Financial crime continues to evolve at speed, exploiting gaps across regulatory frameworks, technologies, and institutions. To keep pace, the AML/CFT community must embrace a comprehensive and collaborative model that balances strategic foresight with day-to-day operational realities.
Key Challenges Facing the AML/CFT Community
The AML/CFT ecosystem currently faces several interconnected problems that hinder its ability to effectively detect, prevent, and respond to financial crime:
Fragmented Approaches: Financial institutions and regulators often develop their AML practices, risk definitions, and data formats independently, leading to inconsistency in controls, duplication of efforts, and barriers to collaboration.
Conceptual vs. Operational Gaps: Regulatory guidelines and conceptual typologies frequently remain abstract and disconnected from the practical realities of daily compliance tasks. Teams responsible for operational processes, transaction monitoring, and AML software often lack a single, structured point of reference, resulting in ambiguous or incomplete implementations.
Unstructured AML Typologies: Official AML typology reports and threat intelligence are often disseminated as unstructured text. This approach forces financial institutions to interpret, manually label, and adapt typologies, making it challenging to efficiently translate regulatory guidance into actionable detection rules or investigative workflows.
Accessibility Challenges for Small and Mid-Tier Institutions: Larger institutions have the resources to develop sophisticated internal typology databases and AI-driven monitoring systems. In contrast, smaller and mid-tier financial institutions often lack access to standardized references, quality datasets, and advanced analytic capabilities, leaving them disproportionately vulnerable to exploitation by sophisticated criminal networks.
Insufficient Cross-Border Knowledge Sharing: AML/CFT intelligence and best practices remain siloed due to inconsistent and limited cross-border information-sharing frameworks. The resulting fragmentation allows criminals to leverage jurisdictional blind spots, delaying effective responses and enabling the spread of illicit schemes.
To directly address these critical challenges, we present "BE SAFE," a set of guiding principles aimed at fostering a more unified, data-driven, and adaptive AML/CFT ecosystem—one that not only safeguards the financial sector but also supports fair and ethical practices.
1. Bridge the Gap
Core Idea
Regulatory guidance and conceptual typologies frequently remain disconnected from the day-to-day realities faced by compliance teams and software developers. “Bridging the gap” means turning abstract rules into tangible data points—so that analysts, data scientists, engineers, and decision-makers share a unified understanding of money laundering risks.
Why It Matters
Disjointed or ambiguous definitions waste precious resources and slow innovation. If compliance rules or AML risk categories aren’t clearly mapped to real-world data, institutions can’t effectively monitor, detect, or coordinate responses. Swift convergence on well-defined models saves time, boosts consistency, and promotes better alignment with regulatory expectations.
Long-Term Vision
Regulators, financial institutions, technology vendors, and researchers collaborate under consistent frameworks that seamlessly translate regulatory language into actionable analytics. This enables quicker solution development and fosters a globally recognized “common language” for identifying and mitigating criminal behaviors.
2. Enable Secure, Ethical, and Proportional Cross-Border Information Exchange
Core Idea
Money launderers thrive on jurisdictional boundaries. Countering them requires ethical and balanced data-sharing practices that respect privacy and sovereignty while enabling rapid detection of complex illicit patterns.
Why It Matters
Without proactive information exchange, criminals exploit fragmented oversight. Proper data exchange—bound by proportionality and privacy safeguards—can help institutions anticipate cross-border laundering strategies rather than merely reacting post-event.
Long-Term Vision
Established public–private and private–private channels that share actionable intelligence within robust compliance frameworks. Regulators, FIUs, and financial institutions collectively detect large-scale schemes, bridging the usual roadblocks around confidentiality, legal obstacles, and trust deficits.
3. Standardize AML/CFT Data Across Institutions
Core Idea
Heterogeneous data labels, inconsistent formats, and vague typologies stifle AML/CFT collaboration and hamper effective risk assessment. Standardization isn’t about imposing a single universal database, but about ensuring common reference points to accelerate integrated analytics.
Why It Matters
Without standardized data structures, even the most advanced AI or analytics remain plagued by noisy inputs or incomplete references. Meaningful comparisons, cross-institution benchmarks, and robust risk scoring become difficult if every institution or vendor uses different definitions.
Long-Term Vision
A universal taxonomy and data labeling scheme widely recognized within the AML/CFT community, supporting open-sourced data efforts and advanced analytics. Organizations could easily exchange or compare risk profiles, typology information, and suspicious behavior logs, improving both local compliance tasks and global risk monitoring.
4. Automate the Discovery of AML/CFT Risks
Core Idea
Criminal networks adapt quickly, leveraging new technologies or overlooked loopholes. Automated monitoring and AI-driven threat detection, supported by quality data, help institutions stay proactive rather than playing catch-up.
Why It Matters
Manual reviews or static rule sets cannot effectively catch sophisticated layering schemes or ephemeral account usage. Automation ensures continuous vigilance, updating detection patterns as criminals shift tactics or exploit new financial products.
Long-Term Vision
High-fidelity AI systems alert compliance teams to novel red flags—even those not previously documented in typologies—and seamlessly adjust to shifts in criminal behavior. This includes real-time checks and anomaly-based detection that reduce false positives and accelerate genuine threat identification.
5. Foster Enhanced Collaboration and Knowledge Sharing
Core Idea
AML/CFT often unfolds in siloed contexts. Meanwhile, criminals share best practices on dark web forums or through cross-border accomplices. A more community-minded approach—sharing lessons, data sets (where permissible), red-flag insights, and new laundering techniques—provides a collective edge.
Why It Matters
Criminal organizations can pivot quickly once they discover which methods yield minimal scrutiny. By pooling expertise, errors can be caught faster, reoccurring suspicious methods can be flagged systematically, and new entrants (like fintechs) receive the guidance they need to implement strong defenses early on.
Long-Term Vision
An “open intelligence” dynamic that encourages frequent contribution of anonymized case data, research findings, and detection strategies. The cumulative effect fosters a stronger, more agile AML/CFT network than any one organization could achieve individually.
6. Empower Institutions to Develop Advanced AI-Powered Monitoring Solutions
Core Idea
Organizations of varying sizes often lack the data volume or specialist teams to implement sophisticated machine learning or advanced analytics for AML/CFT. By providing accessible frameworks, sample datasets, or shareable code libraries, smaller FIs can close this capability gap.
Why It Matters
As criminals become more technologically adept, it is not enough for only large global banks to have state-of-the-art solutions. If smaller or mid-tier institutions remain vulnerable, the entire system is weaker. Equalizing access to advanced AML/CFT capabilities levels the playing field.
Long-Term Vision
Open, ethical data-sharing alliances or centrally accessible data “sandboxes,” allowing AI models to be trained on broad patterns without infringing privacy. Over time, robust AI solutions become standard across the board—preventing criminals from targeting “low-tech” institutions as an easy backdoor.
Our “BE SAFE” Vision
By Bridging regulatory language with operational data, Enabling secure cross-border collaborations, Standardizing data formats, Automating risk detection, Fostering knowledge exchange, and Empowering institutions with advanced technology, the AML/CFT community can move from fragmented, reactive approaches to a holistic, scalable defense against illicit finance.
This “BE SAFE” vision does not impose a singular path for all organizations. Instead, it highlights shared objectives—such as consistent data taxonomies, real-time threat intelligence, and AI-based detection—that reinforce resilience across the industry. Whether through small strategic tweaks or ambitious cross-border partnerships, embracing these principles can elevate AML/CFT readiness worldwide.