Payment Processing Basics

#Africa bleeds $5B a year not to #corruption or #mismanagement, but just to move money within its own borders. Example: A Kenyan business paying a Ugandan supplier. Instead of Nairobi → Kampala, money goes: Nairobi → USD conversion (1–2%). USD routed via New York/London ($20–50 fee). USD → Ugandan shillings (another 1–2%). By the time a $26,000 invoice is paid, $500–1,000 is gone. Whilst we may be denied visas, our money travels freely through New York. And it’s not just trade: Africa’s #diaspora sends $95B home each year, yet pays the world’s highest remittance costs. -We pay the highest cost for credit. -We pay the highest cost for payments. -We pay the highest cost to send our own money home. It’s not inefficiency. It’s design. The #GlobalFinancialSystem wasn’t built for us. The good news? Solutions exist. #PAPSS (Pan-African Payment and Settlement System) is already live linking 15 central banks, 150 commercial banks, and 14 payment switches, with the capacity to handle $300B in intra-African trade annually. Through PAPSS, that same Kenya–Uganda  transaction could  look very different: -One direct conversion from KES → UGX (0.2–0.5% spread). -Settlement netted via African central banks. -Funds received in hours, not days. Estimated cost: $60–150.  Potential savings: $500–950 on a single $26,000 payment. No detours. Value stays in Africa. The challenge isn’t invention. It’s implementation. One Africa. One market. One #payment system. AI image below*

MoneyGram now makes 30B+ fraud decisions per year with AI. Every single one happens in real time, not the next morning. Here's how AI is revolutionizing global payments: MoneyGram moves money across 200+ countries for 50 million people a year. With nearly 500,000 retail locations and 5 billion digital endpoints, every transaction needs instant fraud detection, AML screening, and compliance checks. But most legacy systems still rely on overnight batch processing. The deeper issue: most financial institutions use separate vendors for fraud, AML, and onboarding. That fragmentation creates blind spots where multi-vector attacks slip through. Oscilar solves this with a unified platform for fraud detection, underwriting, onboarding risk, and AML compliance. • Real-time signal correlation • Cognitive Identity Intelligence across thousands of behavioral and device markers • AI agents that auto-update thresholds and rules • No-code deployment with A/B testing and shadow mode The scale is already proven: 1,000+ requests per second and deployment in 6–12 weeks instead of 18+ months. Customers are seeing real results: • TransPecos Bank: 41% fraud reduction and 90% fewer false positives • Flexcar: 100% elimination of asset losses As they expand into digital assets, they need infrastructure that handles both fiat and crypto risk - Oscilar's unified view bridges on-chain intelligence with traditional behavioral and identity data. As Oscilar's CEO puts it: "MoneyGram is leading the way in modernizing global payments by embracing an AI-first approach to risk." "Global financial institutions need agile, AI-native platforms to stay ahead of increasingly sophisticated threats." Newsletter: https://lex.substack.com/ More on Oscilar: https://oscilar.com/

🚀 Real-World Fraud Detection with Apache Kafka, KSQL, and Flink Fraud is evolving—so must fraud detection. In a digital-first world, industries from banking and fintech to gaming and mobility face increasingly sophisticated threats. The key to staying ahead? Real-time fraud prevention powered by Apache Kafka, KSQL, and Apache Flink. 🔹 Why Real-Time Streaming for Fraud Detection? Traditional fraud detection often works after the fact—but modern fraudsters operate in seconds. Streaming data processing enables companies to detect anomalies, correlate events, and block fraud before it happens. 🔹 Case Studies: How Industry Leaders Prevent Fraud in Real Time 🏦 PayPal & CapitalOne – Detecting fraudulent transactions across millions of users 🏦 ING Bank – Preventing identity theft with streaming analytics 🚗 Grab – Stopping ride-hailing fraud in Southeast Asia 🎮 KakaoGames – Preventing gaming fraud and cheating 🔹 The Technology Stack ✅ Kafka Streams & KSQL – Streaming queries and real-time analytics ✅ Apache Flink – Stateful stream processing for advanced fraud detection ✅ Event-Driven Architectures – Enabling scalable, low-latency decision-making 📖 Read the full blog and see how leading companies outsmart fraud in real time: 🔗 https://lnkd.in/eftjdUFB #FraudDetection #ApacheKafka #RealTimeData #StreamingAnalytics #Flink #KSQL #MachineLearning #Cybersecurity #DataStreaming

Subscription fraud is often invisible - but its impact is significant. Fake free trials and recurring payment abuse rarely appear fraudulent at the start. They typically mimic legitimate user behavior, making detection challenging. Common fraud patterns in subscription businesses • Multiple accounts created by the same user • Use of temporary emails and shared or stolen cards • Abnormal usage during trial periods • Intentional chargebacks after extensive consumption Business impact • Revenue leakage • Increased chargeback ratios • Payment gateway penalties • Distorted growth and retention metrics • Higher customer acquisition costs How fraud is detected effectively • Device and IP intelligence • Behavioral signal analysis • Payment reuse and failure patterns • Usage anomalies during trials and renewals Prevention strategies that scale • Limit free trials per device and payment method • Apply step-up verification for high-risk users • Monitor usage prior to renewals • Block bots and high-risk IP ranges • Leverage AI models to identify evolving fraud patterns Outcomes of a strong fraud strategy • Reduced fake users • Lower chargebacks • Accurate business metrics • Protected recurring revenue • Improved trust with genuine customers Fraud prevention is not friction. It is a safeguard for legitimate users and sustainable growth.

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

How to offer the right mix of payment options without overwhelming their customers? 🛒💡 Merchants constantly seek ways to enhance customer experience, with payment options at the forefront of this endeavor. As the former Head of Payments for a $4Bn company, I've observed firsthand the delicate balance required in optimizing payment methods. An eye-opening statistic from our analytics revealed that despite a 99% acceptance rate of a French wallet option, 98% of users initially selected it would switch to an alternative payment method. This underscores a broader trend: the paradox of choice in e-commerce. 🔄🔍 This phenomenon teaches us that diversity in payment options can be seen as an advantage, but it can also deter customers if not implemented thoughtfully. The challenge isn't just to offer a wide array of payment methods but to curate them in a way that resonates with your audience's needs and preferences. 🎯 ✨ Here's the takeaway: The key to enhancing customer satisfaction and boosting conversion rates is simplifying the checkout process. It's not about having the most payment options but the most effective ones. It requires understanding your customer base deeply and making data-driven decisions to streamline their payment experience. 📊💳 How do you strike the perfect balance between offering choice and maintaining simplicity in your payment options? Have you noticed a difference in customer behavior or sales conversions based on the payment methods you offer? #DigitalPayments #Ecommerce #UserExperience #CheckoutSimplicity #CustomerEngagement #BusinessStrategy 👇 Let's discuss below. Your experiences and strategies could provide invaluable guidance to others navigating the complex landscape of online payments.

If you think Stripe Radar is enough, you're covering maybe a third of the fraud vectors that matter. Modern fraud isn't just stolen cards. It's account takeovers, synthetic identities, bot attacks, friendly fraud, and money laundering each requiring its own defense layer. That's why fraud prevention has split into a full stack of specialized tools. Card fraud is still massive, but its share of total losses keeps shrinking. In many verticals, transactional fraud is no longer the biggest threat. Sift is a good illustration. Often seen as a generic fraud tool, it processes signals far beyond payments: ~70% of its detections relate to non-payment events (logins, signups, content abuse) ~1 trillion events analyzed per year ~34,000 sites and apps protected globally And here's the shift almost nobody talks about: the card networks and bureaus are quietly buying up the entire stack. Visa now owns Featurespace and Verifi. Mastercard owns Ethoca and NuData. Equifax owns Kount and Midigator. LexisNexis owns ThreatMetrix. Entrust absorbed Onfido. "Beyond Stripe Radar" increasingly means "beyond a handful of giants." The full stack today: - End-to-End Fraud Platforms: Sift, Forter, Riskified, Signifyd, Sardine, SEON, ClearSale, NoFraud, Ravelin Technology - Device Intelligence & Behavioral Biometrics: Fingerprint, Incognia, BioCatch, ThreatMetrix, Castle, SHIELD, Callsign, NuData Security, a Mastercard company, Darwinium, Trustfull - Identity Verification & KYC: Persona, Alloy, Sumsub, Socure, Onfido, Veriff, Jumio Corporation, Incode, iProov, Trulioo, Mitek Systems, IDnow, GBG - AML & Transaction Monitoring: ComplyAdvantage, Hawk AI, Unit21, Feedzai, NICE Actimize, Quantexa, SAS, FICO, Nasdaq Verafin, Chainalysis, ACI Worldwide, DataVisor - Bot Protection & Account Takeover: Arkose Labs, HUMAN, DataDome, Cloudflare, Kasada, Imperva, Akamai, Netacea, Trusona - Chargeback & Dispute Management: justt, Chargeflow, Ethoca, Verifi Inc., Kount, Midigator, Chargebacks911 Attacker behavior explains the split. AI-generated synthetic identities, credential stuffing at scale, and organized fraud rings have made single-layer defenses obsolete. A rough 2026 picture of where losses sit: ~45% account takeovers and identity fraud ~30% transactional and card fraud ~25% chargebacks and friendly fraud Real-time decisioning, shared fraud networks, and AI-driven risk scoring keep accelerating the trend. Fraud prevention is no longer a feature. It's becoming critical infrastructure for every digital business. PS: I post about payments with Suby, stablecoins & the reality of building a payment startup, every week. Follow for more!

PhonePe proved AI’s value nationally, while the world debates whether AI will replace jobs. (Spoiler: This isn't a classic Indian startup success story) This is one of the most detailed public case studies of production-scale AI in India with quantified results, technical architecture details, and strategic insights relevant to anyone building or selling AI systems. In May 2025, the Department of Telecommunications, India launched the Financial Risk Indicator (FRI) — an AI-powered fraud detection network built to flag suspicious activity across India’s payment ecosystem. PhonePe was the first to integrate it. Results so far 👇 • 48 lakh suspicious transactions blocked • ₹125 crore in potential fraud losses averted (by PhonePe alone) • 40% drop in fraud complaints • 1% false positive rate — remarkably low for systems at this scale But the real story isn’t in the numbers. It’s in how they pulled it off. Instead of building flashy AI features users could see, They built AI infrastructure users never notice. Their Edge Framework runs machine learning models directly on your phone, no cloud dependency, no data exposure. Every decision happens in milliseconds, privately and silently. Underneath it all sits Guardrails, their real-time fraud detection engine. It is a four-layer AI architecture that combines: 1️⃣ Connected Intelligence → Maps relationships between users, devices, and merchants to detect coordinated fraud rings. 2️⃣ Action Intelligence → Monitors behavior patterns and usage frequency to catch anomalies before they escalate. 3️⃣ Profile Intelligence → Scores sender, receiver, and payment instruments in real time for dynamic risk profiling. 4️⃣ Behavioral Biometrics → Flags subtle deviations — typing rhythm, device grip, location shifts — that reveal account takeovers. Every layer works in milliseconds across 31+ crore daily transactions, adapting continuously to new attack patterns. That’s not just AI at work, that’s AI as infrastructure. ---------------------------------------------- 💡 Takeaways for builders and leaders: → The most powerful systems don’t need an interface; they need outcomes. → Real-time AI isn’t optional. In payments, logistics, and cybersecurity, milliseconds can mean millions. → Edge AI = Trust. On-device inference isn’t a gimmick; it’s the future of privacy-first intelligence. → PhonePe’s FRI partnership shows how collaboration can harden entire ecosystems, not just companies. Do you think the future of AI lies in what users see, or in what they never notice? Drop your thoughts below 👇 Government of India (GoI) Rahul Chari

𝗖𝗵𝗮𝘁𝗚𝗣𝗧 𝗝𝘂𝘀𝘁 𝗠𝗮𝗱𝗲 𝗮 𝗣𝗮𝘆𝗺𝗲𝗻𝘁 AI isn’t just writing code or answering questions anymore, it’s starting to make payments Last week, the National Payments Corporation Of India (NPCI), in collaboration with Razorpay and OpenAI, announced a pilot that enables ChatGPT users to complete UPI payments directly within the chat interface No redirects. No checkout pages. Just: “Pay ₹500 to XYZ” and the transaction executes. All authenticated, processed, and confirmed in real time Let’s unpack what this means 👇 𝗪𝗵𝗮𝘁 𝗔𝗿𝗲 “𝗔𝗴𝗲𝗻𝘁𝗶𝗰 𝗣𝗮𝘆𝗺𝗲𝗻𝘁𝘀”? Agentic payments refer to AI-driven financial actions where an AI agent initiates, approves, or manages a transaction on behalf of the user Think: → Auto Paying recurring bills when balance thresholds are met → Detecting better exchange rates and executing payments → Confirming purchases conversationally (“Yes, pay the invoice”) This isn't just automation, it’s delegation of intent to an intelligent agent 𝗪𝗵𝘆 𝗧𝗵𝗶𝘀 𝗣𝗶𝗹𝗼𝘁 𝗠𝗮𝘁𝘁𝗲𝗿𝘀 India’s UPI provides the perfect testing ground. Open APIs, real-time rails, and a massive digital payments user base This pilot demonstrates what’s possible when AI interfaces (ChatGPT) meet instant payments infra ▪️Conversational checkout → Payments embedded directly into chat interfaces ▪️No merchant integration overhead → The AI agent routes and confirms via UPI’s existing rails ▪️Improved accessibility → Users can pay by voice, chat, or text, expanding digital payment reach ▪️Adaptive intent recognition → The AI understands context: “Pay my internet bill” or “Send ₹2,000 to Austin” 𝗪𝗵𝗮𝘁 𝗧𝗵𝗶𝘀 𝗠𝗲𝗮𝗻𝘀 𝗙𝗼𝗿 𝗠𝗲𝗿𝗰𝗵𝗮𝗻𝘁𝘀 𝗮𝗻𝗱 𝗙𝗶𝗻𝗧𝗲𝗰𝗵𝘀 If agentic payments scale, checkout experiences could shift dramatically: ▪️No visible checkout flow → Purchases happen within interfaces customers already use ▪️Authentication embedded at the device level → Biometrics + tokenized credentials replace 3DS pages ▪️Lower friction and abandonment → Especially for microtransactions and bill payments ▪️New security frameworks required → Merchants and issuers will need to define how AI intent = user consent FinTechs could find new roles in enabling “agent orchestration,” while merchants might shift resources from UX optimization to trust verification 𝗪𝗵𝗮𝘁 𝗛𝗮𝗽𝗽𝗲𝗻𝘀 𝗡𝗲𝘅𝘁? India is the first major market to pilot this model, but the implications are global. If it works we could see: → OpenAI partnerships with Visa or Mastercard via Pay-by-Link APIs → EU wallets integrating AI voice checkout within PSD3/SCA frameworks → U.S. banks experimenting with FedNow + agentic orchestration layers 𝗙𝗶𝗻𝗮𝗹 𝗧𝗵𝗼𝘂𝗴𝗵𝘁 We’re entering an era where intent replaces interaction. Agentic payments will redefine the boundaries of what “making a payment” even means Source: TimesofIndia, Moneycontrol 🔔 Follow Jason Heister for daily #Fintech and #Payments guides, technical breakdowns, and industry insights