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AML Investigations In Practice: From Red Flags To Case-Building – What Can We Do?

by Chee Shi
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TL;DR

  • AML work is a discipline, not a hunch. Start with a risk-based scope, validate red flags against expected behavior, then triangulate with documents and data.
  • Convert observations into a defensible hypothesis, track evidence in a traceable matrix, and escalate via clear reporting standards.
  • Maintain legal/ethical controls (privacy, proportionality), and practice continuous learning because typologies evolve.

Who This Is For

  • Compliance analysts and investigators (1–5 years experience)
  • MLROs, FIU/forensics teams
  • Fintech risk, payments, and crypto compliance leads

A Practical Workflow (At-a-Glance)

  1. Trigger & Scoping: Identify the trigger (alert, tip, negative news, law-enforcement request) and define boundaries: entity, period, products, geos.
  2. Baseline & Expected Behavior: Establish customer profile (KYC, nature of business, historical patterns) to anchor what “normal” looks like.
  3. Red-Flag Review: Map signals to categories (behavioral, transactional, network, documentation).
  4. Evidence Harvest: Pull documents and data sources; verify authenticity.
  5. Link Analysis: Connect entities/transactions; build relationship graphs; test alternative explanations.
  6. Hypothesis Draft: Frame a provisional theory; define what would confirm or falsify it.
  7. Case File Build: Write factual narrative, attach exhibits, complete evidence matrix.
  8. Decision & Escalation: Recommend close/monitor/SAR-STR filing/account action.
  9. Post‑Mortem: Feed insights back to monitoring rules and KYC playbooks.

Tip: Use a time‑boxed, risk‑proportional approach: higher inherent risk → deeper expansion; lower risk → narrow, quick close with rationale.

Understanding Money Laundering in Context (Sharper)

The classic stages: placement → layering → integration. In practice, these are messy and may overlap (e.g., instant layering via cross‑border e‑wallet hops).

Business impact: Unchecked illicit flows distort prices, funding, competition, and tax bases; systemic risk grows when controls are weak.

Why it’s hard: Criminals variabilize patterns (randomized amounts/timing, mule chains, synthetic invoices) and exploit jurisdictional arbitrage.

Red Flags: From “Interesting” to “Actionable”

Categorize and score red flags based on customer expected behavior, inherent risk, and explainability.

A. Behavioral

  • Unwillingness to provide source of funds/wealth or contradictory stories.
  • Frequent changes to control/ownership without commercial rationale.
  • Third parties leading all comms for an ostensibly independent client.

B. Transactional

  • Structuring/smurfing near reporting thresholds.
  • Rapid in-out flows with minimal balances (pass‑through accounts).
  • Circular payments among related entities; payments with vague descriptions (e.g., “consultancy”).
  • High‑risk geographies with no clear nexus to customer business.

C. Network/Counterparty

  • Payments to/from newly formed shell entities, common addresses/phones/IPs.
  • Shared directors/UBOs across supposed competitors.
  • Repeated use of the same intermediary banks or PSPs to obfuscate.

D. Documentation & KYB/KYC

  • Invoices inconsistent with industry norms (units, prices, shipping terms).
  • Mismatched signatures, fonts/metadata anomalies; recycled templates.
  • Tax docs or permits that do not align with stated business activity.

Convert each red flag into a testable question (“If legitimate, we would expect…”) and seek corroboration or refutation.

Evidence Sources & What to Look For

  • Core banking/ledger: timestamps, counterparties, MT/MX message fields, reference notes.
  • KYC/KYB: beneficial ownership, control ladders, risk ratings, onboarding rationale.
  • Trade docs (for goods/services): contracts, invoices, BL/AWB, packing lists, Incoterms; reconcile quantities, routes, and payment terms.
  • Open‑source & commercial databases: corporate registries, sanctions/PEP lists, adverse media, court filings.
  • Device/telemetry (where permissible): IPs, device IDs, geolocation consistency.
  • Communications (with approval): unusual channel shifts or scripted replies.

Always authenticate: hash values for digital files, registry confirmations for entities, and cross‑checks with third‑party verifiers.

Linking Suspicious Activities (Doing the Analysis)

  • Build a relationship graph: nodes = persons/entities/accounts; edges = funds flows/ownership/control; include attributes (jurisdiction, risk tags).
  • Apply temporal sequencing: create a timeline; identify bursts, cycles, and event‑driven transfers.
  • Perform counter‑factual tests: “If this was a genuine trade, what shipment or service activity should exist?”
  • Use peer clustering: compare to look‑alike customers (industry, size, region) to detect outliers.
  • Validate with alternative data: vessel/air cargo trackers, customs stats, company filings, web presence.

Legal & privacy guardrails: Minimize data, log access, respect jurisdictional constraints, and ensure need-to-know sharing.

Deciphering Transaction‑Masking Techniques (with Examples)

  • Shell/Shelf Companies: Dormant or zero‑employee entities controlling large flows. Check: operational footprint (website, payroll, utility), director history, filings.
  • Layering via Chains: Multi‑hop transfers across PSPs/e‑wallets/crypto rails. Check: hop latency patterns; consistent memos or amounts.
  • Over/Under‑Invoicing: Mispriced goods/services to move value. Check: unit prices vs market benchmarks; logistics mismatch with declared goods.
  • Back‑to‑Back Loans: Circular lending to disguise proceeds. Check: loan agreements, interest flows, collateral reality.
  • Complicit Professionals: Nominee directors, formation agents. Check: recurrence across networks; shared addresses.
  • Mule Networks: High‑velocity pass‑through personal accounts. Check: salary patterns absent; ATM cash‑outs, device/IP commonality.

From Observations to a Defensible Hypothesis

Frame a hypothesis that is specific, falsifiable, and proportional:

“Between Mar–Jun 2025, Entity A appears to have layered ~US$2.1m through 6 newly formed vendors lacking operational footprint, with circular flows returning to a control entity within 7–10 days.”

Include:

  • Scope (entities, period, products),
  • Mechanism (e.g., sham services + pass‑through layering),
  • Key evidence (docs, transaction clusters, linkages),
  • Confidence level (High/Med/Low) and what would raise/lower it.

Building the Case File (Structure That Works)

  1. Executive Summary (1 page): who/what/why now; recommended action.
  2. Parties & Accounts: identifiers, roles, relationships diagram.
  3. Background: onboarding/KYC facts; expected vs observed behavior.
  4. Detailed Findings: organized by theme (transactions, network, docs).
  5. Risk Assessment: inherent vs residual; sanctions/PEP exposure.
  6. Legal/Ethical Considerations: privacy, tipping‑off risks, jurisdictional notes.
  7. Recommendations: close/monitor/SAR‑STR; account measures; rule updates.
  8. Appendices: exhibits, timelines, evidence matrix, methodology, glossary.

Write facts‑first: avoid conclusions without explicit evidence citations; separate observations from interpretation.

Decision & Escalation

  • No Action / Close: When benign explanation is corroborated; document rationale.
  • Enhanced Monitoring: Set concrete triggers (e.g., next counterparty list, amount caps).
  • File SAR/STR: Follow jurisdictional requirements (deadlines, content, no tipping‑off). Include clear narrative: who, what, when, where, how, why suspicious.
  • Account Actions: Restrict, offboard, or freeze (as permitted by law and policy).

Governance, Controls, and Ethics

  • Independence: Separate first‑line commercial pressure from second‑line investigations.
  • Records: Retention per policy; auditable workpapers.
  • Quality Review: Peer and MLRO sign‑offs for material cases.
  • Training: Typology refreshers, red‑flag calibration, writing defensible narratives.
  • Metrics: Time‑to‑decision, SAR conversion, re‑alert rate, false‑positive ratio.

Common Pitfalls (and Fixes)

  • Red‑flag dumping without prioritization → Score and sequence actions.
  • Assumption bias → Run falsification tests; invite peer challenge.
  • Over‑collection → Collect proportionally; focus on probative value.
  • Incoherent narratives → Use consistent structure and cross‑references.
  • Tool over‑reliance → Combine analytics with human judgment.

Mini Case Study (Illustrative)

Trigger: Alert for repeated same‑day credits and debits.

Context: SMB “consultancy,” 6 months old, no staff on payroll.

Findings:

  • 8 counterparties formed within 90 days, all sharing a incorporation agent.
  • Invoice descriptions generic; amounts round; no VAT/GST evidence.
  • Funds cycle back to a holding entity after 6–9 days via two PSPs.

Hypothesis: Layering of illicit proceeds via sham services.

Decision: File STR; offboard with notice; adjust rules to flag newly formed vendor clusters + round‑number cycles.

Quick Checklists

Red‑Flag Validation

  • Is there a plausible, documented commercial rationale?
  • Do flows match the business model and seasonality?
  • Are docs authentic and consistent across sources?

Case File Readiness

  • Executive summary states who/what/why now.
  • Evidence matrix complete with reliability ratings.
  • Narrative separates facts from analysis.
  • Escalation recommendation is specific and lawful.

Final Thought

Great AML investigations balance skepticism with fairness. The goal isn’t to confirm suspicion—it’s to test it thoroughly, document the path, and take proportionate action that protects the financial system without over‑reaching.