Payment Processing Basics

🚨 𝙅𝙐𝙎𝙏 𝙄𝙉: EPI is in talks with Spain’s Bizum and Portugal’s Sibs about a potential partnership, as Europe pushes to reduce its reliance on U.S. tech giants in the payments sector. A few weeks ago, news broke that the European Union wants to reduce its reliance on Visa, Mastercard, PayPal, and Alipay, according to European Central Bank President Christine Lagarde: https://lnkd.in/d36daRwY “Visa, MasterCard, PayPal and Alipay are all controlled by American or Chinese companies. We should make sure there is a European offer,” she said. In an effort to reduce Europe’s dependency on American companies in the payments space, European payment providers are exploring opportunities for collaboration. The European Payments Initiative (EPI)—a consortium of 16 financial institutions from four countries—is currently in talks with Bizum in Spain and Sibs in Portugal about a potential partnership, according to EPI CEO Martina Weimert. “Both sides are fundamentally interested, but the technical details are complex,” Weimert told Handelsblatt: https://bit.ly/44o7Lb0 EPI, which aims to create a unified European payment system, launched its own payment service called Wero last year. Initially, Wero enables phone-to-phone payments in Germany, Belgium, and France: https://bit.ly/3RO2uC8 Its backers include German savings banks, cooperative banks, and Deutsche Bank, along with institutions from Belgium, France, and the Netherlands. The move to seek cooperation marks a shift in EPI’s strategy. Previously, it had mainly focused on recruiting more banks across Europe to join its project. “The urgency for Europe to become more independent from U.S. firms in the payment space has increased due to the recent U.S. tariff disputes,” Weimert said. “We must leverage effective payment solutions within Europe—and partnerships should be part of that strategy.” Bizum, which supports real-time mobile payments in Spain and is already working with banks in Portugal and Italy to connect national systems, also expressed openness to working with EPI. “We are pleased to see that other European solutions like EPI are open to collaboration. This is the best way to create a pan-European instant payment solution that benefits both consumers and merchants,” the company said. Both Bizum and Wero are built on instant payments, meaning bank transfers completed within seconds. This and more FinTech news in my newsletter: https://lnkd.in/e-tzDwh7 Find this helpful? [ 𝗿𝗲𝗽𝗼𝘀𝘁 ] Anything to add about this subject? [𝗶𝗻𝘃𝗶𝘁𝗲𝗱 𝘁𝗼 𝗰𝗼𝗺𝗺𝗲𝗻𝘁] Nice story, Marcel. Next! [ 𝗹𝗶𝗸𝗲 ] 

#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*

TL;DR: We built a transformer-based payments foundation model. It works. For years, Stripe has been using machine learning models trained on discrete features (BIN, zip, payment method, etc.) to improve our products for users. And these feature-by-feature efforts have worked well: +15% conversion, -30% fraud. But these models have limitations. We have to select (and therefore constrain) the features considered by the model. And each model requires task-specific training: for authorization, for fraud, for disputes, and so on. Given the learning power of generalized transformer architectures, we wondered whether an LLM-style approach could work here. It wasn’t obvious that it would—payments is like language in some ways (structural patterns similar to syntax and semantics, temporally sequential) and extremely unlike language in others (fewer distinct ‘tokens’, contextual sparsity, fewer organizing principles akin to grammatical rules). So we built a payments foundation model—a self-supervised network that learns dense, general-purpose vectors for every transaction, much like a language model embeds words. Trained on tens of billions of transactions, it distills each charge’s key signals into a single, versatile embedding. You can think of the result as a vast distribution of payments in a high-dimensional vector space. The location of each embedding captures rich data, including how different elements relate to each other. Payments that share similarities naturally cluster together: transactions from the same card issuer are positioned closer together, those from the same bank even closer, and those sharing the same email address are nearly identical. These rich embeddings make it significantly easier to spot nuanced, adversarial patterns of transactions; and to build more accurate classifiers based on both the features of an individual payment and its relationship to other payments in the sequence. Take card-testing. Over the past couple of years traditional ML approaches (engineering new features, labeling emerging attack patterns, rapidly retraining our models) have reduced card testing for users on Stripe by 80%. But the most sophisticated card testers hide novel attack patterns in the volumes of the largest companies, so they’re hard to spot with these methods. We built a classifier that ingests sequences of embeddings from the foundation model, and predicts if the traffic slice is under an attack. And it does this all in real time so we can block attacks before they hit businesses. This approach improved our detection rate for card-testing attacks on large users from 59% to 97% overnight. Perhaps even more fundamentally, it suggests that payments have semantic meaning. Just like words in a sentence, transactions possess complex sequential dependencies and latent feature interactions that simply can’t be captured by manual feature engineering. Turns out attention was all payments needed!

Interesting comparison between Apple Pay and Google Pay security models. Apple Pay keeps the entire transaction process more local: The credit card info is stored directly in the Secure Element on the device. A Device Account Number (DAN) is created and used for transactions. Apple doesn’t store your card data on its servers the bank and ecommerce server only see the DAN. Google Pay uses a cloud-based model: Card info is stored on Google’s servers. Google generates a payment token when you make a transaction. This token is then passed to the e-commerce server and ultimately to the bank. Both systems are secure, but Apple’s on-device approach reduces server exposure, which can offer stronger privacy, especially in sensitive contexts. Google’s model allows more server-side flexibility and features like cross-device syncing.

Stripe 𝗯𝘂𝗶𝗹𝘁 𝘁𝗵𝗲 𝗳𝗶𝗿𝘀𝘁 𝘁𝗿𝗮𝗻𝘀𝗳𝗼𝗿𝗺𝗲𝗿 𝗺𝗼𝗱𝗲𝗹 𝘁𝗿𝗮𝗶𝗻𝗲𝗱 𝗼𝗻 𝗽𝗮𝘆𝗺𝗲𝗻𝘁 𝗱𝗮𝘁𝗮! Not for text and NOT for code, BUT for billions of payments. Think GPT, but instead of learning language, it learned the structure, behavior, and patterns behind every transaction: ⬇️ 𝗛𝗲𝗿𝗲 𝗶𝘀 𝘄𝗵𝗮𝘁 Stripe 𝗷𝘂𝘀𝘁 𝗱𝗶𝗱? For years, Stripe used traditional ML — separate models for fraud, disputes, and authorizations. Each one relied on handpicked features like BIN codes, ZIP codes, email addresses, and payment methods. That worked — but it was narrow, manually intensive, couldn’t scale and most importantly, it missed the bigger picture. So Stripe trained a transformer, just like GPT — but instead of learning language, it learned from billions of transactions. Each payment — from a coffee in Paris to a subscription in Tokyo — was turned into a dense vector: a numerical fingerprint capturing its behavior and context. 𝗧𝗵𝗲 𝗼𝘂𝘁𝗰𝗼𝗺𝗲? ➜ Transactions with similar behavior cluster naturally — by issuer, merchant, location, or risk ➜ Suspicious patterns emerge organically — without handcrafted rules ➜ Fraud becomes easier to detect — not because it was labeled, but because it’s "understood" This foundation model captures now the structure and relationships between transactions — in real time — the way GPT models understand the flow of words in a sentence. Stripe no longer needs a different model for every use case. They’ve built one that generalizes across many — and keeps learning. 𝗧𝗵𝗲 𝗿𝗲𝘀𝘂𝗹𝘁? They tested it on one of the hardest problems in the space: Card testing attacks that hide in legitimate traffic. ➜ Traditional ML: 59% detection ➜ Transformer-based model: 97% — overnight Visionary work by Stripe! BUT this approach has implications far beyond payments. Great example to see that foundation models aren’t limited to text. The next phase of AI will probably focus more on transformer architectures trained on high-value, underexplored data domains: transactions, supply chains, behavioral signals, scientific processes — even spreadsheets. 𝗜 𝗮𝗺 𝗽𝗿𝗲𝘁𝘁𝘆 𝘀𝘂𝗿𝗲 𝘄𝗲 𝘄𝗶𝗹𝗹 𝘀𝗲𝗲 𝗺𝘂𝗰𝗵 𝗺𝗼𝗿𝗲 𝗱𝗼𝗺𝗮𝗶𝗻-𝘀𝗽𝗲𝗰𝗶𝗳𝗶𝗰 𝗳𝗼𝘂𝗻𝗱𝗮𝘁𝗶𝗼𝗻 𝗺𝗼𝗱𝗲𝗹𝘀 — 𝗽𝘂𝗿𝗽𝗼𝘀𝗲-𝗯𝘂𝗶𝗹𝘁 𝘁𝗼 𝗼𝗽𝗲𝗿𝗮𝘁𝗲 𝗶𝗻𝘀𝗶𝗱𝗲 𝗰𝗼𝗺𝗽𝗹𝗲𝘅 𝘀𝘆𝘀𝘁𝗲𝗺𝘀 𝗹𝗶𝗸𝗲 𝗳𝗶𝗻𝗮𝗻𝗰𝗲, 𝗵𝗲𝗮𝗹𝘁𝗵𝗰𝗮𝗿𝗲, 𝗮𝗻𝗱 𝗲𝗻𝗲𝗿𝗴𝘆. 𝗙𝗼𝗿 𝘆𝗲𝗮𝗿𝘀, 𝗺𝗮𝗰𝗵𝗶𝗻𝗲 𝗹𝗲𝗮𝗿𝗻𝗶𝗻𝗴 𝗵𝗮𝘀 𝗯𝗲𝗲𝗻 𝗳𝗼𝗰𝘂𝘀𝗲𝗱 𝗼𝗻 𝗹𝗮𝗯𝗲𝗹𝗶𝗻𝗴 𝗽𝗿𝗼𝗯𝗹𝗲𝗺𝘀. 𝗡𝗼𝘄, 𝘄𝗲'𝗿𝗲 𝗲𝗻𝘁𝗲𝗿𝗶𝗻𝗴 𝗮 𝗽𝗵𝗮𝘀𝗲 𝘄𝗵𝗲𝗿𝗲 𝗺𝗼𝗱𝗲𝗹𝘀 𝗯𝗲𝗴𝗶𝗻 𝘁𝗼 𝘂𝗻𝗱𝗲𝗿𝘀𝘁𝗮𝗻𝗱 𝘁𝗵𝗲𝗺 — 𝘀𝘁𝗿𝘂𝗰𝘁𝘂𝗿𝗮𝗹𝗹𝘆, 𝗰𝗼𝗻𝘁𝗲𝘅𝘁𝘂𝗮𝗹𝗹𝘆, 𝗮𝗻𝗱 𝗮𝘁 𝘀𝗰𝗮𝗹𝗲. Full story in the comments. 𝗣.𝗦. 𝗜 𝗷𝘂𝘀𝘁 𝗹𝗮𝘂𝗻𝗰𝗵𝗲𝗱 𝗮 𝗳𝗿𝗲𝗲 𝗻𝗲𝘄𝘀𝗹𝗲𝘁𝘁𝗲𝗿 𝗼𝗻 𝗔𝗜 𝗮𝗴𝗲𝗻𝘁𝘀 𝗮𝗻𝗱 𝘄𝗼𝗿𝗸𝗳𝗹𝗼𝘄𝘀 — 𝗮𝗹𝗿𝗲𝗮𝗱𝘆 𝗿𝗲𝗮𝗱 𝗯𝘆 𝟮𝟬,𝟬𝟬𝟬+. 𝗝𝗼𝗶𝗻 𝗵𝗲𝗿𝗲: https://lnkd.in/dbf74Y9E

Payments have evolved from paper and plastic to APIs and orchestration - giving rise to a new breed of players that simplify the complexity and connect the dots behind the scenes. Here's how we got here. 𝟭. 𝗜𝗻 𝘁𝗵𝗲 𝗽𝗿𝗲-𝟭𝟵𝟵𝟬𝘀 𝗲𝗿𝗮, banks owned the entire payments value chain -acquiring, processing, settlement. Merchant onboarding was complex, and domestic clearing systems ruled. 𝟮. 𝗧𝗵𝗲 𝗿𝗶𝘀𝗲 𝗼𝗳 𝗲-𝗰𝗼𝗺𝗺𝗲𝗿𝗰𝗲 in the late 1990s changed everything. Players like PayPal and Authorize made online payments possible, while banks began exiting the acquiring space or partnering with processors to keep up with demand. 𝟯. 𝗕𝗲𝘁𝘄𝗲𝗲𝗻 𝟮𝟬𝟬𝟬 𝗮𝗻𝗱 𝟮𝟬𝟭𝟬, specialized gateways and regional wallets began to scale, offering merchants greater flexibility and control. The launch of SEPA in Europe marked a push toward payment harmonization, while non-bank players started building infrastructure that bypassed traditional acquiring models altogether. 𝟰. 𝗧𝗵𝗲 𝘀𝗵𝗶𝗳𝘁 𝘁𝗼 𝗔𝗣𝗜-𝗱𝗿𝗶𝘃𝗲𝗻 𝗶𝗻𝗳𝗿𝗮𝘀𝘁𝗿𝘂𝗰𝘁𝘂𝗿𝗲 transformed payments from siloed systems into modular, developer-friendly tools. Merchant onboarding became faster, integrations simpler, and innovation more scalable. Open Banking regulations enabled direct access to bank data, while new credit models redefined consumer behavior. Payments evolved into a flexible, programmable layer of the digital economy. 𝟱. 𝗧𝗼𝗱𝗮𝘆, we’re in the age of seamless integration. Payments are embedded in everything - from ride-hailing apps to SuperApps. Real-time rails like SEPA Instant, UPI and PIX are live. CBDCs are in pilot. However, as payment ecosystems grow more fragmented - with new methods, regional schemes, compliance layers, and fraud risks -complexity has become a major bottleneck for merchants, fintechs, and even banks. Integrating multiple providers, maintaining uptime across systems, and ensuring regulatory compliance isn't just costly - it's unsustainable without the right foundation. This is where a new breed of infrastructure players like 𝗔𝗸𝘂𝗿𝗮𝘁𝗲𝗰𝗼 fit in - offering the tools to simplify complexity and still retain control. • 𝗪𝗵𝗶𝘁𝗲-𝗹𝗮𝗯𝗲𝗹 𝗽𝗮𝘆𝗺𝗲𝗻𝘁 𝗴𝗮𝘁𝗲𝘄𝗮𝘆𝘀 let banks, PSPs, and fintechs launch their own branded platforms fast - without building from scratch. • 𝗣𝗮𝘆𝗺𝗲𝗻𝘁 𝗼𝗿𝗰𝗵𝗲𝘀𝘁𝗿𝗮𝘁𝗶𝗼𝗻 enables merchants to route transactions dynamically across multiple acquirers, reducing costs and failed payments while improving UX. • 𝗕𝗮𝗻𝗸𝘀 can embed API-driven acquiring services into their offerings without the burden of a full-scale tech overhaul. In a world where growth brings fragmentation, the real challenge isn’t enabling payments - it’s managing them. The advantage will lie with infrastructure that can unify complexity, adapt in real time, and scale across borders without adding friction. Opinions: my own, Graphic source: Akurateco Payment Hub Subscribe to my newsletter: https://lnkd.in/dkqhnxdg

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

Africa quietly processed 64 billion instant payment transactions worth nearly 2 trillion dollars in 2024. That is not a fintech headline. That is economic infrastructure hiding in plain sight. This week on the Unlocking Africa Podcast, I sat down with Sabine F. Mensah, Deputy CEO of AfricaNenda Foundation and co-author of the State of Inclusive Instant Payment Systems in Africa 2025 report, one of the most comprehensive studies ever produced on Africa’s real time payments ecosystem. What stood out most in this conversation was how clearly it reframed payments, not as a niche fintech topic, but as core economic infrastructure driving trade, productivity, and inclusion. As Sabine explained… “Digital payments mean more people are accessing and using digital payments and leveraging them to contribute to productive activities that can drive the economy.” Drawing on insights from 31 countries, we explored why Nigeria has emerged as Africa’s first fully mature instant payment system, and why this success was not accidental. In Sabine’s words… “It is not just about speed. It is about who is included and how systems are designed from day one.” We discussed: • Why scale alone does not guarantee inclusion • How interoperability transforms SME cash flow and liquidity • Why instant payments are foundational to AfCFTA success • How real time settlement changes growth outcomes for African businesses • Why trust, consumer protection, and recourse mechanisms matter as much as infrastructure One line that stayed with me throughout the episode… “There is no trade without payment. Digital payments are as important as ports and customs.” And a reminder that inclusion is deeply human... “It is not just one consumer with a bad experience. It is my family, my village, my community.” This episode is essential listening for policymakers, investors, founders, and anyone serious about doing business in Africa. Payment systems are no longer background infrastructure. They are central to growth. ⬇️ Listen now, link in the comments below ⬇️ #AfCFTA #DigitalPublicInfrastructure #PaymentsInfrastructure #AfricaTrade #InclusiveGrowth #Podcast

In this deep dive edition of Fintech Wrap Up, I explored how AWS is enabling businesses to build modern credit card payment processing platforms and payment gateways with its powerful cloud infrastructure. As payments become increasingly digital, AWS provides a secure, scalable, and resilient solution to handle credit card transactions efficiently and in real-time. By using services like API Gateway, DynamoDB, Elastic Kubernetes Service (EKS), and Amazon Managed Streaming for Apache Kafka, businesses can meet high availability and low latency requirements while adhering to compliance standards like PCI DSS. The article delves into the lifecycle of credit card transactions, from authorization to clearing and settlement, offering detailed reference architectures for both the acquiring and issuing processes. It highlights AWS’s capabilities to support global expansion, manage compliance in different regions, and protect sensitive data through tools like AWS Payment Cryptography and ElastiCache. Key features include the ability to scale operations during seasonal spikes, maintain stringent security protocols, and automate monitoring for real-time issue detection. Whether businesses are enhancing their fraud prevention mechanisms, optimizing tokenization processes, or ensuring compliance with industry regulations, AWS’s cloud infrastructure provides the flexibility and reliability needed to succeed in today’s fast-evolving payments ecosystem. If you’re looking to future-proof your payment systems, this deep dive is packed with essential insights! #fintech #payments #aws #cardprocessing Prasanna Thomas Richard Panagiotis Tony Nicolas Arjun Dr Ritesh Sandra