monitoring high-risk jurisdictions effectively is not about stricter controls. It is about smarter ones.

First, monitoring must be behaviour-led. Institutions need to understand how customers typically transact across geographies and identify deviations that indicate risk.

Second, detection must be longitudinal. Jurisdictional risk often becomes visible only when activity is analysed over time rather than transaction by transaction.

Third, monitoring must scale. High-risk jurisdictions are often part of high-volume corridors, particularly in remittance and digital payment ecosystems.

Finally, explainability remains essential. Institutions must be able to clearly explain why transactions were flagged, even when detection logic incorporates complex patterns.

Key Capabilities for Monitoring High-Risk Jurisdictions

Geography as a Risk Dimension, Not a Trigger

Modern monitoring systems treat geography as one of several interacting risk dimensions. Jurisdictional exposure is evaluated alongside transaction velocity, behavioural change, counterparty relationships, and customer profile.

This approach preserves sensitivity to risk while dramatically reducing unnecessary alerts.

Corridor-Based Behavioural Analysis

Rather than focusing on individual countries, effective monitoring analyses corridors. Each corridor has typical patterns related to frequency, value, timing, and counterparties.

Systems that understand corridor norms can identify deviations that suggest layering, structuring, or misuse, even when individual transactions appear routine.

Network and Flow Analysis Across Jurisdictions

High-risk laundering activity often involves networks rather than isolated customers. Network analysis uncovers shared counterparties, circular fund flows, and coordinated behaviour across jurisdictions.

This capability is essential for detecting organised laundering schemes that deliberately exploit geographic complexity.

Dynamic Risk Scoring

Jurisdictional risk should evolve with behaviour. Customers who begin transacting through new high-risk jurisdictions without a clear rationale should see their risk scores adjust dynamically.

Dynamic scoring ensures monitoring remains proportionate and responsive.

Automation and Risk-Based Prioritisation

Monitoring high-risk jurisdictions can generate significant volumes if not managed carefully. Automation is critical to enrich alerts, assemble context, and prioritise cases based on overall risk rather than geography alone.

This allows compliance teams to focus on high-impact investigations.

Regulatory Expectations Around High-Risk Jurisdictions

Regulators expect enhanced scrutiny of transactions involving higher-risk jurisdictions, but they also expect proportionality and effectiveness.

In the Philippines, supervisory reviews increasingly focus on whether institutions can demonstrate that their monitoring frameworks identify genuine risk rather than simply producing alerts. Institutions must show that they understand how geographic exposure interacts with behaviour and networks.

Explainability is especially important. Institutions must justify why certain transactions were flagged while others involving the same jurisdictions were not.

Monitoring frameworks that rely solely on static country lists are increasingly difficult to defend.

How Tookitaki Enables Smarter Jurisdictional Monitoring

Tookitaki approaches transaction monitoring for high-risk jurisdictions as an intelligence challenge rather than a rules challenge.

Through FinCense, transactions are analysed within a broader behavioural and network context. Detection logic focuses on how funds move across geographies, how behaviour changes over time, and how accounts are interconnected.

FinCense is built for high-volume and near real-time environments, enabling institutions to monitor high-risk corridors without performance degradation.

FinMate, Tookitaki’s Agentic AI copilot, supports investigators by summarising geographic patterns, highlighting unusual corridor usage, and explaining why jurisdiction-linked activity was flagged. This improves investigation speed and consistency while maintaining transparency.

The AFC Ecosystem strengthens this further by providing continuously updated typologies and red flags related to cross-border and jurisdiction-driven laundering techniques. These insights ensure detection logic stays aligned with real-world risk.

A Practical Scenario: Seeing Risk Beyond the Border

Consider a Philippine institution observing frequent outbound transfers to several higher-risk jurisdictions. Traditional rules generate numerous alerts purely based on country involvement, overwhelming investigators.

With behaviour-led monitoring, the institution identifies a smaller subset of cases where geographic exposure coincides with unusual transaction velocity, repeated pass-through behaviour, and shared counterparties.

Alerts are prioritised based on overall risk. Investigators receive consolidated views showing how funds move across jurisdictions over time, enabling faster and more confident decisions.

Legitimate activity continues uninterrupted, while suspicious patterns are surfaced more effectively.

Benefits of Intelligence-Led Monitoring for High-Risk Jurisdictions

Modern transaction monitoring for high-risk jurisdictions delivers tangible benefits.

Detection accuracy improves as systems focus on meaningful patterns rather than blunt triggers. False positives decrease, reducing operational strain. Investigations become faster and more consistent due to richer context and automation.

From a governance perspective, institutions gain stronger audit trails and clearer explanations. Regulatory confidence improves as monitoring frameworks demonstrate proportionality and effectiveness.

Most importantly, institutions can manage geographic risk without compromising customer experience or payment speed.

The Future of Jurisdiction-Based Transaction Monitoring

As financial crime becomes increasingly global, jurisdiction-based monitoring will continue to evolve.

Future systems will emphasise predictive intelligence, identifying early signals of geographic risk before funds move. Integration between AML and fraud monitoring will deepen, providing unified visibility across borders.

Agentic AI will play a growing role in helping investigators interpret complex geographic networks. Collaborative intelligence models will allow institutions to learn from emerging jurisdictional risks without sharing sensitive data.

Institutions that invest in intelligence-led monitoring today will be better positioned to manage this future.

Conclusion

High-risk jurisdictions remain a central AML concern, particularly in a highly interconnected financial ecosystem like the Philippines. However, effective monitoring is no longer about stricter country rules.

Modern transaction monitoring for high-risk jurisdictions in the Philippines requires behaviour-led detection, network intelligence, and scalable systems that operate in real time. Institutions must understand how geography interacts with behaviour and scale to surface meaningful risk.

With Tookitaki’s FinCense platform, supported by FinMate and enriched by the AFC Ecosystem, financial institutions can move beyond blunt controls and gain clear, actionable insight into jurisdiction-driven risk.

When risk has a passport, seeing beyond borders is what defines effective compliance.

When money crosses borders at speed, risk rarely stays behind.

Introduction

Cross-border payments are a critical lifeline for the Philippine economy. Remittances, trade flows, digital commerce, and regional payment corridors move billions of pesos across borders every day. For banks and payment institutions, these flows enable growth, inclusion, and global connectivity.

They also introduce some of the most complex money laundering risks in the financial system.

Criminal networks exploit cross-border channels to fragment transactions, layer funds across jurisdictions, and obscure the origin of illicit proceeds. What appears routine in isolation often forms part of a larger laundering pattern once viewed across borders and time.

This is why cross-border transaction monitoring for AML compliance in the Philippines has become a defining challenge. Institutions must detect meaningful risk without slowing legitimate flows, overwhelming compliance teams, or losing regulatory confidence. Traditional monitoring approaches are increasingly stretched in this environment.

Modern AML compliance now depends on transaction monitoring systems that understand cross-border behaviour at scale and in context.

Why Cross-Border Transactions Are Inherently Higher Risk

Cross-border transactions introduce complexity that domestic payments do not.

Funds move across different regulatory regimes, financial infrastructures, and data standards. Visibility can be fragmented, especially when transactions pass through intermediaries or correspondent banking networks.

Criminals take advantage of this fragmentation. They move funds through multiple jurisdictions to create distance between the source of funds and their final destination. Transactions are often broken into smaller amounts, routed through wallets or mule accounts, and executed rapidly to reduce the chance of detection.

In the Philippine context, cross-border risk is amplified by:

• high remittance volumes
• regional payment corridors
• growing digital wallet usage
• increased real-time payment adoption

Monitoring these flows requires more than static rules or country risk lists. It requires systems that understand behaviour, relationships, and patterns across borders.

The Limitations of Traditional Cross-Border Monitoring

Many institutions still monitor cross-border transactions using approaches designed for a slower, lower-volume environment.

Static rules based on transaction amount, frequency, or country codes are common. While these controls provide baseline coverage, they struggle to detect modern laundering techniques.

One major limitation is context. Traditional systems often evaluate each transaction independently, without fully linking activity across accounts, corridors, or time periods. This makes it difficult to identify layered or coordinated behaviour.

Another challenge is alert overload. Cross-border rules tend to be conservative, generating large volumes of alerts to avoid missing risk. As volumes grow, compliance teams are overwhelmed with low-quality alerts, reducing focus on genuinely suspicious activity.

Latency is also an issue. Batch-based monitoring means risk is identified after funds have already moved, limiting the ability to respond effectively.

These constraints make it increasingly difficult to demonstrate effective AML compliance in high-volume cross-border environments.

What Effective Cross-Border Transaction Monitoring Really Requires

Effective cross-border transaction monitoring is not about adding more rules. It is about changing how risk is understood and prioritised.

First, monitoring must be behaviour-led rather than transaction-led. Individual cross-border transactions may appear legitimate, but patterns over time often reveal risk.

Second, systems must operate at scale and speed. Cross-border monitoring must keep pace with real-time and near real-time payments without degrading performance.

Third, monitoring must link activity across borders. Relationships between senders, receivers, intermediaries, and jurisdictions matter more than isolated events.

Finally, explainability and governance must remain strong. Institutions must be able to explain why activity was flagged, even when detection logic is complex.

Key Capabilities for Cross-Border AML Transaction Monitoring

Behavioural Pattern Detection Across Borders

Behaviour-led monitoring analyses how customers transact across jurisdictions rather than focusing on individual transfers. Sudden changes in corridors, counterparties, or transaction velocity can indicate laundering risk.

This approach is particularly effective in detecting layering and rapid pass-through activity across multiple countries.

Corridor-Based Risk Intelligence

Cross-border risk often concentrates in specific corridors rather than individual countries. Monitoring systems must understand corridor behaviour, typical transaction patterns, and deviations from the norm.

Corridor-based intelligence allows institutions to focus on genuinely higher-risk flows without applying blanket controls that generate noise.

Network and Relationship Analysis

Cross-border laundering frequently involves networks of related accounts, mules, and intermediaries. Network analysis helps uncover coordinated activity that would otherwise remain hidden across jurisdictions.

This capability is essential for identifying organised laundering schemes that span multiple countries.

Real-Time or Near Real-Time Detection

In high-speed payment environments, delayed detection increases exposure. Modern cross-border monitoring systems analyse transactions as they occur, enabling faster intervention and escalation.

Risk-Based Alert Prioritisation

Not all cross-border alerts carry the same level of risk. Effective systems prioritise alerts based on behavioural signals, network indicators, and contextual risk factors.

This ensures that compliance teams focus on the most critical cases, even when transaction volumes are high.

Cross-Border AML Compliance Expectations in the Philippines

Regulators in the Philippines expect financial institutions to apply enhanced scrutiny to cross-border activity, particularly where risk indicators are present.

Supervisory reviews increasingly focus on:

• effectiveness of detection, not alert volume
• ability to identify complex and evolving typologies
• quality and consistency of investigations
• governance and explainability

Institutions must demonstrate that their transaction monitoring systems are proportionate to their cross-border exposure and capable of adapting as risks evolve.

Static frameworks and one-size-fits-all rules are no longer sufficient to meet these expectations.

How Tookitaki Enables Cross-Border Transaction Monitoring

Tookitaki approaches cross-border transaction monitoring as an intelligence and scale problem, not a rules problem.

Through FinCense, Tookitaki enables continuous monitoring of cross-border transactions using behavioural analytics, advanced pattern detection, and machine learning. Detection logic focuses on how funds move across borders rather than isolated transfers.

FinCense is built to handle high transaction volumes and real-time environments, making it suitable for institutions processing large cross-border flows.

FinMate, Tookitaki’s Agentic AI copilot, supports investigators by summarising cross-border transaction behaviour, highlighting key risk drivers, and explaining why alerts were generated. This significantly reduces investigation time while improving consistency.

The AFC Ecosystem strengthens cross-border monitoring by providing continuously updated typologies and red flags derived from real-world cases across regions. These insights ensure that detection logic remains aligned with evolving cross-border laundering techniques.

Together, these capabilities allow institutions to monitor cross-border activity effectively without increasing operational strain.

A Practical Scenario: Seeing the Pattern Across Borders

Consider a financial institution processing frequent outbound transfers to multiple regional destinations. Individually, the transactions are low value and appear routine.

A behaviour-led, cross-border monitoring system identifies a pattern. Funds are received domestically and rapidly transferred across different corridors, often involving similar counterparties and timing. Network analysis reveals links between accounts that were previously treated as unrelated.

Alerts are prioritised based on overall risk rather than transaction count. Investigators receive a consolidated view of activity across borders, enabling faster and more confident decision-making.

Without cross-border intelligence and pattern analysis, this activity might have remained undetected.

Benefits of Modern Cross-Border Transaction Monitoring

Modern cross-border transaction monitoring delivers clear advantages.

Detection accuracy improves as systems focus on patterns rather than isolated events. False positives decrease, reducing investigation backlogs. Institutions gain better visibility into cross-border exposure across corridors and customer segments.

From a compliance perspective, explainability and audit readiness improve. Institutions can demonstrate that monitoring decisions are risk-based, consistent, and aligned with regulatory expectations.

Most importantly, effective cross-border monitoring protects trust in a highly interconnected financial ecosystem.

The Future of Cross-Border AML Monitoring

Cross-border transaction monitoring will continue to evolve as payments become faster and more global.

Future systems will rely more heavily on predictive intelligence, identifying early indicators of risk before funds move across borders. Integration between AML and fraud monitoring will deepen, providing a unified view of cross-border financial crime.

Agentic AI will play a growing role in supporting investigations, interpreting complex patterns, and guiding decisions. Collaborative intelligence models will help institutions learn from emerging cross-border threats without sharing sensitive data.

Institutions that invest in intelligence-driven monitoring today will be better positioned to navigate this future.

Conclusion

Cross-border payments are essential to the Philippine financial system, but they also introduce some of the most complex AML risks.

Traditional monitoring approaches struggle to keep pace with the scale, speed, and sophistication of modern cross-border activity. Effective cross-border transaction monitoring for AML compliance in the Philippines requires systems that are behaviour-led, scalable, and explainable.

With Tookitaki’s FinCense platform, supported by FinMate and enriched by the AFC Ecosystem, financial institutions can move beyond fragmented rules and gain clear insight into cross-border risk.

In an increasingly interconnected world, the ability to see patterns across borders is what defines strong AML compliance.