Card declines are a Data Problem! Card declines are a Data Problem! Yes, you read it right. Card declines are not just a risk issue, but is also considered as a data issue. Card declines are not just costly, but misunderstood too. Let me explain this. In Part 1 of my series , we saw how declines grow from vague issuer responses, outdated card info, or poor transaction context despite the user having sufficient funds. Seconding that, in Part 2, we explored actionable fixes like tokenization, retries, and metadata cleanup. Now, it is time that we switch gears to how Artificial Intelligence is providing an aid to payment approval and handling. As AI is evolving at an enormous pace, this shift is happening fast too. We're moving from static rules and blunt fraud filters to more adaptive, counter intuitive and context-aware intelligence that can gauge the difference between real fraud and false positives , and can in fact, coach users through a failed payment attempt. How do Traditional Systems work? How do Traditional Systems work? The Legacy authorization systems operate on fixed rules.For instance, the card declines as soon as the billing address ZIP does not match with the shipping address ZIP. These legacy systems use simple fraud scoring models and rely on minimal issuer feedback. These systems are not designed to learn from new behaviour. They evaluate declines based on pre-set patterns only and in this current fintech landscapes where there are new frauds detected every other day, that rigidity in decision making causes leads to a higher number of good transactions to be blocked than bad ones being caught. And this is exactly where we need Artificial Intelligence in play as it will be here that we can leverage the AI and the Large Language Models(LLMs) to bring adaptability, personalization, and real-time learning into the payment stack. How NOT to lose your good customers? How NOT to lose your good customers? Well, the answer here is simple. Design or use fraud detection systems using AI as fraud detection is one of the key places where AI is transforming your entire approvals flow. Unlike the traditional static rule engines, modern AI models: Continuously learn from new transactions → Score behaviour based on real-time context (IP, device fingerprint, session behaviour) → Predict the likelihood of fraud, not just match against blacklists. For instance, the Radar by Stripe uses ML models trained on hundreds of billions of global transactions and learns fraud patterns across industries and geographies. The result is lower chargebacks without tanking approval rates. Similarly, RevenueProtect by Adyen runs adaptive risk scoring that adjusts in milliseconds , using everything from device signals to behavioural analytics. The most important thing to note here is that fraud systems that are powered by Artificial Intelligence can drastically reduce false positives while protecting revenue and that exactly is something the static filters struggle with. What are Real-Time Decisioning Engines doing? What are Real-Time Decisioning Engines doing? Traditional payment risk engines ran in batches and often ran asynchronously or delayed for non-card payments. Today, Artificial Intelligence allows real-time risk analysis during the transaction flow. Modern engines directly look at the Session time vs. the average time, typing speed and the mouse movement, email domain reputation, Time zone vs. IP mismatch and the shopping behaviour compared to customer history and provide a decision. These modern engines make use of LLM models and assign a dynamic risk score, not just a binary yes/no. That risk score further feeds into the custom flows: Low risk? → Auto-approve Medium risk? → Add step-up (e.g., OTP or 3DS) High risk? → Decline or prompt for new payment Low risk? → Auto-approve Medium risk? → Add step-up (e.g., OTP or 3DS) High risk? → Decline or prompt for new payment This flexibility helps merchants sustain more good transactions- especially on mobile, cross-border, and new-user payments. Smart Retry Recommendations Smart Retry Recommendations One of the most common reasons for a failed transaction? Insufficient funds, and is often temporary. But instead of retrying it blindly, modern platforms use AI to predict when the user is most likely to have funds (e.g., post-salary, time-of-day, spending cycles), time retries for higher success and adapt retry behaviour based on past recovery data. The impact has been real. Evidence can be drawn from Recurly and Chargebee as both reported a 30–50% higher recovery of failed payments when using the adaptive retry models than scheduled attempts. a This is the autonomous artificial intelligence working behind the scenes . Where LLMs Come In ? Where LLMs Come In ? Most users see a generic error like, "Transaction failed. Please try again or use another card." "Transaction failed. Please try again or use another card." It is vague, frustrating, and offers no clarity, of course. But LLMs change that by generating real-time contextual, empathetic, and actionable communication. Instead of a code, the system can say, "Your card was declined due to a temporary bank issue. This sometimes happens with international purchases. Want to try again in a few minutes or use PayPal?" "Your card was declined due to a temporary bank issue. This sometimes happens with international purchases. Want to try again in a few minutes or use PayPal?" LLMs thereby help you map vague decline codes to helpful narratives, reducing the abandonment and support tickets. How LLMs have helped our Internal Support Tools? How LLMs have helped our Internal Support Tools? I work at Braintree, and here my teams often face declines in the "Do Not Honour" or "Issuer Unavailable" categories. Using LLMs internally, our support teams could translate these hard to remember decline codes to likely causes and recommend actions such as retry, prompt for a new method, escalate to issuer or even generate pre-filled user responses. This of course lead to faster support, less friction, and a better understanding of why a transaction failed. Quick Comparison between Traditional vs AI-Driven Payment Approvals Quick Comparison between Traditional vs AI-Driven Payment Approvals Capability Legacy System AI/LLM-Driven System Fraud Detection Static, rule-based filters Adaptive, ML-based scoring from real-time transaction data User Messaging Generic decline codes (e.g., “Do Not Honor”) Contextual, user-friendly explanations powered by LLMs Retry Strategy Fixed intervals with limited success Predictive retry timing based on user behavior & payment history Risk Scoring Binary (Approve/Decline) Continuous scoring with dynamic thresholds Support Tooling Manual investigation, static logs AI-assisted root cause analysis and smart response generation Cross-Border Handling High false decline rates due to lack of context Higher approval via behavioral and geo-intent models Security Compliance Rigid systems, often blind to nuance Transparent, auditable, and explainable AI logic Capability Legacy System AI/LLM-Driven System Fraud Detection Static, rule-based filters Adaptive, ML-based scoring from real-time transaction data User Messaging Generic decline codes (e.g., “Do Not Honor”) Contextual, user-friendly explanations powered by LLMs Retry Strategy Fixed intervals with limited success Predictive retry timing based on user behavior & payment history Risk Scoring Binary (Approve/Decline) Continuous scoring with dynamic thresholds Support Tooling Manual investigation, static logs AI-assisted root cause analysis and smart response generation Cross-Border Handling High false decline rates due to lack of context Higher approval via behavioral and geo-intent models Security Compliance Rigid systems, often blind to nuance Transparent, auditable, and explainable AI logic Capability Legacy System AI/LLM-Driven System Capability Capability Legacy System Legacy System AI/LLM-Driven System AI/LLM-Driven System Fraud Detection Static, rule-based filters Adaptive, ML-based scoring from real-time transaction data Fraud Detection Fraud Detection Fraud Detection Static, rule-based filters Static, rule-based filters Adaptive, ML-based scoring from real-time transaction data Adaptive, ML-based scoring from real-time transaction data User Messaging Generic decline codes (e.g., “Do Not Honor”) Contextual, user-friendly explanations powered by LLMs User Messaging User Messaging User Messaging Generic decline codes (e.g., “Do Not Honor”) Generic decline codes (e.g., “Do Not Honor”) Contextual, user-friendly explanations powered by LLMs Contextual, user-friendly explanations powered by LLMs Retry Strategy Fixed intervals with limited success Predictive retry timing based on user behavior & payment history Retry Strategy Retry Strategy Retry Strategy Fixed intervals with limited success Fixed intervals with limited success Predictive retry timing based on user behavior & payment history Predictive retry timing based on user behavior & payment history Risk Scoring Binary (Approve/Decline) Continuous scoring with dynamic thresholds Risk Scoring Risk Scoring Risk Scoring Binary (Approve/Decline) Binary (Approve/Decline) Continuous scoring with dynamic thresholds Continuous scoring with dynamic thresholds Support Tooling Manual investigation, static logs AI-assisted root cause analysis and smart response generation Support Tooling Support Tooling Support Tooling Manual investigation, static logs Manual investigation, static logs AI-assisted root cause analysis and smart response generation AI-assisted root cause analysis and smart response generation Cross-Border Handling High false decline rates due to lack of context Higher approval via behavioral and geo-intent models Cross-Border Handling Cross-Border Handling Cross-Border Handling High false decline rates due to lack of context High false decline rates due to lack of context Higher approval via behavioral and geo-intent models Higher approval via behavioral and geo-intent models Security Compliance Rigid systems, often blind to nuance Transparent, auditable, and explainable AI logic Security Compliance Security Compliance Security Compliance Rigid systems, often blind to nuance Rigid systems, often blind to nuance Transparent, auditable, and explainable AI logic Transparent, auditable, and explainable AI logic Compliance Matters and How? Compliance Matters and How? Using AI in payments or anywhere else as well comes loaded with both governance and compliance. These implemented intelligent systems must be GDPR/CCPA compliant and be able to provide audit trails for decisions. The models must avoid biased training data and allow for human overrides whenever necessary. Leading providers tackle this with explainable AI, rigorous testing, and transparency reports. The Decline Experience Is Getting Smarter The Decline Experience Is Getting Smarter What used to be a hard "No" is now a moment for insight, personalization, and recovery. i Do not think of AI and LLMs as modes for just fraud detection.They're reshaping how approvals work to allow more good transactions, context aware communication and allow for retry logic in order to recover any potentially lost revenue. And as the models get better with access to broader data and deeper issuer collaboration, the line between "declined" and "rescued" will blur further.
