Payment Fraud Prevention

Financial crime compliance (FCC) remains a critical priority for financial institutions, requiring robust controls, governance, and regulatory alignment. The Financial Crime Guide (FCG) 2025, published by the UK Financial Conduct Authority (FCA), offers a comprehensive framework for firms to strengthen their financial crime risk management, covering money laundering, fraud, bribery, sanctions, insider trading, and market manipulation. Key Takeaways ✅ Governance and Senior Management Responsibility • Firms must establish a clear governance structure where senior management actively oversees financial crime risks. • Boards and risk committees should regularly review financial crime reports and escalate key concerns. • Financial crime risk must be integrated into corporate risk management, with dedicated MLROs ensuring compliance. ✅ Risk-Based Approach & Compliance Framework • Firms must continuously assess their exposure to financial crime risks across products, services, customers, and jurisdictions. • A proactive risk assessment model should be in place, using data-driven insights and regulatory intelligence. • EDD is required for high-risk entities, such as PEPs and businesses in high-risk sectors. ✅ Money Laundering & Terrorist Financing Controls • Real-time transaction monitoring must detect unusual patterns, particularly in cross-border payments and digital assets. • Strong KYC and CDD processes are required to UBO. • Firms should leverage AI-driven AML analytics to track complex laundering networks and illicit flows. ✅ Fraud Prevention & Data Security • Firms must strengthen internal controls to detect fraudulent transactions and mitigate risks from synthetic identity fraud and cybercrime. • Cybersecurity measures should align with the NCSC, GDPR, and UK ICO guidelines to prevent data breaches and financial fraud. • A zero-trust security model is encouraged, with continuous monitoring of internal and external fraud risks. ✅ Sanctions, Asset Freezes & Proliferation Financing • With evolving geopolitical risks, financial institutions must align their sanction screening tools with FATF, OFSI, and EU sanction lists. • Compliance teams must detect and prevent trade-based money laundering (TBML) and ensure crypto asset compliance against sanctions circumvention tactics. • Third-country correspondent banking relationships must undergo stringent due diligence and ongoing risk monitoring. Strategic Actions for Compliance Leaders 🔹 Automate financial crime controls—AI-driven compliance tools improve fraud detection, sanctions screening, and transaction monitoring. 🔹 Enhance regulatory engagement—proactive collaboration with FCA, FATF, and JMLSG ensures alignment with evolving compliance expectations. 🔹 Integrate cybersecurity and financial crime risk strategies—given the rise in cyber-enabled financial crime, firms must merge cyber risk governance with FCC protocols. #FinancialCrime #Compliance #AML #Sanctions #CyberRisk

Ignoring cybersecurity just cost a major bank $250M in a single breach. Here's the harsh reality about cyber risk in finance: Implement continuous monitoring systems that detect suspicious activities in real-time, flagging unusual transactions and access patterns before they escalate into major security incidents. Deploy multi-layered authentication protocols across all financial systems, combining biometrics, hardware tokens, and behavioral analytics to create an impenetrable defense against unauthorized access. Establish automated backup systems that maintain encrypted copies of critical financial data, ensuring business continuity even if primary systems are compromised by ransomware or malicious attacks. Create dedicated incident response teams trained specifically for financial cyber threats, capable of containing breaches within minutes instead of hours and minimizing potential losses. Integrate AI-powered threat intelligence tools that predict and prevent emerging cyber threats, analyzing global attack patterns to strengthen financial security measures before vulnerabilities are exposed. Protection isn't expensive. Recovery is.

Fraud wasn’t supposed to be a core product challenge. But for most businesses operating online today, it has staunchly become one. In 2024, Indian businesses lost ₹22,842 crore to cybercrime. That’s a 206% increase over the previous year. The first few months of 2025 have already added another ₹7,000 crore in losses. This isn't just a compliance or security concern anymore. It shows up as frozen accounts, locked working capital, rising chargebacks, and misuse through stolen cards, fake UPI payments, and promo abuse. What surprised us most was how quickly chargebacks became part of the everyday reality for merchants: 1. More than half involve deliberate abuse 2. Smaller businesses aren’t spared - around 30 percent of Indian SMEs now report direct losses from fraud, with revenue hits of up to 5 percent. The nature of fraud has changed. Attacks are faster, more coordinated, and more sophisticated. The usual playbook of reacting after the damage doesn't hold up anymore. We decided to rebuild our approach from first principles. RiskShield is what came out of it. It’s a fraud detection engine that runs within the payment flow. It scores every transaction in real time using machine learning, detects fraud rings using graph intelligence, syncs with government risk data like I4C, DoT blacklist, NCRB, and blocks bad actors mid-transaction. It also flags early signs of promo abuse, card testing, and UPI manipulation. So far, RiskShield has helped block over ₹1,700 crore in fraud attempts. It has flagged 2 crore high-risk signals and protected more than 6,600 merchants. The system operates quietly in the background, with an F1 score of 87 percent which is a measure that balances precision (how often fraud alerts are correct) and recall (how much fraud we actually catch) and recall close to 95 percent. Most issues are prevented before anyone files a complaint. There’s still more work to do, but one thing is clear to us now: Fraud cannot be treated as an after-effect. It has to be designed against from the beginning. PS. Here's the flow we have built ⬇️

🌐 Yesterday, the Financial Action Task Force (FATF) released a new report on the growing threat of cyber-enabled fraud The paper, “Cyber-Enabled Fraud – Digitalisation and Money Laundering, Terrorist Financing and Proliferation Financing Risks”, delivers a clear message: online fraud has become one of the most widespread and profitable forms of crime worldwide. Key figures highlight the scale of the problem: ▪️90% of jurisdictions identify fraud as a major #moneylaundering risk ▪️In the United Kingdom, #fraud accounts for over 40% of all crimes ▪️In several countries, up to 15% of adults have been victims of a cyber-enabled scam ▪️Global losses amount to tens of billions of dollars each year Why is the threat growing? Digitalisation has increased the speed, scale, and sophistication of #fraudschemes: ▪️highly convincing phishing and social engineering attacks ▪️the use of #AI, including #deepfakes and synthetic content ▪️messaging apps and social media as operational channels ▪️instant payments and the use of #cryptoassets ▪️networks of #moneymules and nominee accounts In many cases, money laundering mechanisms are now embedded from the very beginning of the fraud scheme. The #FATF response: key priorities The report outlines several critical actions for authorities and financial institutions: ▪️greater payment transparency to improve traceability of funds ▪️faster tools for asset freezing and recovery ▪️stronger regulation of #virtualassets ▪️enhanced transparency on #beneficialownership ▪️faster domestic and international cooperation ▪️broader use of advanced technologies (#machinelearning, #riskscoring, real-time detection) The key takeaway Cyber-enabled fraud is now a global, industrialised, and cross-border phenomenon. Addressing it requires an integrated approach that combines #AML capabilities, advanced #technology, public–private collaboration, and strong international coordination. The FATF has confirmed that tackling fraud will be one of its strategic priorities in the coming years.

A risk-based approach (RBA) in financial crime investigative reporting means prioritizing and tailoring investigative efforts based on the level of risk posed by an entity, transaction, or behavior. This helps ensure that resources are used efficiently and the highest risks are addressed first. Here’s how to apply an RBA in your financial crime investigations: ⸻ 1. Understand the Risk Factors Start by identifying key risk factors relevant to the case: • Customer risk: High-risk jurisdictions, PEPs, adverse media, source of wealth • Product/service risk: Complex or anonymous services (e.g., crypto, shell companies) • Geographic risk: Countries with high levels of corruption, sanctions, or terror financing • Channel risk: Non face-to-face onboarding, third-party payments • Transaction risk: Unusual size, frequency, or destination ⸻ 2. Prioritize Investigations Based on Risk • High-risk cases: Prioritize cases with potential regulatory or reputational fallout (e.g., sanctions breaches, PEP corruption cases, terrorism financing). • Medium/low-risk: Investigate based on patterns or thresholds, but possibly with fewer resources or less urgency. Example: A transaction from a sanctioned country to a shell company = High risk A retail customer sending a one-time large payment abroad = Medium risk ⸻ 3. Use Risk Scoring Tools (if available) Many banks use automated risk rating or scoring models. Use these as a starting point, but always apply judgment. • Don’t rely solely on automation. • Combine quantitative risk scores with qualitative red flags (e.g., client behavior, inconsistencies). ⸻ 4. Tailor Your Investigation Depth Use the risk level to decide how deep you go: • High risk: Deep source-of-funds checks, multi-jurisdictional tracing, external data (e.g., adverse media, leaks like Panama Papers). • Lower risk: Focus on transaction logic, brief documentation review, internal flags. ⸻ 5. Document Risk Justification Clearly • Explain why a case is considered high/medium/low risk. • Link your conclusion to the bank’s risk appetite and policy (e.g., “This exceeds the Group’s tolerance for shell company exposure in high-risk jurisdictions.”) ⸻ 6. Escalate Appropriately High-risk findings should go to: • Senior management • Compliance/Legal • Financial Intelligence Unit (FIU), for potential SAR/STR filing ⸻ 7. Continuous Feedback Loop • Track which risk types lead to confirmed cases or SARs. • Adjust your risk filters and triage logic accordingly. ⸻ Example Case: Scenario: A corporate customer sends frequent payments to a shell company in Cyprus. • Risk Factors: Offshore shell, high volume, no economic rationale, high-risk jurisdiction. • Action (RBA): Full KYC review, source-of-funds check, look for links to known tax evasion schemes, possibly escalate for SAR filing.

🛡️ Axis Bank's AI Fraud Detection System Delivers Double-Digit Results Axis Bank is demonstrating how AI-led intelligence is reshaping fraud prevention in Indian banking — replacing legacy rule-based systems with predictive, real-time detection architecture. 📊 Key Highlights: The Numbers • Retail customer frauds fell ~30% year-on-year last fiscal, with continued double-digit decline in both volume and value this fiscal • Digital frauds prevented through AI-led monitoring and risk-based controls saw a 4.5-fold increase in FY26 vs FY25 • Fraud incidents across retail mobile banking, internet banking, and shopping malls dropped ~40% year-on-year The Shift to AI • Axis Bank is actively replacing rule-based fraud detection systems with AI-based systems, improving its ability to anticipate and identify fraud ahead of time. • The bank's intelligence-led prevention architecture enables early detection of suspicious transactions through behavioural pattern analysis flagging real-time deviations Mule Account Hotspots • Fraudsters increasingly operate through organised networks using mule accounts as intermediary layers to obscure fund trails and move illicit funds across multiple accounts • Identified hotspots include border areas near Bangladesh in West Bengal, parts of Assam, Bihar, Jharkhand, Haryana, Rajasthan, Madhya Pradesh, and outskirts of Chennai • For these regions, the bank has introduced product-level controls, enhanced monitoring, and risk-based flagging for transactions originating from higher-risk areas 💡 Why This Matters: This is a strong real-world signal of how AI-driven fraud intelligence — not just compliance checkboxes — is becoming central to retail banking risk management in India, especially as mule account networks grow more sophisticated. As fraud patterns evolve with organised, geographically-distributed networks, how ready do you think the broader Indian banking sector is to match this level of AI-led detection? #FraudPrevention #AIinBanking #AxisBank #RiskManagement #BankingTechnology #DigitalBanking #FinancialCrimeIntelligence #BFSI #CyberSecurity #MuleAccounts #BankingSecurity #RiskGovernance #RetailBanking #BankingInnovation #FinTech #OperationalRisk #AIFraudDetection #BankingTrends #FinancialCrime #IndianBanking #DigitalFraud #BankingCompliance #RealTimeMonitoring #BankingAnalytics #PredictiveAnalytics #BankingRisk #SecureBanking #FinancialSecurity #BankingData #RiskIntelligence

How prepared are you to comply with the recent Master Direction from RBI? Key Points from RBI's Master Direction on Fraud Risk Management in NBFCs issued on 15.07.2024 1. Policy & Framework - Establish a Board-approved Fraud Risk Management Policy. - Constitute a Special Committee of the Board for Monitoring and Follow-up of Fraud Cases. - Ensure senior management is responsible for the implementation and periodic review of the policy. 2. Early Warning Signals (EWS) from a Fraud perspective: - Develop and integrate an EWS framework with core banking solutions. - Regularly review and update early warning indicators for fraud detection. 3. Monitoring Financial Transactions: - Vigilantly monitor credit facilities, loan accounts, and other financial transactions for any signs of fraudulent activity. - Employ external or internal audits to investigate suspected fraud. 4. Reporting Protocols: - Immediately report incidents of fraud to the appropriate Law Enforcement Agencies (LEAs). - Appoint nodal officers for fraud reporting and coordination with LEAs. - Submit Fraud Monitoring Returns (FMRs) to the RBI within 14 days of fraud classification. 5. Staff Accountability and Penal Measures: - Conduct timely examinations of staff accountability in fraud cases. - Implement penal measures to restrict future credit facilities for entities and individuals involved in fraud for a period of five years post-settlement. 6. Legal Audits and Auditor Roles: - Conduct periodic legal audits of title documents for all credit facilities of ₹1 crore and above. - Ensure auditors report potential fraudulent activities immediately and conduct thorough internal audits covering all aspects of fraud management. 7. Closure of Fraud Cases: - Close fraud cases reported to RBI once the necessary actions and legal processes are completed. - Maintain detailed records of all closed fraud cases for future audits. 8. Additional Instructions: - Report instances of theft, burglary, dacoity, and robbery to RBI within seven days of occurrence. - Submit quarterly returns on such incidents through the prescribed online portal. At EY Forensics, we are supporting Banks and NBFC client is complying with these directives and help build a fraud risk management framework.

80% of Financial Frauds Are Now Digital—Are We Prepared? The number of digital financial frauds skyrocketed in FY24, growing more than four times year-on-year. The message is clear: the battlefield of financial fraud has gone digital, and so must our defences. Relying on single-layered security measures is like locking your front door but leaving your windows wide open. Fraudsters are becoming more sophisticated, leveraging phishing, malware, and identity theft to exploit vulnerabilities across the digital ecosystem. Solution? 𝐑𝐨𝐛𝐮𝐬𝐭 𝐦𝐞𝐚𝐬𝐮𝐫𝐞𝐬 𝐭𝐡𝐚𝐭 𝐰𝐚𝐭𝐜𝐡, 𝐥𝐞𝐚𝐫𝐧, 𝐚𝐧𝐝 𝐚𝐜𝐭 𝐢𝐧 𝐫𝐞𝐚𝐥-𝐭𝐢𝐦𝐞. Here’s what a multi-layered framework looks like in action: ✅ 𝐁𝐞𝐡𝐚𝐯𝐢𝐨𝐫𝐚𝐥 𝐀𝐧𝐚𝐥𝐲𝐭𝐢𝐜𝐬: AI monitors real-time user behaviour—location changes, sudden high-value transactions—and triggers step-up authentication if something feels off. ✅ 𝐁𝐢𝐨𝐦𝐞𝐭𝐫𝐢𝐜 𝐀𝐮𝐭𝐡𝐞𝐧𝐭𝐢𝐜𝐚𝐭𝐢𝐨𝐧: Fingerprints and facial recognition provide nearly impossible-to-spoof ID checks, shutting down common phishing and credential attacks. ✅ 𝐃𝐲𝐧𝐚𝐦𝐢𝐜 𝐑𝐢𝐬𝐤 𝐒𝐜𝐨𝐫𝐢𝐧𝐠: Every transaction gets a risk profile. Unusual device types, odd transaction sizes, and abnormal frequencies get flagged, prompting further checks. ✅ 𝐄𝐧𝐝-𝐭𝐨-𝐄𝐧𝐝 𝐄𝐧𝐜𝐫𝐲𝐩𝐭𝐢𝐨𝐧: Even if criminals intercept data in transit, encryption ensures it’s just scrambled noise, not usable information. ✅ 𝐒𝐞𝐜𝐮𝐫𝐞 𝐀𝐏𝐈𝐬: As businesses integrate with partners, secure APIs validate incoming requests and ward off unauthorized intrusions at the integration points. 𝘙𝘦𝘮𝘦𝘮𝘣𝘦𝘳: Digital fraud isn’t going away—it’s evolving. The only way to stay ahead is to think like a fraudster while building like a strategist. How do you safeguard your digital financial operations? Share your approach in the comments below. #DigitalFraud #FinancialFraud #Cybersecurity

⚡ 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.

One message from UK authorities came through very clearly this month - many firms may now need to rethink their FinCrime strategy 🚨 The UK’s latest joint statement from the FCA, Bank of England and HMT (15 May) may be one of the clearest signals yet on how seriously authorities are now viewing frontier AI, cyber resilience and FinCrime risks. But perhaps even more striking were Nikhil Rathi’s warnings at the FCA’s FinCrime Conference the day before - that FinCrime is becoming increasingly tech-driven, interconnected and AI-enabled, making it an issue of both economic and national security.   Key highlights 🔎 → AI cyber risks are increasingly being linked directly to fraud, operational resilience, financial stability & market integrity → Regulators openly recognise that frontier AI can identify and exploit vulnerabilities at a scale and speed beyond human capability → Greater focus on third-party technology and supply chain vulnerabilities → Boards & senior management are expected to understand AI-enabled threats much more deeply → Firms are expected to strengthen not only prevention, but also detection, containment & recovery capabilities → Growing recognition that firms with weak cyber and governance controls may become increasingly exposed to financial crime threats   🚨 The direction of travel is becoming increasingly clear:   The future of FinCrime compliance will require much closer integration between AML, fraud, cyber, operational resilience and AI governance - with stronger expectations around intel-sharing, governance and real-time risk management.   Keen to hear your thoughts - particularly around the operational and governance challenges firms may now face as risks increasingly converge 👇 Stay tuned - don't forget to save this post!