Introduction
Financial crime has evolved. So must the defense.
As fraud and money laundering schemes become increasingly sophisticated and intertwined, financial institutions (FIs) can no longer afford to address Anti-Money Laundering (AML) and Fraud Management in silos. The convergence of these domains—referred to as FRAML (Fraud and Anti-Money Laundering)—has become not just a regulatory expectation but a business imperative.
That’s why leading FIs are gradually shifting to FRAML 2.0—an integrated, AI-powered, customer-centric approach to financial crime risk management. It’s not just an acronym anymore—it’s a strategy for resilience, reputation, and ROI.
What Is FRAML and Why Now?
FRAML (Fraud and Anti-Money Laundering) unites two critical risk functions into a single, integrated model. While historically managed separately, these domains now face shared threats, overlapping data, and similar regulatory expectations.
The market shift is clear:
- In 2023, global fines for AML failures topped $6 billion.
- Up to 5% of corporate revenue is lost to fraud annually—nearly $4.7 trillion worldwide.
- Fraud losses in the US alone reached $5.8 billion, with synthetic identity fraud accounting for 85% of all fraud.
Yet, most financial institutions continue to operate fraud and AML as separate programs—with duplicated infrastructure, disjointed analytics, and delayed investigations. This siloed approach not only increases operational costs but also creates blind spots that criminals can exploit. FRAML solves this.
The Rising Complexity of Financial Crime
Financial criminals are tech-savvy, globally connected, and fast-moving. Their tactics evolve faster than traditional controls can adapt. Four forces are driving convergence:
- Fraudsters Are Blurring the Lines: Criminals use fraudulent transactions to layer illicit funds. Identity theft leads to account takeovers (ATO) that evolve into money laundering operations.
- Cross-Channel Sophistication: Fraud now involves payments, cards, online banking, and mobile apps—often in the same incident.
- Regulators Are Demanding Holistic Risk Visibility: Global regulators—from FinCEN to FATF—expect financial institutions to show enterprise-wide financial crime visibility, not departmental snapshots.
- Legacy Tools Are Obsolete: Siloed legacy systems lead to duplicated alerts, disjointed investigations, and operational inefficiencies. They are incapable of detecting multi-dimensional threats or enabling adaptive risk-based responses.
Not All Frauds Are the Same: Challenges Across Banking and Payments
A key reason siloed systems fail is because fraud types differ dramatically by sub-sector. There’s no “one-size-fits-all” approach to fraud and AML.
In Retail Banking:
- First-party fraud (e.g., false disputes)
- Synthetic identity creation for credit abuse
- Account takeover of dormant or low-balance accounts
In Payments & Fintech:
- Real-time payments enable faster fraud with limited clawback windows
- Authorized Push Payment (APP) scams are harder to detect
- High-volume, low-value transactions increase noise and false positives
In Commercial and Corporate Banking:
- Trade-based money laundering and shell company structures
- Large-amount fraud masked as legitimate invoice payments
- Complex correspondent banking networks obscuring fund trails
Each fraud type requires different data signals, detection logic and alert management priorities. Yet, FRAML enables these to be unified through shared infrastructure and AI models tailored by segment.
Introducing the FRAML 2.0 Framework
Sutherland’s FRAML 2.0 framework spans the full financial crime value chain—from prevention and detection to investigation and reporting. It includes:
1. Unified Risk Data Layer
Integrates structured and unstructured data from KYC, transaction logs, behavioral biometrics, device IDs, and external watchlists.
2. AI/ML-Driven Detection Engine
- Behavior-based anomaly detection
- Identity clustering
- Graph analytics for mule network detection
3. Smart Alert Prioritization
Combines fraud and AML signals to flag alerts with the highest financial and reputational impact.
4. Case Management and SAR Filing
- Consolidated investigation workflows
- Integrated SAR creation tools
- Explainable AI for regulatory transparency
5. Continuous Feedback Loop
- Analyst input feeds back into the ML models
- Enables constant tuning, leading to higher precision
How AI and Data Are Powering FRAML 2.0
Intelligent Data Fusion
Our FRAML platform uses data orchestration layers that aggregate customer, transaction, device, behavioral, and third-party intelligence into a single view. This “360-degree risk profile” is foundational for convergence.
AI-Driven Detection & Alert Prioritization
Machine Learning (ML) and behavioral analytics are used to detect suspicious patterns in real time. Techniques such as identity clustering, anomaly detection, and graph analytics uncover complex fraud and laundering rings—faster and more accurately than rules alone.
Explainable AI for Compliance
With explainable AI models, our FRAML platform can justify why an alert was triggered—making it easier for investigators and satisfying regulators who require transparency in algorithmic decisioning.
Continuous Model Improvement
Platforms like Sutherland FRAML, built on agentic AI and proprietary risk intelligence, incorporate feedback loops to constantly refine detection models
Sutherland’s proprietary tools deliver. Here’s how:
- Sutherland Sentinel AI for secure data masking and compliance monitoring
- Sutherland HelpTree GenAI assistant for investigator guidance and knowledge surfacing
- Sutherland FinTelligent engine for anomaly detection and pattern recognition
For example:
- A European P2P lender saw a 50% drop in transaction monitoring remediation time.
- A global blockchain-based financial institution achieved an 80% increase in onboarding efficiency through AI-powered document verification.
The Benefits of Convergence
| Key Benefit | Measurable Impact |
|---|---|
| Lower Total Cost of Compliance | Up to 30% TCO reduction through shared infrastructure |
| Reduced False Positives | Up to 70% reduction in false positives using ML-based alerting |
| Faster Time to Resolution | Smart triage and case routing reduce investigation lag by 50% |
| Improved CX | Seamless onboarding with 80% lower drop-off rates |
| Regulatory Confidence | End-to-end audit trails and explainable AI for SARs and case reviews |
Why Sutherland’s Approach to FRAML Stands Out
Sutherland offers an integrated model for FRAML—combining consulting, operations, and technology under one roof. What sets it apart:
- 2,400+ SMEs in financial crime, fraud, and risk ops
- 50+ pre-built fraud scenarios across sectors
- Real-time transaction monitoring integrated with industry-leading data sources
- Full ecosystem of capabilities from identity proofing and payment screening to SAR submission and forensic audits
- Modular deployment: from startups to Tier 1 banks, tailored to each institution’s risk profile
Closing Thoughts
FRAML 2.0 isn’t just a compliance strategy—it’s a competitive advantage. As fraudsters become increasingly sophisticated and regulators more demanding, financial institutions that adopt an integrated approach will lead the industry in trust, security, and customer experience.
Whether you’re a mid-market digital bank or a global fintech processing millions of transactions a day, the FRAML mindset applies—and pays off.
It’s time to connect the dots.
References
- Gartner, “Market Guide for Anti-Money Laundering Solutions,” 2024
- Association of Certified Fraud Examiners, “Report to the Nations,” 2023
- McKinsey & Company, “Synthetic Identity Fraud: A Growing Threat,” 2023
- FATF Guidelines on AML & Fraud Risk Convergence, 2023
