Transactional Fraud Analysis

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

Key Findings from the 2025 State of #Fraud Report 🔸 Rising Fraud Incidents Across All Sectors: 60% of financial institutions and #fintechs reported an increase in fraud events targeting #consumer and business accounts in 2024. Fraud was predominantly digital, with 80% of events occurring on #online or #mobilebanking channels 🔸 Key Fraud Types: Credit card fraud, identity theft, and account takeover (ATO) #fraud were the most common types of fraud reported. 20% of enterprise #banks ranked check fraud as their most frequent fraud type. 🔸 Financial and Reputational Costs: 31% of organizations experienced fraud losses exceeding $1M in 2024. 73% ranked #reputational damage as the most severe consequence of fraud, followed closely by direct financial losses (72%) and loss of clients (72%). 🔸 Role of Organized Crime: 71% of fraud attempts were attributed to financial #criminals or fraud rings, marking a shift from first-party to third-party fraud. 🔸 Fraud #Detection and Prevention: 56% of financial organizations most commonly detected fraud at the transaction stage, while 33% identified it during onboarding. Real-time interdiction was conducted by only 47% of respondents, highlighting a gap in immediate fraud prevention. 🔸 Fraud Detection Trends: Inconsistent user #behavior (28%) and mismatched personal data (20%) were leading indicators of fraud attempts. Mid-market banks reported the highest incidence of fraud, with 56% facing over 1,000 fraud cases. 🔸 AI and Technology Adoption: 99% of organizations reported using AI in fraud prevention, with 93% agreeing that machine learning and #generativeAI will revolutionize detection capabilities. #AI was predominantly used for anomaly detection (59%) and explaining large datasets for #risk analysis (67%). 🔸 Fraud Prevention Investments: 93% of respondents indicated ongoing #investments in fraud prevention, with identity risk solutions being the most impactful (34%). Top technologies for 2025 include identity risk solutions (64%), document #verification software (49%), and voice/facial recognition systems (38%). 🔸 Regulatory Impact: 62% of organizations plan to increase fraud prevention investments in response to #regulatory scrutiny and potential #reimbursement requirements for fraud losses. Predictions for 2025: 🔆 Fraud will continue to rise, driven by increased availability of consumer data on the #darkweb 🔆 Financial institutions are expected to adopt #centralized platforms for fraud and identity risk management to enhance efficiency and reduce losses 🔆 Advanced AI tools and real-time #payments systems will remain key focus areas for fraud mitigation strategies. These findings emphasize the need for a multi-layered approach to fraud prevention, prioritizing identity verification, AI-driven analytics, and real-time interdiction

Financial Crime Detection in Banking: Key Focus Areas 1. Transaction Monitoring: Unusual Transaction Patterns: Identifying sudden large deposits, frequent high-value transactions, or rapid fund movements. Structuring (Smurfing): Detecting multiple smaller transactions made to avoid reporting thresholds. Cross-Border Transfers: Scrutinizing international fund transfers, especially to/from high-risk countries. Round-Tripping: Monitoring funds leaving and re-entering accounts, often disguised as legitimate transactions. 2. Customer Due Diligence (CDD) and KYC: Identity Verification: Authenticating documents like Aadhaar, PAN, and passports during onboarding. Source of Funds Verification: Ensuring declared income aligns with account activity. Continuous Monitoring: Regularly updating customer data and tracking changes in transaction behavior. High-Risk Customer Screening: Assigning risk scores and applying Enhanced Due Diligence (EDD) for high-risk customers, such as PEPs. 3. Anti-Money Laundering (AML): Suspicious Transaction Reports (STR): Flagging and reporting suspicious activities to regulatory authorities. Sanctions Screening: Checking customers and transactions against global watchlists and sanctions databases. Behavioral Analytics: Using machine learning to detect deviations from typical transaction patterns. 4. Fraud Detection Techniques: Account Takeover Prevention: Monitoring for unusual login attempts, location changes, or device usage. Synthetic Identity Detection: Identifying accounts opened with fake identities or stolen data. Insider Threat Detection: Tracking employee access to sensitive data and unusual actions within the banking system. 5. Money Mule Activity: Rapid Inflows and Outflows: Detecting quick fund transfers after receiving deposits. Third-Party Fund Movements: Monitoring accounts receiving funds from multiple, unrelated parties. Dormant Account Reactivation: Identifying sudden activity in long-inactive accounts. 6. Red Flags for Financial Crimes: Inconsistent Financial Behavior: Transactions that don’t align with a customer’s known profile or declared income. Frequent Changes in Personal Information: Multiple changes in contact details, addresses, or email IDs in short spans. Unusual Business Accounts: Personal accounts used for high-volume business-like transactions. 7. Politically Exposed Persons (PEPs): Adverse Media Checks: Regular screening of news and legal databases for negative mentions. Large Transaction Scrutiny: Enhanced monitoring of high-value transactions linked to PEPs. 8. Technology and Analytics: Machine Learning Models: Identifying hidden patterns through anomaly detection and predictive analytics. Network Link Analysis: Mapping connections between suspicious accounts to uncover broader criminal networks. Real-Time Alerts: Generating instant alerts for potentially fraudulent activity

Welcome to 𝐓𝐡𝐞 𝐏𝐚𝐲𝐦𝐞𝐧𝐭𝐬 𝐀𝐜𝐚𝐝𝐞𝐦𝐲 by Checkout.com — Episode 6 👋 𝐓𝐡𝐞 𝐓𝐲𝐩𝐞𝐬 𝐨𝐟 𝐅𝐫𝐚𝐮𝐝 𝐢𝐧 𝐏𝐚𝐲𝐦𝐞𝐧𝐭𝐬: ► Fraud in payments is a growing challenge for merchants, issuers, and payment processors. Fraudulent transactions not only cause financial losses but also damage a merchant’s reputation ► To combat fraud effectively, businesses must leverage fraud detection tools, authentication techniques, and dispute management strategies to stay ahead of bad actors while maintaining a seamless customer experience — 𝐓𝐡𝐞 𝐓𝐲𝐩𝐞𝐬 𝐨𝐟 𝐅𝐫𝐚𝐮𝐝 & 𝐄𝐱𝐚𝐦𝐩𝐥𝐞𝐬 ► 3-𝐏𝐚𝐫𝐭𝐲 𝐅𝐫𝐚𝐮𝐝 – This occurs when a fraudster uses stolen card details to make purchases. ► 𝐅𝐫𝐢𝐞𝐧𝐝𝐥𝐲 𝐅𝐫𝐚𝐮𝐝 – A cardholder disputes a legitimate transaction, either by mistake or to reverse a purchase. ► 𝐆𝐨𝐨𝐝 𝐅𝐚𝐢𝐭𝐡 𝐏𝐚𝐲𝐦𝐞𝐧𝐭 𝐃𝐢𝐬𝐩𝐮𝐭𝐞𝐬 – The customer disputes a payment due to issues with product quality or fulfillment. Fraud prevention strategies must be tailored to identify, assess, and respond to these types of fraud in real time. — 𝐓𝐡𝐞 𝐏𝐫𝐨𝐜𝐞𝐬𝐬: 𝐂𝐮𝐭𝐭𝐢𝐧𝐠 𝐃𝐨𝐰𝐧 𝐨𝐧 𝐂𝐚𝐫𝐝 𝐅𝐫𝐚𝐮𝐝 1️⃣ 𝐅𝐫𝐚𝐮𝐝 𝐃𝐞𝐭𝐞𝐜𝐭𝐢𝐨𝐧 𝐄𝐧𝐠𝐢𝐧𝐞𝐬 – These tools analyze transaction data (e.g., IP addresses, device data...) to assess fraud risks. 2️⃣ 3𝐃 𝐒𝐞𝐜𝐮𝐫𝐞 𝐀𝐮𝐭𝐡𝐞𝐧𝐭𝐢𝐜𝐚𝐭𝐢𝐨𝐧 – Adds an extra layer of protection by requiring customer verification for high-risk transactions. 3️⃣ 𝐌𝐚𝐜𝐡𝐢𝐧𝐞 𝐋𝐞𝐚𝐫𝐧𝐢𝐧𝐠 & 𝐀𝐈 – Predicts fraud patterns based on historical transactions and behavioral analytics. 4️⃣ 𝐓𝐨𝐤𝐞𝐧𝐢𝐳𝐚𝐭𝐢𝐨𝐧 – Converts sensitive payment data into tokens, reducing the risk of stolen card details being misused. 5️⃣ 𝐂𝐡𝐚𝐫𝐠𝐞𝐛𝐚𝐜𝐤 𝐏𝐫𝐞𝐯𝐞𝐧𝐭𝐢𝐨𝐧 – Strategies like real-time alerts and clear billing descriptors — 𝐓𝐡𝐞 𝐃𝐚𝐭𝐚: 𝐊𝐞𝐲 𝐃𝐚𝐭𝐚 𝐏𝐨𝐢𝐧𝐭𝐬 𝐭𝐨 𝐑𝐞𝐝𝐮𝐜𝐞 𝐅𝐫𝐚𝐮𝐝 Fraud detection relies on rich transaction data to identify suspicious activity and block fraudulent payments: ► Customer Name – Verifies the cardholder’s identity and checks for patterns of fraudulent behavior (e.g., fake names...). ► IP Address – Flags transactions from high-risk regions or locations inconsistent with the customer’s normal behavior. ► Billing Address – Used for Address Verification System (AVS) checks to confirm that the billing address matches the cardholder’s bank records. ► Delivery Address – Helps detect fraudulent transactions by assessing mismatched shipping details. ► Email Address – Identifies fraud patterns, such as disposable email addresses or emails associated with prior chargebacks. Providing complete and accurate data in payment requests enhances fraud detection and reduces false declines, improving both security and conversion rates. —— Source: Checkout.com x Connecting the dots in payments... ► Sign up to 𝐓𝐡𝐞 𝐏𝐚𝐲𝐦𝐞𝐧𝐭𝐬 𝐁𝐫𝐞𝐰𝐬 : https://lnkd.in/g5cDhnjC ► Connecting the dots in payments... and Marcel van Oost

Transaction Monitoring process in a bank. Transaction monitoring in a bank is a crucial process that involves the continuous review and analysis of customer transactions to detect and prevent financial crimes such as money laundering, terrorist financing, fraud, and other illicit activities. Banks and financial institutions are required by law to implement robust transaction monitoring systems as part of their anti-money laundering (AML) and know your customer (KYC) compliance efforts. These systems help identify unusual or suspicious patterns of activity that might indicate illicit behavior. Here's an overview of how transaction monitoring works in a bank: Data Collection and Storage: Banks collect a vast amount of transactional data from various sources, including customer accounts, wire transfers, electronic funds transfers, cash deposits, withdrawals, and more. This data is stored securely in databases for further analysis. Rule-Based Monitoring: Transaction monitoring systems use predefined rules and scenarios to flag potentially suspicious transactions. These rules are often based on regulatory requirements and internal policies. For example, a rule might trigger an alert if a customer suddenly makes multiple large transactions that deviate from their normal behavior. Threshold Monitoring: Banks set certain thresholds for different types of transactions. If a transaction exceeds a specific threshold, it may trigger an alert. For instance, a large cash deposit by an individual who typically deals in smaller amounts might raise suspicion. Behavioral Analysis: Advanced transaction monitoring systems utilize behavioral analytics to establish a baseline for each customer's transaction patterns. Deviations from this baseline can indicate suspicious activity. Anomaly Detection: Transaction monitoring systems use machine learning and artificial intelligence algorithms to identify unusual patterns that might not be captured by traditional rule-based methods. These algorithms can adapt to evolving tactics used by criminals. Alert Generation: When a transaction meets the criteria of a predefined rule or exhibits suspicious behavior, the system generates an alert. Investigation and Reporting: The compliance team investigates flagged transactions to determine if they are indeed suspicious or if there's a legitimate explanation. They may analyze additional customer information, transaction history, and other relevant data. If warranted, a suspicious activity report (SAR) may be filed with relevant regulatory authorities. Feedback Loop: The investigation process feeds back into the transaction monitoring system. Regulatory Compliance: Banks are subject to various AML and KYC regulations, which include regular audits of their transaction monitoring processes to ensure that they are effectively detecting and preventing financial crimes.

#Transaction monitoring #TM #AML #KYC 1. What is transaction monitoring, and why is it important? Answer: Transaction monitoring is the process of reviewing financial transactions to detect suspicious or unusual activity, such as money laundering, fraud, or other financial crimes. It is crucial because it helps financial institutions comply with regulatory requirements, mitigate financial and reputational risks, and assist law enforcement in identifying and preventing illicit activities. 2. How would you identify suspicious activity in a customer’s transaction history? Answer: To identify suspicious activity, I would look for several key indicators, including: •Large transactions that deviate from the customer’s normal patterns. •Multiple transactions just below reporting thresholds (also known as “structuring”). •Transactions involving high-risk countries or entities. •Sudden changes in account activity without a plausible explanation. I would carefully analyze these patterns, compare them with the customer’s KYC (Know Your Customer) information, and escalate the activity for further investigation if necessary. 3. What steps would you take if you discovered a customer conducting multiple small transactions below the reporting threshold? Answer: This behavior, known as “structuring” or “smurfing,”is often used to evade detection. If I identified this activity, I would: 1.Review the customer’s transaction history for patterns or anomalies. 2.Verify the customer’s KYC profile to understand their typical transaction behavior. 3.Document my findings and escalate the case to the compliance team or file a Suspicious Activity Report (SAR) if the activity raises concerns. 4. Can you explain the difference between false positives and true positives in transaction monitoring? Answer: A false positive occurs when the monitoring system generates an alert for activity that, upon investigation, is found to be normal or legitimate. For example, a large transaction that aligns with the customer’s established behavior could trigger an alert, but upon review, it may not be suspicious. A true positive, on the other hand, is an alert that, after investigation, indicates genuine suspicious activity, such as money laundering or fraud. The goal is to refine the monitoring system to minimize false positives while ensuring true positives are identified and properly addressed. 5. Describe a time when you had to escalate an issue. What steps did you take? Answer: In my previous role, I noticed a customer making frequent high-value transactions to and from a high-risk country. After reviewing their transaction history, I observed no valid explanation for the volume or nature of these transactions. I then checked their KYC documentation and found no consistent justification for the behavior. I escalated the case to the compliance team with a detailed report on the transaction patterns and risks. They filed a Suspicious Activity Report.

Must read this weekend: 🚨 The 2024 edition of the UK Finance Annual Fraud Report paints a stark picture: over £1.17 billion lost to fraud in 2023 alone. While slightly down from 2022, the scale, complexity, and evolution of fraud tactics are intensifying—and the financial crime compliance world must adapt swiftly. 💳 Authorised Push Payment (APP) Fraud Surges APP fraud accounted for £459.7 million, with a worrying rise in romance scams (up 17%) and investment fraud (up 6%). Notably, 77% of APP cases originated on social media or messaging platforms—underscoring the role of Big Tech in enabling financial crime and the regulatory gap in tech-finance convergence. For #AMLCompliance officers, this signals a need to revisit typologies and customer behaviour monitoring. 💻 Online and Digital Threat Vectors Dominate • Online fraud: 76% of all reported cases • Mobile app-based scams: 20% of total APP cases • Remote access and impersonation scams remain prevalent 🏦 Institutional Response: Reimbursements and Regulation UK banks reimbursed 62% of APP losses voluntarily, amounting to £287 million. However, the introduction of the PSR mandatory reimbursement requirement (effective 7 October 2024) will shift the burden significantly. This makes fraud risk management not just an operational issue, but a financial liability for banks and payment service providers. 🔍 Emerging Typologies & Red Flags • Deepfake impersonation cases have appeared for the first time in UK Finance’s tracking • Fraudsters are increasingly leveraging AI-driven phishing and spoofing tools • Fraud networks are fragmenting across platforms—e.g., splitting scams across email, social, and crypto rails This aligns with global trends flagged in Europol and FATF typology reports, and should be directly reflected in transaction monitoring system adjustments, staff training, and third-party tech risk assessments. 🧠 What Should Financial Crime Teams Do? • Tighten controls on APP risk scoring, social media transaction narratives, and high-risk beneficiary profiling • Integrate new data sources, such as device telemetry and behavioural biometrics • Collaborate with tech platforms under evolving UK Online Safety and Economic Crime frameworks • Conduct internal simulations of fraud reimbursement impact under PSR rules • Update governance: fraud risk is now a board-level concern, not a back-office issue #FinancialCrime #FraudPrevention #AML #Compliance #APPFraud #RiskManagement #regulatory

5 Benefits of Fighting Fraud with Unit21: ✅ Comprehensive Fraud Detection and Prevention: Unit21 offers a single platform for transaction monitoring, real-time evaluation, and case management. It integrates diverse data sources for a 360-degree view of user activities and effectively combats fraud. ✅ Innovative Features for Enhanced Security: Key features include real-time monitoring to block suspicious transactions, machine learning to reduce false positives, automated alerts for fraud recovery, and #darkweb monitoring for proactive threat mitigation. ✅ Flexible and Configurable Rules: Unit21 supports scenario, dynamic, and real-time rules, allowing institutions to deploy pre-built or custom fraud detection strategies tailored to specific risks and operational needs. ✅ Advanced Analyst Tools: Analysts benefit from AI-driven investigation aids, network and transaction analyses, and past activity insights to streamline workflows and make informed decisions quickly. ✅ Fraud Consortium: By leveraging data from over 60 financial institutions and over 40 million entities, Unit21 enhances detection capabilities and enables institutions to respond collectively to emerging fraud patterns. Read more 📚 and watch the demo 👀 in my recent blog post!