{"id":77686,"date":"2026-10-07T11:22:25","date_gmt":"2026-10-07T14:22:25","guid":{"rendered":"https:\/\/evertectrends.com\/?p=77686"},"modified":"2026-10-06T15:48:44","modified_gmt":"2026-10-06T18:48:44","slug":"payment-fraud-artificial-intelligence","status":"publish","type":"post","link":"https:\/\/evertectrends.com\/en\/payment-fraud-artificial-intelligence\/","title":{"rendered":"AI against fraud: responding at the speed of the attack"},"content":{"rendered":"\n

Payment fraud doesn’t announce itself. On any given day, a merchant’s normal transaction flow gets mixed with a wave of suspicious attempts: hundreds of small charges testing stolen cards, repeated retries, and sudden spikes from sources that had never shown up before. By the time someone spots it in a report, the damage is already done.<\/p>\n\n\n\n

The difference between stopping an attack and absorbing its chargeback costs almost always comes down to one variable: how quickly you can make sense of what’s happening. That’s where artificial intelligence changed the game.<\/p>\n\n\n\n

Fraud moves at machine speed<\/h2>\n\n\n\n

The rise of e-commerce across Latin America and the Caribbean brought with it an equivalent leap in fraud sophistication. Attacks are no longer isolated attempts \u2014 they’ve become automated, large-scale operations that are increasingly hard to tell apart from legitimate traffic.<\/p>\n\n\n\n

An attacker no longer tests one card \u2014 they test thousands in minutes, from multiple sources, around the clock. At that speed, purely reactive models \u2014 reviewing the damage after it happens \u2014 simply fall short. Fraud prevention becomes a discipline of anticipation, and AI is the tool capable of processing, at the pace of business, a volume of data no human team could handle manually.<\/p>\n\n\n\n

What’s at stake: the real cost of CNP fraud<\/h2>\n\n\n\n

Card-not-present (CNP) fraud rarely feels like a single, decisive hit. It behaves more like a slow, silent leak \u2014 building quietly under the surface and striking across three fronts.<\/p>\n\n\n\n

A chargeback is a chain of costs, not a single amount. When a cardholder disputes a purchase, the merchant doesn’t lose once \u2014 they lose several times over. They lose the product already shipped, the revenue the bank reverses, the chargeback penalty fee, the team’s time managing the dispute, and, if chargeback rates climb, even their standing with card issuers. According to the True Cost of Fraud study by LexisNexis<\/a>, for every dollar lost to fraud, merchants in Latin America face roughly $3.50 in total costs.<\/p>\n\n\n\n

False positives are the invisible cost. Fraud shows up in reports; legitimate customers wrongly declined do not. That’s revenue that came knocking and was turned away by a poorly calibrated control. Which is why approval rate isn’t just an operational metric \u2014 it’s a direct lever on revenue.<\/p>\n\n\n\n

The third cost is time. Manually spotting patterns across thousands of transactions is slow and depends entirely on the analyst’s expertise. And by the time the picture comes together, the cards have already been tested and the chargebacks are already in motion.<\/p>\n\n\n\n

Continuous monitoring and AI analysis \u2014 in record time<\/h2>\n\n\n\n

This is where Evertec brings something different to the table. We’ve built an AI-powered fraud analysis system designed specifically for card-not-present transactions: e-commerce, MOTO, and digital wallets.<\/p>\n\n\n\n

The starting point isn’t a one-off procedure \u2014 it’s an ongoing process. The transaction flow is monitored continuously, with particular attention to those that were declined or flagged as suspicious. Across that volume, the analysis engine works without stopping:<\/p>\n\n\n\n

    \n
  1. Calculates key indicators: volume, approval and decline rates, response and reason codes, payment methods, and behavioral patterns by merchant, time of day, or transaction amount.<\/li>\n\n\n\n
  2. Detects risk patterns such as card testing, velocity by IP, suspicious retries, automated traffic, shared identities across different payers, unusual after-hours activity, authentication failures, and merchants with disproportionate decline rates.<\/li>\n\n\n\n
  3. Interprets the findings through an AI model tuned for CNP fraud and organizes them into a structured report \u2014 with an executive summary, descriptive analysis, detected patterns, and fraud hypotheses, each backed by its own evidence.<\/li>\n\n\n\n
  4. Ranks everything by severity \u2014 critical, high, medium, and low \u2014 so the team always knows what to address first.<\/li>\n<\/ol>\n\n\n\n

    More than a metrics dashboard, the system delivers an analysis that reads the situation, prioritizes risks, and proposes concrete actions. What used to take hours of manual review now happens in minutes \u2014 accelerating the response while an attack is still unfolding.<\/p>\n\n\n\n

    From detection to action: prioritized recommendations<\/h2>\n\n\n\n

    The difference between a report and a tool is that the tool tells you what to do. Each analysis comes with practical recommendations across three time horizons:<\/p>\n\n\n\n

    Immediate:<\/strong> rules to activate right away, suspicious IPs or BINs to block, quick actions to stop revenue from bleeding out.<\/p>\n\n\n\n

    Short-term:<\/strong> threshold adjustments and velocity controls to close the gaps the attack exploited, without catching legitimate customers in the crossfire.<\/p>\n\n\n\n

    Strategic:<\/strong> authentication improvements and control integrations to build stronger defenses for the long run.<\/p>\n\n\n\n

    The goal isn’t just to stop the fraud coming in \u2014 it’s also to reduce false positives and protect the approval rate: acting precisely on what’s suspicious while letting the genuine transactions through.<\/p>\n\n\n\n

    Beyond stopping incoming fraud, the objective is to reduce false positives and preserve approval rates by targeting suspicious activity without disrupting legitimate customer transactions.<\/p>\n\n\n\n

    The next step: a defense that adapts to the attack<\/h2>\n\n\n\n

    Detecting and recommending is the present. The next step is a defense that automatically adjusts to the threat level.<\/p>\n\n\n\n

    When the AI identifies a significant attack, it can recommend \u2014 and in the future, orchestrate \u2014 a temporary tightening of security rules. Once the risk has passed, those rules automatically return to their normal state, avoiding unnecessary friction for legitimate customers.<\/p>\n\n\n\n

    Think of it like an immune system: it activates when threatened and returns to baseline when the danger is gone.<\/p>\n\n\n\n

    It’s the natural evolution of fraud prevention \u2014 one that doesn’t just watch, but responds at the attacker’s own pace.<\/p>\n\n\n\n

    AI with human judgment: not a black box<\/h2>\n\n\n\n

    Skepticism toward AI usually comes from a fair place: no one wants to trigger critical rules based on a recommendation they can’t clearly explain.<\/p>\n\n\n\n

    That’s why the analysis is built to be auditable. Every hypothesis includes the evidence behind it and the data needed to validate it, so the team can review the recommendations and make the final call with clear, grounded information.<\/p>\n\n\n\n

    AI doesn’t replace the expert \u2014 it makes them more effective. While the system processes large volumes of transactions and spots patterns in minutes, human judgment still makes the final call.<\/p>\n\n\n\n

    A fraud prevention ecosystem built to support<\/h2>\n\n\n\n

    This capability integrates into the fraud prevention ecosystem of Evertec and Placetopay, complementing authentication tools, rules engines, and real-time risk management with a new layer of AI-assisted analysis.<\/p>\n\n\n\n

    The result is a fuller picture of operations and a faster response to emerging threats.<\/p>\n\n\n\n

    Responding at the speed of the attacker<\/h2>\n\n\n\n

    Fraud evolves at the pace of automation, and keeping up has stopped being a competitive advantage \u2014 it’s now a baseline requirement. AI makes it possible to turn massive transaction volumes into faster, better-informed decisions, helping protect revenue without compromising the customer experience.<\/p>\n\n\n\n

    At Evertec, we’re driving that evolution with solutions that combine artificial intelligence and advanced risk management to help our clients stay ahead of fraud and act with greater precision.<\/p>\n\n\n\n

    Want to know how AI can strengthen your fraud defenses? Talk to our experts<\/a> and find out how to stay ahead of fraud, protect your approval rate, and respond at the speed every attack demands.<\/p>\n","protected":false},"excerpt":{"rendered":"

    Payment fraud requires increasingly faster responses. Learn how artificial intelligence helps analyze transactions, detect suspicious patterns, prioritize risks, and support faster fraud prevention decisions.<\/p>\n","protected":false},"author":66,"featured_media":77685,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[2464],"tags":[1706,3600,3818,3815,1364,1792,3298,2413,3817,1413,3816],"class_list":["post-77686","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-security","tag-artificial-intelligence","tag-artigo-de-colunista","tag-chargebacks","tag-cnp-fraud","tag-e-commerce-en","tag-fraud-detection-en","tag-fraud-prevention","tag-ia-en-2","tag-payment-fraud","tag-payments","tag-risk-management"],"yoast_head":"\nPayment fraud: how AI speeds up prevention<\/title>\n<meta name=\"description\" content=\"Learn how AI helps identify payment fraud, detect suspicious patterns, prioritize risks, and accelerate 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