Payment Processing Optimization

I've helped dozens of acquirers, including Adyen, Checkout.com, and others optimize their authorization rates. 99% of them fall into 3 big traps... Mistakes that keep them bleeding revenue through unnecessary declines. Here's how to quickly fix them (so you can start maximizing approval rates today): Before data scientists got involved in payments, optimization wasn't really a thing. Engineers just found the fastest way to get their work done, often creating these systemic issues that persist today. So here are a few traps to avoid and how to fix them: TRAP 1: Generic Response Code Abuse Most teams send 80%+ of declines as "05: Do Not Honor" or "51: Not Sufficient Funds." This renders your data useless. You can't identify trends, optimize strategies, or help merchants understand why transactions fail. Strategic Fix: Treat response codes as your optimization roadmap. The more granular your codes, the more likely you are to find patterns that can drive intelligent retry logic and merchant coaching. TRAP 2: Blanket Decline Strategies Teams block entire countries, merchant categories, or transaction types "just to be safe." This kills legitimate transactions and frustrates customers who then switch to competitors. Strategic Fix: Risk is contextual, not categorical. Build dynamic risk models that consider transaction velocity, device fingerprinting, and behavioral patterns rather than static rules. TRAP 3: Static Authorization Hold Periods Most acquirers hold authorizations for 7+ days, blocking customer spending power unnecessarily. Strategic Fix: Authorization holds are working capital management. Analyze settlement timing by merchant segment to optimize cash flow without increasing risk. Other ways to increase authorization rates and revenue include: Account Updater: Automatically updates expired card details with merchants, preventing recurring payment failures Stand-In Processing: When issuers are offline, optimized STIP parameters can approve low-risk transactions instead of blanket declines Real-time alerts: Building alerts to notify when BINs are underperforming, so you can take appropriate actions such as Dynamic 3DS or Payment Flagging. The result? Acquirers who focus on implementing these fixes see 15-25% fewer unnecessary declines within as little as 60 days. Authorization optimization isn't just about approving more transactions; it's about intelligently managing risk while maximizing revenue per transaction attempt. P.S. During this summer, I have turned my newsletter into a Payments 4.0 Summer School, every week I will go deep, explaining the current trends and opportunities, providing the best frameworks and strategies. Subscribe here https://lnkd.in/etQJ2Tb5 to get it.

🚨 𝐇𝐨𝐰 𝐭𝐨 𝐏𝐨𝐰𝐞𝐫 𝐏𝐚𝐲𝐦𝐞𝐧𝐭𝐬 𝐰𝐢𝐭𝐡 𝐀𝐈 — by DEUNA👇 Modern payments no longer just process — they reason, adapt, and optimize. This post breaks down the architecture of an AI-native payments ecosystem — and how leading enterprises are using it to reduce friction, improve approval rates, and drive intelligent growth. — 𝐓𝐡𝐞 𝐄𝐯𝐨𝐥𝐯𝐢𝐧𝐠 𝐑𝐨𝐥𝐞 𝐨𝐟 𝐀𝐈 𝐢𝐧 𝐏𝐚𝐲𝐦𝐞𝐧𝐭𝐬 AI connects the dots between data, security, speed, personalization, and behavior — enabling intelligent action in real time. 🔹 Security → Real-time fraud scoring and behavioral anomaly detection. Microsoft leverages AI to detect coordinated fraud attacks across geographies, using real-time IP fingerprinting and dynamic 3DS decisions. 🔹 Speed → Automated decisioning and optimized checkout logic. eBay deploys AI models to streamline its global checkout flow, dynamically adjusting the experience by market, device, and payment method trends. 🔹 Personalization → Adaptive routing and dynamic UX. Checkout.com enables merchants to personalize payment options at checkout based on customer history, issuer behavior, and local preferences. 🔹 Behavioral Data → Continuous learning from patterns in issuer behavior, retries, fraud triggers, and consumer habits. — 𝐓𝐡𝐞 𝐂𝐨𝐫𝐞 𝐋𝐚𝐲𝐞𝐫𝐬 𝐨𝐟 𝐀𝐈 𝐢𝐧 𝐏𝐚𝐲𝐦𝐞𝐧𝐭𝐬 (hypothetical examples) 1️⃣ Unified Data → Consolidation of data from PSPs (e.g., Getnet, Stripe, Payplug), fraud tools, CRMs, and internal commerce systems. → eBay standardizes transaction-level data across global PSPs and marketplaces to enable unified performance insights and routing logic. 2️⃣ Agentic Intelligence → A reasoning layer that evaluates and ranks millions of routing paths, retries, and fraud strategies based on expected outcome. → Getnet merchants in LATAM use ATHIA to switch routing strategies in real time during issuer outages. 3️⃣ Machine Learning → ML models tailored to commerce — optimizing for approval rates, fraud risk, customer type, and payment method behavior. → Google uses ATHIA’s ML models to proactively adjust retry windows for license renewals based on historical bank acceptance timing. 4️⃣ Analysis & Visualization → Data is transformed into dynamic visualizations that surface anomalies and opportunities without requiring deep SQL or manual dashboards. → Stripe provides merchants with visual routing breakdowns and simulated outcomes — 𝐓𝐡𝐞 𝐎𝐮𝐭𝐜𝐨𝐦𝐞: 𝐀 𝐒𝐲𝐬𝐭𝐞𝐦 𝐓𝐡𝐚𝐭 𝐀𝐜𝐭𝐬 — 𝐍𝐨𝐭 𝐉𝐮𝐬𝐭 𝐑𝐞𝐩𝐨𝐫𝐭𝐬 ✅ Contextual checkout experiences by geography and device ✅ Lower transaction costs via intelligent acquirer routing ✅ Higher approval rates through dynamic retries ✅ Reduced fraud and false declines with adaptive scoring AI in payments is no longer experimental. It’s the backbone of scalable, programmable commerce. Intelligence in motion. — Source: DEUNA ► 𝐓𝐡𝐞 𝐏𝐚𝐲𝐦𝐞𝐧𝐭𝐬 𝐁𝐫𝐞𝐰𝐬: https://lnkd.in/g5cDhnjC ► Connecting the dots in Payments... | Marcel van Oost

𝗧𝗵𝗲 𝗘𝘃𝗼𝗹𝘂𝘁𝗶𝗼𝗻 𝗼𝗳 𝗣𝗮𝘆𝗺𝗲𝗻𝘁 𝗢𝗿𝗰𝗵𝗲𝘀𝘁𝗿𝗮𝘁𝗶𝗼𝗻 Legacy routing is breaking under the weight of today’s shifts. Static retries can't keep up anymore, especially when approval rates, cost, fraud, and user experience vary so widely by region, rail, and PSP. This is where AI-powered optimization comes in. Here’s what the shift looks like👇 𝗧𝗵𝗲 𝗣𝗿𝗼𝗯𝗹𝗲𝗺 𝗪𝗶𝘁𝗵 𝗦𝘁𝗮𝘁𝗶𝗰 𝗥𝗼𝘂𝘁𝗶𝗻𝗴 Most payment systems still rely on sequential logic → Route to PSP A with a Network Token → If it fails, retry at PSP A with the PAN → If that fails, retry at PSP B with the PAN → If that fails, payment flow is over This is inefficient and reactive Each failure adds latency, hurts conversions, and risks duplicate auths AI flips the script. Instead of reacting to failures, it predicts success before the first call is made 𝗛𝗼𝘄 𝗔𝗜 𝗥𝗼𝘂𝘁𝗶𝗻𝗴 𝗪𝗼𝗿𝗸𝘀 Advanced orchestration engines now use models that consider dozens of real-time inputs: → Card type, issuing bank, and BIN behavior → Past performance of specific PSPs for similar transactions → Country-level fraud trends and regulatory friction → Network-level auth patterns → Time of day, value, and payment method used With enough volume and clean data, the system learns which route is likely to maximize approvals, minimize cost, and avoid fraud 𝗘𝘅𝗮𝗺𝗽𝗹𝗲: 𝗗𝗲𝘂𝗻𝗮 📌 LATAM orchestration provider DEUNA exemplifies the shift Their Athia engine uses ML to dynamically route transactions based on performance data. The results? Huge improvements: ✔️ +8–10% approval uplift ✔️ ~15% GMV recovered ✔️ ~12% cost reduction ✔️ ~30% conversion rate uplift Deuna adapts per country and issuer, even for high-risk verticals or cash-preferred regions, without merchants needing to write logic 𝗪𝗵𝘆 𝗠𝗲𝗿𝗰𝗵𝗮𝗻𝘁𝘀 𝗦𝗵𝗼𝘂𝗹𝗱 𝗖𝗮𝗿𝗲 → AI optimizes at the source, no more blind retries → It reduces false positives, letting good customers through faster → It optimizes cost, avoiding higher-fee PSPs when possible → It scales dynamically as merchants enter new markets or expand PM's For high-volume merchants or those operating internationally, the right AI is a competitive advantage 𝗧𝗵𝗲 𝗧𝗿𝗮𝗱𝗲-𝗢𝗳𝗳𝘀 → AI needs clean data and sufficient volume to work → Not every "AI" tool provides deep AI insights, some are rocks painted gold → AI shouldn't replace business rules, it should work alongside them Merchants still need controls around fraud, compliance, and payment method availability 𝗙𝗶𝗻𝗮𝗹 𝗧𝗵𝗼𝘂𝗴𝗵𝘁𝘀 AI is reshaping the payments stack from checkout to clearing. For payment orchestration, it represents a big shift from stale logic to real-time, data-driven optimization Source: Deuna 🔔 Follow Jason Heister for daily #Fintech and #Payments guides, technical breakdowns, and industry insights

Traditional payment processing creates a single point of failure. When you use one processor, you inherit ALL their relationship problems with the card networks. High chargebacks? Your problem. Suspicious activity flags? Your clean business takes the hit. It's like letting your messy roommate's credit score affect yours 📉 Multi-PSP orchestration fixes this: ➡️Single processor: 82% authorization rate ➡️Multi-PSP optimization: 95%+ authorization rates That’s a 13-15% immediate revenue boost just from transactions that SHOULD have worked. Geographic arbitrage is insane with: ➡️US business serving EU customers: 2.7% + international fees ➡️With local acquiring: can get access to as low as 1.5% domestic EU rates Processing $1M monthly at an 82% authorization rate means leaving $180K on the table every year. While competitors focus on marketing optimization, the smart businesses are capturing 15% more revenue from the same traffic. So the question is... Can you afford to keep losing 15% of your legitimate revenue to preventable payment failures?

What if I told you that your payment system is quietly leaking millions of dollars every year — and you don’t even see it? Let’s learn a case study by Tranzzo. Most conversations around payment orchestration focus on connectivity, compliance, and coverage. But the real threat to revenue often hides in plain sight: your routing logic. Revenue leakage from poor routing rarely shows up as a red flag. Merchants don’t usually “see” the missed approvals — they only notice slightly lower revenue, unexpected churn, or strange fluctuations in conversion metrics. Each misrouted transaction quietly erodes unit economics, and the damage only becomes obvious when it’s already significant. 👉 From my experience, key sources of hidden loss include: 🔹 Grey zone transactions - delayed approvals that temporarily block cash flow and reduce operational predictability. 🔹 Overpaid transaction fees - static routing often sends payments through suboptimal PSPs, unnecessarily increasing costs. 🔹 Data loss for fraud and risk models - limited routing insights reduce the predictive accuracy of anti-fraud algorithms. 🔹 Short-term channel optimization - focusing solely on the cheapest PSP or corridor can negatively impact customer lifetime value (LTV), especially in cross-border payments. Industry data confirms the impact - approval rate gaps between optimal vs. suboptimal routing can range from 5% to 12%, depending on vertical and geography. For large merchants, that difference translates directly into millions of dollars annually. I have had a conversation about this topic with the team at Tranzzo, who’ve spent years refining adaptive smart routing for merchants across Europe, America, and MENA. Their approach to dynamic orchestration — routing decisions made in real time, based on live data: 🔹 PSP uptime and latency 🔹 Geo-specific approval trends 🔹 Currency conversion efficiency 🔹 Even time-of-day performance patterns In practice, this means the system continuously learns and adapts. Every payment is routed along the optimal path to maximize approval rates while minimizing costs. The benefits extend beyond cost efficiency — merchants gain faster settlement cycles, improved cash flow predictability, and stronger customer trust. 👉 The key takeaway In a fragmented global payments ecosystem, relying on a single acquirer or static routing logic is no longer viable. The real competitive edge lies in the ability to adapt transactions dynamically, at scale, and in real-time. The question I keep returning to is: how many merchants are silently leaking revenue today — and how many will take the strategic leap to treat routing as a core part of their growth playbook? #fintech #payments #paymentorchestration

Checkout optimization used to mean adding more payment methods. Today it’s about shaping the payment journey before friction ever shows up. Fintech Adyen just launched Personalize inside its Uplift suite. The headline feature is real-time Dynamic Identification, trained on trillions of transactions across its network. Why it matters: 37% of shoppers abandon when checkout takes too long. 72% of businesses say transaction fees are pressuring margins. Static checkout flows treat every buyer the same. Modern payment stacks can’t afford that. Personalize adjusts the experience in real time. It can: • Prioritize cost-efficient payment rails • Suppress unnecessary authentication • Surface risk signals before authorization • Route transactions based on identity and context Early data: • 9.4% lower payment costs on eligible traffic in year one of Uplift • 42% reduction in false positives • +1.19% average conversion lift, up to 6% for some merchants • Pilots showing up to 3% lower transaction costs • Tebi: 4.26% cost savings and 0.8% conversion lift This is not incremental CRO. The real shift is architectural. Checkout is becoming a data and feedback loop problem, not a front-end design problem. The platforms that unify acquiring, issuing, risk, and identity inside one system will compound advantages over time. If you’re running payments at scale: Are you optimizing a page… or optimizing a network?

Back in 2015, I made a mistake that many founders still make today: I thought payments were just a commodity. Connect a provider, then forget about it, right? Wrong. After building Rappi and now Yuno, I learned that the payments landscape has evolved dramatically. → Multiple payment processors needed per country → New payment methods launching every month → Complex approval rate optimization → Fraud management → Local regulations in each market As a result, we see: → Companies losing money due to suboptimal payment setups → Opportunities missed because of limited payment options → Resources wasted on complex integrations A recent example: One of our merchants accepted an alternative payment method with a 40% conversion. "That's normal," they said. Their provider told them so. We ran an A/B test with a different provider. The result: 70% conversion. That's the difference a payment orchestrator/partner can make - we: - Ask the right questions - Run the right tests - Optimize what others assume is "normal" Payment infrastructure can be either your biggest limitation or your strongest competitive advantage. The choice is yours.

💡 𝗦𝘁𝗿𝗮𝘁𝗲𝗴𝘆 + 𝗣𝗹𝘂𝗴𝗶𝗻 𝗗𝗲𝘀𝗶𝗴𝗻 𝗣𝗮𝘁𝘁𝗲𝗿𝗻𝘀 + 𝗗𝗗𝗗 Integrating multiple payment methods like PayPal and Stripe into an e-commerce platform can be tricky. With the Strategy and Plugin patterns, you can make the process much more flexible, extensible, and maintainable. Here's how: 𝟭. 𝗦𝘁𝗿𝗮𝘁𝗲𝗴𝘆 𝗣𝗮𝘁𝘁𝗲𝗿𝗻: Interchangeable Payment Methods •  Each payment method (PayPal, Stripe) follows a common IPaymentMethod interface. The PaymentService delegates the payment processing task to the appropriate payment method. •  How It Follows SOLID: 𝗦𝗶𝗻𝗴𝗹𝗲 𝗥𝗲𝘀𝗽𝗼𝗻𝘀𝗶𝗯𝗶𝗹𝗶𝘁𝘆: Each payment method focuses solely on processing payments. 𝗢𝗽𝗲𝗻/𝗖𝗹𝗼𝘀𝗲𝗱: You can easily add new payment methods without altering existing code. 𝗟𝗶𝘀𝗸𝗼𝘃 𝗦𝘂𝗯𝘀𝘁𝗶𝘁𝘂𝘁𝗶𝗼𝗻: You can swap payment methods without breaking anything. 𝗜𝗻𝘁𝗲𝗿𝗳𝗮𝗰𝗲 𝗦𝗲𝗴𝗿𝗲𝗴𝗮𝘁𝗶𝗼𝗻: The interface is focused, with no unnecessary methods. 𝗗𝗲𝗽𝗲𝗻𝗱𝗲𝗻𝗰𝘆 𝗜𝗻𝘃𝗲𝗿𝘀𝗶𝗼𝗻: The PaymentService depends on the abstraction (IPaymentMethod), not the concrete implementation. 𝟮. 𝗣𝗹𝘂𝗴𝗶𝗻 𝗣𝗮𝘁𝘁𝗲𝗿𝗻: 𝗗𝘆𝗻𝗮𝗺𝗶𝗰 𝗣𝗮𝘆𝗺𝗲𝗻𝘁 𝗠𝗲𝘁𝗵𝗼𝗱𝘀 •  Payment methods are implemented as plugins, loaded at runtime based on user input or configuration. •  Why It Follows 𝗖𝗹𝗲𝗮𝗻 Architecture: 𝗦𝗲𝗽𝗮𝗿𝗮𝘁𝗶𝗼𝗻 𝗼𝗳 𝗖𝗼𝗻𝗰𝗲𝗿𝗻𝘀: Payment logic is isolated, keeping the core system clean. 𝗠𝗼𝗱𝘂𝗹𝗮𝗿 𝗗𝗲𝘀𝗶𝗴𝗻: Payment methods are independent modules, easy to extend and maintain. 𝟯. 𝗛𝗼𝘄 𝗜𝘁 𝗙𝗶𝘁𝘀 𝘄𝗶𝘁𝗵 𝗗𝗗𝗗 (𝗗𝗼𝗺𝗮𝗶𝗻-𝗗𝗿𝗶𝘃𝗲𝗻 𝗗𝗲𝘀𝗶𝗴𝗻) •  Bounded Contexts: Each payment method (PayPal, Stripe) has its own distinct processing rules within its bounded context. •  Ubiquitous Language: Clear and consistent language around payment functionality. •  Aggregate Roots: Payment transactions are treated as aggregates, ensuring consistency. 𝟰. 𝗕𝗲𝗻𝗲𝗳𝗶𝘁𝘀 •  Scalability & Flexibility: Easily add or swap payment methods. •  Maintainability: Isolated payment logic makes updates and testing easier. •  SOLID Adherence: High cohesion, low coupling, and extensibility for future changes. 𝟱. 𝗧𝘂𝗿𝗻𝗶𝗻𝗴 𝘁𝗵𝗲 𝗣𝗮𝘆𝗺𝗲𝗻𝘁 𝗠𝗼𝗱𝘂𝗹𝗲 𝗶𝗻𝘁𝗼 𝗮 𝗠𝗶𝗰𝗿𝗼𝘀𝗲𝗿𝘃𝗶𝗰𝗲 The loosely coupled and modular design of the payment modules enables them to evolve into microservices with these steps: 𝗗𝗲𝗰𝗼𝗺𝗽𝗼𝘀𝗲 𝘁𝗵𝗲 𝗦𝗲𝗿𝘃𝗶𝗰𝗲: The payment module becomes its own microservice, handling all payment methods internally. 𝗔𝗣𝗜 𝗚𝗮𝘁𝗲𝘄𝗮𝘆: Route requests to the right payment microservice. 𝗘𝘃𝗲𝗻𝘁-𝗗𝗿𝗶𝘃𝗲n: Use event-driven communication (like RabbitMQ or Kafka) to decouple services. 𝗦𝗰𝗮𝗹𝗮𝗯𝗹𝗲 & 𝗜𝗻𝗱𝗲𝗽𝗲𝗻𝗱𝗲𝗻𝘁: Each payment service can scale independently. Disclaimer: I'm not claiming this is the 'best' way, just the way I thought of it. Feel free to roast my solution and offer better ones! #DotNet #DesignPatterns #DDD #CleanArchitecture #Microservices

In my pre-Bain life I did a fair amount work focused on making government payments accessible, easy to use, and modern. Recent DOGE efforts draw attention to the need for improvement, but I fear the result will be chaos. But it is a wake-up call for how we can do better. Payment enabled eGov solutions ought to be seamless, secure, and efficient. Instead, they are often a mess of inefficiency, manual processes, and legacy systems that frustrate both citizens and businesses. Governments can (and must) do better. Based on my work with municipal, state/provincial, and national agencies here in the U.S. and abroad, here are my suggestions: 1️⃣ Go digital—but do it right Paper checks and manual processing should be relics of the past. e-payments reduce costs, increase speed, and improve security. But modernization needs to be done strategically, not as a rushed power grab. The Government Finance Officers Association (GFOA) stresses the importance of clear policies to ensure smooth implementation. 2️⃣ Prioritize security and access controls One of DOGE’s biggest missteps was attempting to override Treasury’s existing safeguards. To retain trust, governments need to implement robust security protocols, multi-factor authentication, and access restrictions to prevent unauthorized use of sensitive financial data. 3️⃣ Build transparency and accountability Every payment should be auditable, and every decision should be accountable. Establishing clear oversight mechanisms prevents fraud and ensures public trust. Solutions like real-time transaction monitoring and transparent reporting help keep everyone honest. 4️⃣ Leverage APIs and interoperability Government payment systems should integrate seamlessly with banking infrastructure, tax agencies, and social services. APIs allow for better data exchange, reducing processing delays and ensuring more efficient fund distribution. 5️⃣ Ensure 24/7 availability Citizens rely on government payments for essentials. Government agencies can take advantage of round-the-clock payment rails. But real time payment infrastructure isn't enough. Gov agencies need redundancy measures in place to prevent downtime and must streamline internal processes to ensure that benefits and refunds aren’t delayed by bureaucratic inefficiencies. 6️⃣ Use smart reporting and analytics Robust data analytics can help detect anomalies, optimize agency cash flow management, and prevent fraud. Government entities should invest in AI-driven insights to improve forecasting and decision-making. The Path Forward Government payment modernization isn’t just about technology—it’s about trust. DOGE’s overreach highlights the dangers of prioritizing speed over thoughtful execution. The alternative? A strategic, well-governed shift toward digital, secure, and interoperable payments that serve the public good. The stakes are too high to get this wrong. Let’s make sure we get it right. (photo is me in Islamabad back in 2016)

💳 𝗣𝗮𝘆𝗺𝗲𝗻𝘁 𝗢𝗿𝗰𝗵𝗲𝘀𝘁𝗿𝗮𝘁𝗶𝗼𝗻: 𝗧𝗵𝗲 𝗜𝗻𝘁𝗲𝗹𝗹𝗶𝗴𝗲𝗻𝗰𝗲 𝗟𝗮𝘆𝗲𝗿 Most teams think payments = processing. But processing just moves money. Orchestration decides how well it moves. ⚡ 𝗘𝘃𝗲𝗿𝘆 𝘁𝗿𝗮𝗻𝘀𝗮𝗰𝘁𝗶𝗼𝗻 𝗶𝘀 𝗮 𝗿𝗲𝗮𝗹-𝘁𝗶𝗺𝗲 𝗱𝗲𝗰𝗶𝘀𝗶𝗼𝗻 → Which acquirer to route to → Whether to retry — and where → How to balance cost vs approval → How to fight fraud without killing conversion These decisions happen in under 200ms. If you're not controlling them — your PSP is. ⚙️ 𝗪𝗵𝗮𝘁 𝗮𝗻 𝗼𝗿𝗰𝗵𝗲𝘀𝘁𝗿𝗮𝘁𝗶𝗼𝗻 𝗲𝗻𝗴𝗶𝗻𝗲 𝗮𝗰𝘁𝘂𝗮𝗹𝗹𝘆 𝗱𝗼𝗲𝘀 It sits between your platform and your providers, adding an intelligence layer: → Smart routing by BIN, geo, amount, issuer behavior → Failover cascades across PSPs in <100ms → Real-time fraud scoring + device fingerprinting → A/B testing across providers to optimize cost & approval rate → 150+ custom rules: currency, issuer, time, amount 📊 𝗧𝗵𝗲 𝗻𝘂𝗺𝗯𝗲𝗿𝘀 𝘀𝗽𝗲𝗮𝗸 ↑ +8% approval rate ↓ −30% processing cost ⏱ 99.99% uptime ⚡ <200ms latency 🎯 𝗕𝗼𝘁𝘁𝗼𝗺 𝗹𝗶𝗻𝗲 You don’t need more payment providers. You need a smarter layer between them.