Analyzing Client Data for AML Risk Patterns

Case Study: A Suspicious Customer – How Would You Handle It? The Scenario Alex, a long-time customer of BrightBank, had maintained a small savings account for years. Recently, Alex started making large cash deposits just under the reporting threshold of $10,000, often splitting deposits across multiple branches in a single day. Shortly after each deposit, the funds were transferred to offshore accounts in jurisdictions known for lax financial regulations. A teller noticed the pattern and flagged it to the compliance team. When BrightBank's AML analysts reviewed Alex's account, they identified the following red flags: Frequent cash deposits just below the threshold. Multiple deposits across branches on the same day. Wire transfers to high-risk countries with minimal documentation. Vague explanations from Alex about the source of funds, citing "business earnings" but refusing to provide documentation. The Bank's Actions; Enhanced Due Diligence (EDD): BrightBank requested detailed information about Alex’s business, including financial records and contracts. Alex provided limited and inconsistent responses. Transaction Monitoring: Analysts set up real-time alerts for Alex’s transactions to track ongoing activities. Suspicious Transaction Report (STR): After gathering evidence, BrightBank filed an STR with the local financial intelligence unit. Account Closure: Due to non-compliance with requests and ongoing suspicion, the bank terminated Alex’s account relationship. Key Lessons; Behavioral Patterns Matter: Structuring transactions to avoid reporting thresholds is a common tactic for money launderers. EDD is Crucial: Asking the right questions and gathering supporting documents can help uncover illicit activities. Timely Reporting: Filing STRs promptly ensures regulatory obligations are met and aids broader investigations. Questions for AML Professionals Behavioral Red Flags: What additional red flags would you look for in this scenario? EDD Best Practices: How do you approach customers who provide vague or incomplete documentation during EDD? Risk Mitigation: What steps can banks take to strengthen their detection of structured transactions like Alex’s? Global Collaboration: How can financial institutions collaborate internationally to track suspicious activities involving offshore accounts? Ethical Dilemma: If Alex had been a high-net-worth client, how might that have influenced the handling of this case?

The payment services sector is evolving rapidly—bringing with it increased financial crime vulnerabilities. The ComplyAdvantage AML Risk Assessment for the Payments Sector provides a detailed examination of emerging risks across different business models and transaction types. It delivers a practical view for #compliance professionals on where threats are intensifying 🌍 Inherent Risk: Geography, Speed & Customer Diversity PSPs inherently carry higher AML/CTF risk due to their structure and function. Key inherent risk drivers include: • Wide and often unverified customer bases • Exposure to high-risk jurisdictions, particularly in cross-border remittances • A lack of long-standing business relationships (many transactions are occasional or one-off) • High transaction volumes with fast settlement speeds • Remote onboarding via digital channels and agent-based distribution networks These factors align with regulatory findings from the EBA and FATF, reinforcing the need for a granular #AML risk assessment framework for PSPs. 🧍 High-Risk Customer Profiles The sector is often used by or caters to: • Non-residents and unbanked individuals • De-risked clients from the banking system • PEPs • High-risk institutional clients such as gambling platforms, crypto exchanges, and crowdfunding service providers This customer segmentation places increased pressure on onboarding controls, ongoing monitoring, and EDD processes, particularly as traditional banking entities continue to exit high-risk client segments. 🧮 Transaction Typologies & Red Flags The report highlights how PSPs are exploited through: • Smurfing (splitting large transactions into smaller ones to avoid detection) • Fund layering through offshore corridors and complex ownership structures • Use of digital wallets and prepaid cards in jurisdictions with weak controls • Movement of funds through shadow intermediaries, creating opaqueness in fund origin and ownership These risks necessitate the deployment of automated transaction monitoring, typology libraries, and real-time behavioural analytics. 🛡️ Sector-Specific Controls Needed such as: • Remote onboarding and digital KYC validation tools • Screening across all agent and sub-agent relationships • Managing the risk of correspondent arrangements (particularly for remittance firms) • Implementing risk scoring at the product, customer, and geographic level The guidance also reinforces the need for risk-based proportionality—not all PSPs are equal, and controls must reflect business models. 📈 Emerging Threats: Tech, Tokens, and Fraud Key emerging threats include: • Use of AI-generated synthetic IDs in onboarding • Fraud typologies blending phishing, money mule networks, and APP fraud • Crypto-enabled payments being used for layering and obfuscation • Increased fraud in BNPL and digital wallets due to insufficient ID checks #financialcrime #regulatory #sanctions ##payments

Today’s AML challenges go beyond catching suspicious transactions, they’re about identifying the people and behavior behind those actions. Financial criminals often use stolen identities or manipulated accounts, requiring AML to take a dual approach: who is behind the activity and how they’re behaving. Historically, these aspects have been siloed. Transaction monitoring systems handled behavior, while identity verification tools validated the person. But without tying these two components together, we lose crucial context. Combining identity verification and behavioral data within AML offers a comprehensive view that’s far more effective. This is where our recent acquisition of Effectiv’s transactional decision engine becomes so powerful. With tools that unify identity and transactional behaviors, we can detect anomalies more accurately, reducing noise and catching the subtle signals of illicit activity. Imagine this in practice: previously, we’d detect possible human trafficking by picking up identities that may have been identified as human traffickers based on a list that was compiled to identify perpetrators. It is incomplete at best. But with integrated transaction monitoring tools, we can spot specific behaviors across identity and transactions, like clusters of activity that match trafficking patterns or indications of manipulated identities. The result? Cleaner data, more accurate detection, and a radically more effective AML operation that recognizes the true behavior of financial crime. In AML, bridging the who and how is more than a step forward - it’s the new standard of security.

🛡️𝟓 𝐓𝐫𝐚𝐧𝐬𝐚𝐜𝐭𝐢𝐨𝐧 𝐏𝐚𝐭𝐭𝐞𝐫𝐧𝐬 𝐓𝐡𝐚𝐭 𝐒𝐜𝐫𝐞𝐚𝐦 𝐌𝐨𝐧𝐞𝐲 𝐋𝐚𝐮𝐧𝐝𝐞𝐫𝐢𝐧𝐠 In transaction monitoring, individual transactions might look normal… but patterns tell the truth. Here are the 5 patterns every AML analyst should instantly recognize 👇 🔴 𝟏. 𝐑𝐚𝐩𝐢𝐝 𝐈𝐧-𝐚𝐧𝐝-𝐎𝐮𝐭 𝐓𝐫𝐚𝐧𝐬𝐟𝐞𝐫𝐬 (𝐒𝐞𝐜𝐨𝐧𝐝𝐬/𝐌𝐢𝐧𝐮𝐭𝐞𝐬) Money comes in → money goes out immediately. This is classic layering and extremely common in mule accounts. 💬 Example: I reviewed a case where ₹12 lakh moved through 6 accounts in under 4 minutes. Customer said: “Urgent business payments.” The behaviour said: layering. 🟠 𝟐. 𝐒𝐭𝐫𝐮𝐜𝐭𝐮𝐫𝐞𝐝 𝐃𝐞𝐩𝐨𝐬𝐢𝐭𝐬 (𝐉𝐮𝐬𝐭 𝐁𝐞𝐥𝐨𝐰 𝐓𝐡𝐫𝐞𝐬𝐡𝐨𝐥𝐝𝐬) Multiple cash deposits like ₹9,95,000 or $9,500 — repeatedly. Criminals avoid triggers but keep the volume high. 💬 Example: Eight ₹9.95L deposits in one week. When questioned: “Just how I like to deposit.” Reality: Placement strategy. 🟡 𝟑. 𝐑𝐞𝐩𝐞𝐚𝐭𝐞𝐝 𝐑𝐨𝐮𝐧𝐝-𝐍𝐮𝐦𝐛𝐞𝐫 𝐓𝐫𝐚𝐧𝐬𝐚𝐜𝐭𝐢𝐨𝐧𝐬 ₹5,00,000 → ₹5,00,000 → ₹5,00,000 Perfect round numbers with no proper reason. Often linked to underground cash movement or hawala-style activity. 🟢 𝟒. 𝐇𝐢𝐠𝐡-𝐑𝐢𝐬𝐤 𝐉𝐮𝐫𝐢𝐬𝐝𝐢𝐜𝐭𝐢𝐨𝐧 𝐇𝐨𝐩𝐬 Funds routed through secrecy havens or lenient AML regions before reaching the final account. 💬 Example: Three offshore jurisdictions → personal account. Legal? Maybe. Suspicious? Absolutely. 🔵 𝟓. 𝐂𝐮𝐬𝐭𝐨𝐦𝐞𝐫 𝐏𝐫𝐨𝐟𝐢𝐥𝐞 𝐌𝐢𝐬𝐦𝐚𝐭𝐜𝐡 This is where AML gets real. The biggest red flags often come from behaviour that does not fit the customer. A “student account” receiving USD 40,000 in crypto A grocery store receiving international wires A dormant account suddenly processing high-value inbound/outbound payments When 𝐚𝐜𝐭𝐢𝐯𝐢𝐭𝐲 ≠ 𝐩𝐫𝐨𝐟𝐢𝐥𝐞, your suspicion should switch on immediately. Not all unusual patterns indicate money laundering but every pattern deserves a second look. In AML, behaviour speaks louder than documentation 🔍 #AML #AntiMoneyLaundering #TransactionMonitoring #FinancialCrime #FinCrime #KYC #CDD #RiskManagement #Compliance #RegTech #ACAMS #FraudPrevention #MonitoringRules #ComplianceCommunity #AMLInsights #LinkedinLearning

How I Prepare for Revolut Fincrime Analyst (FIU – Investigator) Assessment If you're preparing for AML/KYC roles, one thing I learned companies don’t test theory, they test how you think in real scenarios. So here are 15 practical MCQs with answers + short explanations 👇 1. A customer receives ₹3,00,000 and splits it into 5 transactions within minutes. What does this indicate? A. Salary distribution B. Layering C. Refund activity D. Investment Answer: B — Rapid splitting of funds is a classic layering technique to hide origin. 2. A low-income customer shows high-value transactions. What is your first step? A. Ignore B. Block account C. Review KYC & source of funds D. Close alert Answer: C — Always verify income vs activity mismatch before taking action. 3. Multiple deposits of ₹9,500 are made repeatedly. This is: A. Gambling B. Structuring C. Cashback D. Salary Answer: B — Transactions just below threshold indicate structuring to avoid detection. 4. A customer sends funds to a high-risk country frequently. What will you do? A. Ignore B. Close alert C. Conduct risk review D. Wait Answer: C — High-risk geography requires detailed review and possible escalation. 5. Which is a strong indicator of a mule account? A. Low balance B. High inflow-outflow pattern C. ATM usage D. Regular login Answer: B — Mule accounts typically move funds quickly without retention. 6. A customer denies providing source of funds. What next? A. Ignore B. Escalate C. Close alert D. Wait Answer: B — Refusal is a red flag and requires escalation. 7. Transactions are normal but login location changes rapidly across countries. Risk? A. Normal B. Account takeover C. Salary issue D. Refund Answer: B — Sudden geo changes suggest possible account compromise or VPN misuse. 8. A customer receives funds from 10 unknown senders and transfers immediately. This indicates: A. Business activity B. Layering/mule activity C. Investment D. Gift Answer: B — Multiple unknown sources + quick transfers = mule/layering pattern. 9. What matters more in AML review? A. Amount B. Pattern C. Customer age D. Location Answer: B — Patterns reveal intent more than transaction size. 10. A Politically Exposed Person (PEP) is high risk because: A. High salary B. Corruption risk C. Travel frequency D. Public profile Answer: B — PEPs have higher exposure to bribery/corruption risks. 11. Small transactions just below reporting threshold indicate: A. Normal B. Structuring C. Refund D. Salary Answer: B — This is a common tactic to bypass reporting limits. This is how I prepare focusing on patterns, logic, and decision-making instead of just definitions. Don't forget to follow Sambhaji Chougule for more informative AML KYC Insights 🙏 #AML #KYC #Fincrime #FraudDetection #AntiMoneyLaundering #Compliance #RiskManagement #Revolut #InterviewPreparation #FinanceJobs #CareerGrowth #Freshers #JobPreparation #Banking #LinkedInLearning

🇭🇰 Hong Kong SFC flags rising misuse of licensed VATPs for layering Last week the Hong Kong Securities and Futures Commission (SFC) issued a circular highlighting the use of of licensed virtual asset trading platforms (VATPs) and other licensed corporations for "potential layering activities in money laundering." Money laundering typically consists of three phases: 1) Placement, where the funds are introduced into the financial ecosystem 2) Layering, where their origin is obscured using complex transaction patterns 3) Integration, where laundered funds are introduced into the legitimate financial ecosystem "From our supervisory work, the SFC has identified an emerging trend of suspicious fund movements involving frequent and swift fund deposits as well as withdrawals in client accounts maintained with licensed firms. These [...] suggested that the clients might use the accounts maintained with the licensed firms as depositary accounts or conduits for transfers, which could obscure the origin and destination of the funds and constitute layering activities in money laundering," the regulator said. The SFC also highlighted the failure of some licensed firms to detect red flags associated with layering behaviours, with some having disregarded the abovementioned patterns solely on the basis that no third party was involved. The regulator conducted a detailed analysis and identified nine different red flags that licensed firms should be wary of. These include a behavioural pattern inconsistent with client profiles, short business relationships, small and sequential transactions to bypass transaction monitoring thresholds, and more. The circular also reminds licensed firms — including VASPs — of their AML/CFT obligations and sets out clear expectations, including: 🔹 Strengthening transaction monitoring to must detect patterns such as swift in-and-out fund flows, structuring, inactive accounts after withdrawals, and unusual changes to client banking or wallet details. 🔹 Exercising heightened scrutiny on deposits and withdrawals, even when third-party activity is not visible. 🔹 Implementing bank account registration and wallet address whitelisting 🔹 Screening wallet addresses and transactions using appropriate tools including blockchain analytics 🔹 Additional risk-mitigating controls, including limiting withdrawals to the original funding source or imposing short holding periods to prevent immediate layering. This circular is a timely reminder that effective AML/CFT isn’t about ticking boxes — it’s about genuinely understanding behaviour, context, and risk. Controls and transaction monitoring rules only work when paired with good judgment, curiosity, and a willingness to interrogate anomalies rather than explain them away. 📷 : Typhoon shelter crab, a spicy, garlicky crab dish born in the Hong Kong harbour in the 60s and 70s. Photo from Michelin Guide.

P-4 How an AML investigation actually works: how alerts are generated, when investigations start, how analysts review them: 1. Triggering of an Alert An AML investigation does not start randomly; it begins when the monitoring system detects unusual activity that breaks defined rules. Every bank or financial institution sets up Transaction Monitoring Systems (TMS) which automatically scan customer transactions against pre-set thresholds, typologies, and scenarios. Examples of alert triggers: A customer deposits ₹10,00,000 in cash within one day. Multiple smaller deposits (₹49,000, ₹48,000) to avoid reporting threshold → structuring/smurfing. Funds moving frequently between multiple accounts without clear purpose. Transactions with high-risk jurisdictions (tax havens, sanctioned countries). Unusual activity compared to the customer profile (e.g., student account receiving heavy business transfers). When such a red flag is detected, the system generates an alert. 2. Case Creation Once an alert is generated, it is sent to the AML Case Management System where it becomes a “case” assigned to an investigator. The case file usually includes: Transaction details (date, time, amount, counterparties). Customer profile (KYC data, occupation, income level, country). Historical account activity. Any existing risk rating (high, medium, low). 3. Start of Investigation An AML investigator/analyst begins by reviewing the case step by step: Step 1: Understand the Customer Who is the customer? (individual or corporate) What is their risk profile? (low, medium, high) What does their business or occupation suggest about expected transactions? Step 2: Analyze the Alert Transaction(s) Was it a one-time anomaly or repeated pattern? Does the transaction make sense compared to the customer’s background? Are there signs of structuring, layering, or shell company involvement? Step 3: Perform Screening Run the customer and counterparties through sanctions lists (OFAC, UN, EU, RBI). Check for PEP (Politically Exposed Person) status. Conduct Adverse Media checks for negative news (fraud, gambling, drug trafficking, etc.). Step 4: Gather Additional Information Request documents from the Relationship Manager (e.g., invoices, contracts). Compare with expected customer behavior. Decision-Making in Investigation Once analysis is complete, the investigator must decide the outcome of the alert. Possible outcomes: False Positive True Positive (Suspicious Case) Request for Information (RFI) #Check #P-5 for next step AML #KYC #EDD #TransactionMonitoring #FalsePositive #FinancialCrime #AntiMoneyLaundering #Compliance #RiskManagement #SanctionsScreening #STR #SAR #CDD #KYB #FIU #FATF #BSA #AMLInvestigation #FraudPrevention #CFT #AMLTraining