Fraud Risk Assessment

If my boss asked me to "assess our risk surface area and fraud priorities", this is how I would get it done by 5PM tomorrow. Step by step process. 1 - Pull our last 90 days of fraud data. Not just the obvious stuff like chargeback rates, but the full spread: login attempts, account creation patterns, payment declines... everything. Why 90 days? Because fraudsters love to exploit seasonal patterns, and we need that context. 2 - Map out every single entry point where money moves. I'm talking checkout flows, refund processes, loyalty point redemptions... even those "small" marketing promotion codes everyone forgets about. (Fun fact: I once found a six-figure exposure in a forgotten legacy gift card system) 3 - Time for some real talk with our front-line teams. Customer service reps, payment ops folks, even the engineering team that handles our API integrations. These people see the weird edge cases before they show up in our dashboards. 4 - Create a heat map scoring each entry point on three factors: → Financial exposure (how much could we lose?) → Attack complexity (how hard is it to exploit?) → Detection capability (can we even see it happening?) 5 - Cross-reference our current fraud rules and models against this heat map. Brutal honesty required here – where are our blind spots? Which high-risk areas are we treating like low-risk ones? 6 - Pull transaction data for our top 10 riskiest areas and run scenario analysis. If fraud rates doubled tomorrow, what would break first? (It's usually not what leadership thinks) 7 - Document our current resource allocation vs. risk levels. Are we spending 80% of our time on 20% of our risk? Been there, fixed that. 8 - Draft a prioritized roadmap based on: → Quick wins (high impact, low effort) → Critical gaps (high risk, low coverage) → Strategic investments (future-proofing our defenses) 9 - Prepare three scenarios for leadership: → Minimum viable protection → Balanced approach → Fort Knox mode Because let's be real, budget conversations need options. 10 - Package it all up with clear metrics and KPIs for each priority area. Nothing gets funded without numbers to back it up. ps... Make it visual. Leadership loves a good heat map, and it makes complex risk assessments digestible. Trust me on this one

𝐊𝐢𝐧𝐠 𝐕 𝐡𝐚𝐬 𝐣𝐮𝐬𝐭 𝐜𝐡𝐚𝐧𝐠𝐞𝐝 𝐭𝐡𝐞 𝐠𝐚𝐦𝐞 𝐟𝐨𝐫 𝐄𝐭𝐡𝐢𝐜𝐬 𝐚𝐧𝐝 𝐅𝐫𝐚𝐮𝐝 𝐑𝐢𝐬𝐤 𝐆𝐨𝐯𝐞𝐫𝐧𝐚𝐧𝐜𝐞. The conversation has shifted dramatically: ❌ 𝐍𝐨 𝐥𝐨𝐧𝐠𝐞𝐫: "Do you have ethics policies?" ✅ 𝐍𝐨𝐰: "Can you evidence their impact on ethical culture?" King V doesn't take your word for it. It demands tangible proof. 𝐒𝐢𝐱 𝐜𝐫𝐢𝐭𝐢𝐜𝐚𝐥 𝐜𝐡𝐚𝐧𝐠𝐞𝐬 𝐄𝐭𝐡𝐢𝐜𝐬 𝐚𝐧𝐝 𝐅𝐫𝐚𝐮𝐝 𝐑𝐢𝐬𝐤 𝐏𝐫𝐚𝐜𝐭𝐢𝐭𝐢𝐨𝐧𝐞𝐫𝐬 𝐨𝐮𝐠𝐡𝐭 𝐭𝐨 𝐤𝐧𝐨𝐰: 1. 𝐄𝐭𝐡𝐢𝐜𝐬 𝐢𝐬 𝐚 𝐛𝐨𝐚𝐫𝐝‑𝐥𝐞𝐯𝐞𝐥 𝐊𝐏𝐈: Culture indicators, leadership behaviour, and whistleblowing responsiveness are now measurable governance outcomes that require evidence-based reporting. 2. 𝐃𝐢𝐠𝐢𝐭𝐚𝐥 𝐞𝐭𝐡𝐢𝐜𝐬 𝐢𝐬 𝐚 𝐠𝐨𝐯𝐞𝐫𝐧𝐚𝐧𝐜𝐞 𝐫𝐞𝐪𝐮𝐢𝐫𝐞𝐦𝐞𝐧𝐭: AI-enabled fraud, algorithmic bias, deepfakes, and data manipulation are recognised as core governance risks, reflecting how fraud has evolved into digital ecosystems. 3. 𝐅𝐫𝐚𝐮𝐝 𝐫𝐢𝐬𝐤 𝐢𝐬 𝐞𝐦𝐛𝐞𝐝𝐝𝐞𝐝 𝐬𝐭𝐫𝐚𝐭𝐞𝐠𝐢𝐜𝐚𝐥𝐥𝐲: King V integrates fraud risk across the entire value chain, from supply chain vulnerabilities to ESG reporting integrity, making it a strategic imperative rather than an operational function. 4. 𝐋𝐞𝐚𝐝𝐞𝐫𝐬𝐡𝐢𝐩 𝐚𝐜𝐜𝐨𝐮𝐧𝐭𝐚𝐛𝐢𝐥𝐢𝐭𝐲 𝐢𝐬 𝐞𝐧𝐟𝐨𝐫𝐜𝐞𝐚𝐛𝐥𝐞: Boards must demonstrate proactive consequence management, transparent conflict oversight, and ethical decision-making frameworks as governance requirements. 5. 𝐂𝐨𝐦𝐛𝐢𝐧𝐞𝐝 𝐚𝐬𝐬𝐮𝐫𝐚𝐧𝐜𝐞 𝐢𝐧𝐜𝐥𝐮𝐝𝐞𝐬 𝐞𝐭𝐡𝐢𝐜𝐬 𝐚𝐧𝐝 𝐟𝐫𝐚𝐮𝐝: The first, second, and third lines of defence must align on ethics, fraud risk, compliance, and technology controls. Fragmented assurance is a governance failure. 6. 𝐄𝐒𝐆 𝐢𝐧𝐭𝐞𝐠𝐫𝐢𝐭𝐲 𝐜𝐨𝐧𝐧𝐞𝐜𝐭𝐬 𝐭𝐨 𝐟𝐫𝐚𝐮𝐝 𝐠𝐨𝐯𝐞𝐫𝐧𝐚𝐧𝐜𝐞: Carbon credit fraud, greenwashing, and climate-related misstatements are explicitly recognised as ethics and fraud risks requiring governance oversight. 𝐓𝐡𝐞 𝐬𝐡𝐢𝐟𝐭: Ethics, technology, and fraud governance have converged. Organisations must demonstrate credibility through evidence, not just compliance documentation.

⚡ How Banks Detect Card Fraud in Under 100 ms Every time you tap your card, an incredible amount of analysis happens before your transaction is approved—usually in less than 100 milliseconds. Let's see what happens behind the scenes. 💳 Step 1: Transaction Initiated You tap your card at a POS terminal. An ISO 8583 authorization request is created and sent through: POS Terminal ↓ Acquirer Bank ↓ Visa / Mastercard ↓ Issuer Bank The issuer now has only a few milliseconds to decide whether the transaction is genuine. 🧠 Step 2: Fraud Engine Takes Over Before checking your account balance, the issuer's Fraud Detection Engine evaluates the transaction using hundreds of rules and AI models. It analyzes signals such as: 📍 Location Check Is the transaction happening in a location consistent with your recent activity? Example: A purchase in London just minutes after one in Delhi is suspicious. 💰 Transaction Amount Is the amount unusual for this cardholder? ⚡ Velocity Check Have there been multiple transactions within a very short time? Example: 5 purchases in 2 minutes. 🏪 Merchant Category (MCC) Does the merchant type match your normal spending behavior? 📱 Device & Channel Is this a trusted device or payment channel? 📊 Behavioral Analysis Does this transaction fit your historical spending pattern? 🚫 Blacklist & Watchlists Is the card, merchant, IP address, or device already flagged? 🤖 Step 3: AI Generates a Risk Score All these checks are combined to calculate a risk score. Risk Score < 30 ↓ Approve ✅ Risk Score 30–70 ↓ Step-up Authentication (OTP / 3DS) Risk Score > 70 ↓ Decline ❌ This decision is made in just a few milliseconds. ⏱️ Example Timeline 0 ms → Card tapped 20 ms → Authorization reaches issuer 45 ms → Fraud engine evaluates risk 75 ms → Decision made 95 ms → Response reaches POS The customer only notices a brief "Processing..." message, while the bank has already analyzed hundreds of data points. 🛡️ Why It Matters Modern fraud detection isn't based on a single rule. Banks use a combination of: Rule-based engines Machine Learning models Real-time behavioral analytics Device fingerprinting Historical transaction patterns to stop fraudulent transactions before money leaves the account. 💡 Key Takeaway Banks don't just check your balance—they evaluate every transaction against hundreds of risk signals in under 100 milliseconds before deciding whether to approve or decline it. Every time you tap your card, an AI-powered fraud engine races against the clock—analyzing hundreds of signals and making a decision in under 100 milliseconds. That's the invisible technology protecting billions of transactions every day.

FATF Raises the Bar: Cyber-Enabled Fraud Is Now an AML Priority 🚨 FATF’s latest report (24 Feb 26) on the growing threat of cyber-enabled fraud makes one thing clear: digitalisation has transformed fraud from isolated scams into scalable, cross-border criminal ecosystems. 90% of the jurisdictions assessed by the FATF, have identified fraud as a major money laundering risk. Instant payments. Social engineering. Virtual assets. Synthetic identities. The speed of illicit flows now outpaces traditional controls. What’s new? 🔎 → Fraud proceeds are increasingly intertwined with ML, TF and PF risks → Criminals exploit payment rails, VASPs and weak beneficial ownership transparency → Exploits regulatory fragmentation across jurisdictions FATF’s message to practitioners is direct: 🔎 → Integrate cyber-enabled fraud into your AML risk assessments → Enhance real-time monitoring and rapid freezing mechanisms → Strengthen public-private intelligence sharing → Close transparency gaps across payment chains and virtual assets Keen to hear your insights, especially on breaking down internal silos 👇 The institutions that adapt fastest will define the next phase of financial crime resilience. Stay tuned!

The UK’s Financial Conduct Authority (#FCA ) released its latest report, “Assessing and Reducing the Risk of Money Laundering Through the Markets (MLTM)”, on January 23, 2025. This comprehensive report revisits the findings of the 2019 thematic review (TR19/4) and provides updated insights into MLTM risks, firm practices, and regulatory expectations. Key Highlights 1. Renewed MLTM Risk Assessment • The FCA emphasizes that capital markets continue to present avenues for laundering criminal proceeds, especially through complex financial transactions. • Risks highlighted include anonymity of trades, high transaction volumes, cross-border complexities, and the growing sophistication of criminals. #MoneyLaundering #FinancialMarkets 2. Case Studies on MLTM Typologies The report provides practical examples of MLTM cases, including: • Pre-arranged trades: Designed to obscure the origin of funds. • Wash and mirror trading: Used to create an illusion of legitimate trading. • Circular transactions: Executed across multiple entities to confuse audit trails. These examples demonstrate why #Compliance in identifying and preventing suspicious activities. 3. Firm Shortcomings and Expectations • Deficiencies noted: • Inadequate business-wide risk assessments (BWRA) tailored to specific business models. • Over-reliance on counterparties for due diligence without proper oversight. • Weak transaction monitoring (TM) systems that fail to identify MLTM risks effectively. • Insufficient customer risk assessments (CRA), with poorly documented methodologies. #RiskManagement • FCA’s Expectations: Firms must: • Enhance governance structures and ensure active senior management involvement. • Implement robust, tailored TM and KYC processes. • Utilize newly available data-sharing provisions under the Economic Crime and Corporate Transparency Act (ECCTA) to improve collaboration. 4. Recommendations and Next Steps • Firms are encouraged to adopt innovative technology solutions like AI for transaction monitoring and to increase collaboration across teams (front office, compliance, and TM). • Regulators and firms must engage in better information sharing to mitigate emerging risks. These actions highlight the importance of staying vigilant in the fight against financial crime. #RegulationUpdates 5. Training and Awareness • Tailored financial crime training is critical. The FCA noted that many firms still lack role-specific guidance, red flag indicators, and training tailored to their business models. 📚

How to Implement a Robust VACCP (Vulnerability Assessment and Critical Control Points) Plan Ensuring food safety is not just about preventing contamination—it’s also about protecting against food fraud. Economically Motivated Adulteration (EMA) continues to be a global concern, affecting raw material integrity, product authenticity, and consumer trust. To systematically address this risk, VACCP (Vulnerability Assessment and Critical Control Points) is an essential tool for food businesses. This structured approach helps identify, assess, and mitigate risks associated with food fraud. Steps to Carry Out a VACCP 1. Identify High-Risk Raw Materials (RMs) Focus on ingredients that are historically prone to adulteration, economically attractive for fraud, or sourced from high-risk regions. Common examples include milk powder (melamine), honey (sugar syrup), olive oil (dilution), black pepper (papaya seed adulteration), seafood (formalin preservation), and organic spices (pesticide residues). 2. Assess the Vulnerability of Each RM Use a structured risk assessment matrix with key questions (FSSC 22000 Guidelines): -> Is there a strong economic incentive for fraud? -> Are there known fraud cases for this material, supplier, or region? -> How difficult is it to detect fraud (lack of routine testing)? -> Is there unsecured access to raw materials in the supply chain? -> Is the supplier relationship short-term or spot-buying? -> Does the supplier lack independent fraud certifications? -> Is the supply chain complex (multiple intermediaries, high-risk regions)? Assign scores (1–5) to each question and calculate the Vulnerability Score. 3. Categorize Risk Levels and Define Actions Low Risk (7-14) → No immediate action required. Moderate Risk (15-21) → Track the supply chain more closely. High Risk (22-28) → Increase testing for adulteration. Extreme Risk (29-35) → Implement increased audits, fraud assessment using the SSAFE tool, or find an alternate supplier. 4. Take Targeted Mitigation Actions -> Supplier Engagement & Audits: Conduct regular supplier audits to verify compliance. -> Testing Protocols: Implement targeted analytical tests (e.g., isotope testing for honey, DNA testing for spices, GC-MS for oil adulteration). Strengthen Contracts: Include strict clauses on authenticity, fraud prevention, and traceability requirements. -> Enhance Traceability: Use blockchain or digital traceability systems where feasible. -> Develop Alternative Supplier Strategies: Reduce dependency on high-risk suppliers. 5. Annual Review & Continuous Monitoring A VACCP plan should be reviewed annually or whenever new fraud risks emerge. Ensure corrective actions are in place for suppliers with increasing vulnerability scores. Attached: Example VACCP Risk Matrix & Assessment Food fraud is evolving, and so should our risk mitigation strategies. A strong VACCP approach helps build trust, ensure compliance, and protect consumers.

Fraud vs AML: Same Battlefield, Different Missions Many professionals entering Financial Crime assume Fraud and AML are interchangeable. They aren’t. But in live operations — especially in digital payments — they collide every single day. 🔎 Practical Scenario (Real-World Pattern) A new customer onboarded: ✔ KYC completed ✔ Low initial risk rating ✔ Small, normal transactions for a few days Then suddenly: • Multiple high-value inward transfers from unrelated parties • Funds withdrawn or transferred out within minutes • Logins from new devices / IP shifts • Rapid addition of beneficiaries Now the internal debate begins. ⸻ 👉 Fraud Team Perspective • Possible mule account • Account takeover indicators • Immediate monetary loss risk Primary objective: Protect funds. Action: Freeze, restrict, investigate urgently. ⸻ 👉 AML Team Perspective • Structuring pattern • Layering behaviour • Source of funds inconsistencies • Transaction activity inconsistent with profile Primary objective: Regulatory protection. Action: Trigger EDD, review for STR/SAR filing, escalate if needed. ⸻ 🔥 The Key Difference Fraud asks: “Will we lose money right now?” AML asks: “Is the financial system being abused?” Fraud is reactive and time-sensitive. AML is risk-based and regulatory-focused. But here’s the connection: Weak onboarding today → Fraud exposure tomorrow → AML escalation later. Financial crime control is an ecosystem. Silos create blind spots. ⸻ What I’m Observing In my current fraud risk exposure, I’ve noticed that early behavioural signals — device mismatch, velocity changes, transaction clustering — often predict AML review weeks later. Financial crime work is not just investigation. It is behavioural analytics + lifecycle risk monitoring. If you’re preparing for AML or Fraud interviews, don’t just define the terms. Explain how they intersect in real operational scenarios. That’s what differentiates theory from experience. #FinancialCrime #AML #FraudPrevention #KYC #RiskManagement #Compliance #Fintech #revolut #hiring #jobs #interview

𝗪𝗵𝘆 𝗛𝗶𝗴𝗵-𝗩𝗮𝗹𝘂𝗲 𝗧𝗿𝗮𝗻𝘀𝗮𝗰𝘁𝗶𝗼𝗻𝘀 𝗚𝗲𝘁 𝗙𝗹𝗮𝗴𝗴𝗲𝗱 𝗳𝗼𝗿 𝗙𝗿𝗮𝘂𝗱 High-value transactions often trigger fraud alerts, leading to declined payments, frustrated customers, and lost revenue. But why does this happen, and how can merchants improve approval rates without increasing fraud risk? 𝗪𝗵𝘆 𝗔𝗿𝗲 𝗧𝗵𝗲𝘆 𝗙𝗹𝗮𝗴𝗴𝗲𝗱? Issuers and fraud systems are designed to err on the side of caution when it comes to large purchases. Here’s why ⤵️ 🔹 𝗗𝗲𝘃𝗶𝗮𝘁𝗶𝗼𝗻 𝗳𝗿𝗼𝗺 𝗦𝗽𝗲𝗻𝗱𝗶𝗻𝗴 𝗣𝗮𝘁𝘁𝗲𝗿𝗻𝘀 → If a cardholder rarely spends over $500, a $3,000 purchase is atypical to fraud detection systems. 🔹 𝗜𝗻𝗰𝗿𝗲𝗮𝘀𝗲𝗱 𝗙𝗿𝗮𝘂𝗱 𝗥𝗶𝘀𝗸 → Fraudsters often attempt large-value purchases before a stolen card is reported. 🔹 𝗔𝘂𝘁𝗵𝗼𝗿𝗶𝘇𝗮𝘁𝗶𝗼𝗻 𝗟𝗶𝗺𝗶𝘁𝘀 & 𝗩𝗲𝗹𝗼𝗰𝗶𝘁𝘆 𝗖𝗵𝗲𝗰𝗸𝘀 → Some issuers impose limits on daily or per-transaction spend, automatically flagging high-value payments. 🔹 𝗚𝗲𝗼𝗴𝗿𝗮𝗽𝗵𝗶𝗰 & 𝗠𝗲𝗿𝗰𝗵𝗮𝗻𝘁 𝗖𝗮𝘁𝗲𝗴𝗼𝗿𝘆 𝗧𝗿𝗶𝗴𝗴𝗲𝗿𝘀 → A high-value purchase from an unexpected location (or an MCC linked to high fraud rates) can increase risk scores. 🔹 𝗠𝗶𝘀𝗺𝗮𝘁𝗰𝗵𝗲𝗱 𝗔𝘂𝘁𝗵𝗲𝗻𝘁𝗶𝗰𝗮𝘁𝗶𝗼𝗻 𝗦𝗶𝗴𝗻𝗮𝗹𝘀 → If 3DS isn’t triggered when expected, or if device data looks unusual, issuers may suspect fraud. 𝗛𝗼𝘄 𝗖𝗮𝗻 𝗠𝗲𝗿𝗰𝗵𝗮𝗻𝘁𝘀 𝗥𝗲𝗱𝘂𝗰𝗲 𝗗𝗲𝗰𝗹𝗶𝗻𝗲𝘀? ▪️𝗘𝗻𝗰𝗼𝘂𝗿𝗮𝗴𝗲 𝗣𝗿𝗲-𝗔𝘂𝘁𝗵 𝗔𝗹𝗲𝗿𝘁s → Customers can notify their issuer before making large purchases. ▪️𝗨𝘀𝗲 𝗜𝗻𝘁𝗲𝗹𝗹𝗶𝗴𝗲𝗻𝘁 𝟯𝗗𝗦 → While frictionless checkout is ideal, selectively triggering 3DS for high-value payments reassures issuers of legitimacy. ▪️𝗢𝗽𝘁𝗶𝗺𝗶𝘇𝗲 𝗥𝗶𝘀𝗸-𝗕𝗮𝘀𝗲𝗱 𝗔𝘂𝘁𝗵𝗲𝗻𝘁𝗶𝗰𝗮𝘁𝗶𝗼𝗻 → Merchants using machine learning fraud tools should fine-tune risk scoring to avoid unnecessary declines. ▪️𝗘𝗻𝗮𝗯𝗹𝗲 𝗠𝘂𝗹𝘁𝗶𝗽𝗹𝗲 𝗣𝗮𝘆𝗺𝗲𝗻𝘁 𝗢𝗽𝘁𝗶𝗼𝗻𝘀 → Giving customers the option to split payments (e.g., credit + BNPL) or pay via A2A can help. ▪️𝗟𝗲𝘃𝗲𝗿𝗮𝗴𝗲 𝗜𝘀𝘀𝘂𝗲𝗿 𝗖𝗼𝗹𝗹𝗮𝗯𝗼𝗿𝗮𝘁𝗶𝗼𝗻 → Merchants can work with issuers to optimize approval rates by sharing fraud signals and adopting network intelligence tools like Visa Trusted Listing or Mastercard Smart Authentication. 𝗙𝗶𝗻𝗮𝗹 𝗧𝗵𝗼𝘂𝗴𝗵𝘁𝘀 High-value transactions will always pose some level of risk, but merchants can take proactive steps to reduce legitimate payment declines and ensure strong approval rates without increasing fraud exposure. Source: Kount, an Equifax Company, SEON 🚨Follow Jason Heister for daily #Fintech and #Payments guides, technical breakdowns, and industry insights.

Understanding the methodology for customer risk profiling under the #AML framework. Is it sufficient to classify the customer as "high" or "low" risk merely based on their jurisdiction or person being a #PEP? The answer is NO! Customer Risk Assessment (#CRA) is an extensive process that assesses the ML/FT risk a customer poses. While evaluating this, a comprehensive view of all the parameters impacting the business relationship must be considered. This includes: ➡ Associated geographies (nationality, domicile, business operations) ➡ Outcome of screening (#Sanctions, PEP or presence of any #AdverseMedia) ➡ Nature of business activities ➡ Legal structure (complexity and transparency) ➡ Services or products involved ➡ Nature of the proposed transaction (frequency, value, consistency with customer's social/economic profile, etc.) ➡ Expected mode of payment ➡ Delivery channels (including involvement of third parties) ➡ Any other risk factors considering the nature and size of the business and the customer’s profile With a robust Customer Risk Assessment, strengthen your efforts around detecting and combatting financial crime. #AMLUAE #AntiMoneyLaundering #AntiFinancialCrime #CustomerRisk #RiskAssessment #CDD #SanctionsCompliance #EDD

Free Resource Friday! If 2024 has taught us anything, it’s that fraud remains one of the most critical compliance challenges we face today. From APP fraud to synthetic identity scams, money muling, and cross-border fraud, financial criminals are evolving faster than ever. For compliance professionals, fraud risk assessment is no longer optional—it’s a must-have for proactive risk mitigation, regulatory alignment, and reputational protection. 🔍 ACAMS has released a FREE best practice guide on Fraud Risk Assessment, offering: ✔️ A step-by-step methodology for conducting robust fraud risk assessments ✔️ Insights into emerging fraud threats and trends ✔️ A risk prioritization matrix to help organizations focus on high-impact risks ✔️ Strategies to break down silos and create a multi-disciplinary fraud risk approach ✔️ Real-world examples, frameworks, and a fraud risk register template 💡 Key takeaway? Fraud is not just an AML issue—it’s an enterprise-wide risk. Organizations that embed fraud risk assessments into their compliance framework will be better equipped to handle regulatory changes and reduce financial crime exposure. #FraudPrevention #Compliance #RiskAssessment #ACAMS #FinancialCrime