Payment Fraud Risk Management

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 ⬇️

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

“Everybody is doing their best—but it’s not enough. We can—and must—do better.” 🛡️ In a talk I gave last year at Indian Institute of Technology, Bombay, I had shared some thoughts on the state of payment fraud in India—a challenge that’s growing in scale and complexity. The ideas still remain relevant as the problem gets more acute day by day. I am attaching a short clip below. Do give it a listen. (Full talk link in comments.) Despite strong intent across institutions, there’s a lack of coordinated effort in turning data into action. We’re dealing with fragmented information, unclear accountability, and underutilized regulatory tools. As I highlighted in the talk, fraud is no longer just a customer problem—it’s a systemic design issue. ⸻ 🇮🇳 Where we stand • RBI’s 2017 circular on customer liability is clear, but poorly understood and rarely enforced in spirit. • OTPs don’t travel straight from bank to user—they pass through SMS aggregators and telcos, both of which have been known points of failure. • Yet in most cases, the burden of proof wrongly shifts to the customer. The result? In 2023 alone: • ₹10,000 crore lost to digital fraud • 17 million mobile numbers disconnected due to misuse • ~7 lakh fraud complaints recorded ⸻ 🌍 Lessons from abroad In the talk, I outlined how countries like Singapore and the UK are tackling fraud more holistically: 🔹 Singapore assigns clear roles: • Banks must issue timely alerts • Telcos must filter out malicious messages • Consumers are responsible only when they ignore clear warnings 🔹 UK’s new APP fraud rules shift the frame: • Payment Service Providers (PSPs) must generate scam-specific alerts • Fraud data is published per PSP—creating reputational pressure • Reimbursement is required within 5 days (split between sender and receiver PSPs) • Liability is shared based on customer care, PSP actions, and transaction complexity ⸻ 🧩 What India needs To reduce fraud meaningfully, we need: ✅ Interoperable fraud intelligence—PSPs should be able to identify suspicious patterns across institutions ✅ Consumer-facing friction—contextual alerts at the right moment can make a huge difference ✅ Enforcement of existing protections—including shifting the burden of proof ✅ Data transparency—publish PSP-level fraud metrics to incentivize action ✅ Role clarity across banks, telcos, regulators, and tech providers ⸻ We have the technology. We have the intent. What’s missing is alignment and shared accountability. “Fraud follows the money. Responsibility must too.” 🔍 To all colleagues in banking, payments, telecom, and policy: let’s collaborate to close the gaps—and not the complaints. #DigitalPayments #PaymentFraud #RBI #Fintech #PublicPolicy #CyberSecurity #UPI #CustomerProtection #IndiaBanking #FinancialEcosystem

🔴🔵🟢 Understanding the Fraud Diamond and Its Importance in Fraud Risk Management 🔴🔵 📌 What is the Fraud Diamond? The Fraud Diamond is an enhanced framework that explains why fraud happens by adding a critical fourth element—Capability—to the classic Fraud Triangle. It emphasizes that fraud occurs only when someone has the Pressure, Opportunity, Rationalization and Capability to commit it. 📌 The Four Elements of the Fraud Diamond 🔴 Pressure — The driving force behind fraud, such as financial difficulties or unrealistic goals. 🟡 Opportunity — The circumstances or weaknesses in controls that make fraud possible. 🔵 Rationalization — How the fraudster justifies their dishonest actions. 🟢 Capability — The skills, authority, or position that enable the fraud to be executed. 📌 Why is the Fraud Diamond Important in Fraud Risk Management? ➡️ By considering all four elements, organizations gain a deeper understanding of fraud risk, enabling more effective prevention and detection. ➡️ The addition of Capability helps organizations: - Identify high-risk individuals with both motive and means. - Design targeted controls based on fraud risk profiles. - Strengthen fraud awareness and monitoring strategies. 📌 Applying the Fraud Diamond in Practice 🟠 Implement segregation of duties and strong controls to reduce opportunity. 🟣 Monitor employee behavior to detect signs of pressure. ⚫ Promote ethical culture to limit rationalization. 🔴 Evaluate role access and skill sets to manage capability risks. 🟢 Use data analytics and AI for continuous fraud detection. 📌 The Future of Fraud Risk Management with the Fraud Diamond 🟡 Organizations that adopt this model are more resilient to modern fraud challenges, especially with digital transformation. 🟠 Advanced analytics and behavioral insights improve detection of fraud capability indicators for proactive risk management. #FraudRiskManagement #FraudDiamond #OperationalRisk #RiskManagement #InternalControls #FraudPrevention

Can AI Outpace Fraudsters in Real-Time? A payment platform detects and blocks fraudulent transactions before they happen, all in milliseconds. Here’s how one fintech did it: AI analyzed user behavior to spot anything unusual. Machine learning models evolved daily, adapting to new fraud tactics. Risk scores in real-time flagged suspicious payments instantly. The result? Fraud cut by 60% without slowing down legitimate users. In a world of instant payments, AI is the secret weapon to stay secure. How are you protecting your platform?

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

PayPal launches Dynamic Scam Alerts for Friends & Family payments PayPal has introduced a new layer of protection for peer-to-peer transactions—Dynamic Scam Alerts, a real-time, AI-powered system that intervenes before funds are sent under the Friends & Family category. This is significant because Friends & Family payments, while convenient, are excluded from purchase protection. That makes them attractive targets for scams involving impersonation, fake listings, or coercion via social platforms. Dynamic Scam Alerts analyze each transaction in real time, scoring its fraud risk using behavioral signals, historical patterns, and metadata. Based on that risk score, users are presented with one of three outcomes: Low risk → Informational warning, minimal friction Medium risk → Stronger prompt, highlighting the option to cancel High risk → Transaction is blocked automatically, no override The system is built on adaptive models that continuously evolve—detecting new scam techniques by learning from live transaction data. This approach allows PayPal to move away from static rules and toward a contextual, decision-based framework. This marks a shift in how financial platforms handle consumer-grade fraud risk: - Risk detection is embedded directly into the user flow - Alert fatigue is minimized by tailoring the intervention - Response time is immediate, before funds move - Trust is reinforced through intelligent escalation As scams become more AI-driven, the industry is clearly moving toward upstream fraud prevention—where every transaction is assessed, and every warning is data-informed. This rollout sets a precedent for contextual, real-time protection in P2P payments. https://lnkd.in/eM-FRgTp Nicolas Pinto Sam Boboev Simon Taylor #Fintech #Payments #FraudPrevention #AI #RiskManagement #PayPal #CyberSecurity #P2P #MachineLearning #TransactionRisk #DigitalTrust

🇪🇺 Out just now, the European Banking Authority has just published a draft opinion on new types of payment fraud and their possible mitigates. This opinion is based on payment data from 2022 and is aimed to strengthen upcoming coming the PSD3 / PSR legislative framework. Page 5 outlines some of the emerging fraud trends and page 8 onwards highlights their proposal. There are some big ticket items here: 🔶 Reinforced security requirements for PSPs   🔶 A fraud risk management framework to be put in place by PSPs  🔶 Amended liability rules  🔶 Strengthen and harmonize the supervision of fraud management  🔶 Security requirements for a single EU-wide platform for information sharing #fincrime #fraud #payments