{"id":17659,"date":"2025-10-15T12:21:07","date_gmt":"2025-10-15T12:21:07","guid":{"rendered":"https:\/\/buvei.com\/blog\/?p=17659"},"modified":"2025-10-15T12:21:07","modified_gmt":"2025-10-15T12:21:07","slug":"ai-for-virtual-card-fraud-detection","status":"publish","type":"post","link":"https:\/\/buvei.com\/blog\/ai-for-virtual-card-fraud-detection\/","title":{"rendered":"AI for Virtual Card Fraud Detection"},"content":{"rendered":"<p data-start=\"586\" data-end=\"1155\">In an era of rapidly expanding digital payments, <strong data-start=\"635\" data-end=\"652\">virtual cards<\/strong> have become a popular way to enhance security by masking underlying card details. Yet, fraudsters continue to evolve, targeting virtual card systems with sophisticated attacks. To stay ahead, platforms must deploy advanced <strong data-start=\"876\" data-end=\"898\">AI fraud detection<\/strong> techniques optimized for virtual card transactions. In this article, we explore how AI is used to detect fraud on virtual card transactions and present <strong data-start=\"1051\" data-end=\"1080\">four strategic approaches<\/strong> that platforms like <strong data-start=\"1101\" data-end=\"1110\">Buvei<\/strong> can adopt to heighten reliability and trust.<\/p>\n<p data-start=\"586\" data-end=\"1155\"><img loading=\"lazy\" decoding=\"async\" class=\"alignnone size-full wp-image-17666\" src=\"https:\/\/wordpress.buvei.com\/wp-content\/uploads\/2025\/10\/AI-for-Virtual-Card-Fraud-Detection\uff0c\uff0c\uff0c\uff0c\uff0c\uff0c\uff0c\uff0c-\u8fd9\u662f\u6211\u7684\u6807\u9898\uff0c\u5e2e\u6211\u751f\u6210\u51e0\u5f20\u9996\u56fe\uff0c\u4e0d\u8981\u592a\u82b1\uff0c\u4e0d\u8981\u6587\u5b57.jpg\" alt=\"\" width=\"1600\" height=\"896\" srcset=\"https:\/\/wordpress.buvei.com\/wp-content\/uploads\/2025\/10\/AI-for-Virtual-Card-Fraud-Detection\uff0c\uff0c\uff0c\uff0c\uff0c\uff0c\uff0c\uff0c-\u8fd9\u662f\u6211\u7684\u6807\u9898\uff0c\u5e2e\u6211\u751f\u6210\u51e0\u5f20\u9996\u56fe\uff0c\u4e0d\u8981\u592a\u82b1\uff0c\u4e0d\u8981\u6587\u5b57.jpg 1600w, https:\/\/wordpress.buvei.com\/wp-content\/uploads\/2025\/10\/AI-for-Virtual-Card-Fraud-Detection\uff0c\uff0c\uff0c\uff0c\uff0c\uff0c\uff0c\uff0c-\u8fd9\u662f\u6211\u7684\u6807\u9898\uff0c\u5e2e\u6211\u751f\u6210\u51e0\u5f20\u9996\u56fe\uff0c\u4e0d\u8981\u592a\u82b1\uff0c\u4e0d\u8981\u6587\u5b57-300x168.jpg 300w, https:\/\/wordpress.buvei.com\/wp-content\/uploads\/2025\/10\/AI-for-Virtual-Card-Fraud-Detection\uff0c\uff0c\uff0c\uff0c\uff0c\uff0c\uff0c\uff0c-\u8fd9\u662f\u6211\u7684\u6807\u9898\uff0c\u5e2e\u6211\u751f\u6210\u51e0\u5f20\u9996\u56fe\uff0c\u4e0d\u8981\u592a\u82b1\uff0c\u4e0d\u8981\u6587\u5b57-1024x573.jpg 1024w, https:\/\/wordpress.buvei.com\/wp-content\/uploads\/2025\/10\/AI-for-Virtual-Card-Fraud-Detection\uff0c\uff0c\uff0c\uff0c\uff0c\uff0c\uff0c\uff0c-\u8fd9\u662f\u6211\u7684\u6807\u9898\uff0c\u5e2e\u6211\u751f\u6210\u51e0\u5f20\u9996\u56fe\uff0c\u4e0d\u8981\u592a\u82b1\uff0c\u4e0d\u8981\u6587\u5b57-768x430.jpg 768w, https:\/\/wordpress.buvei.com\/wp-content\/uploads\/2025\/10\/AI-for-Virtual-Card-Fraud-Detection\uff0c\uff0c\uff0c\uff0c\uff0c\uff0c\uff0c\uff0c-\u8fd9\u662f\u6211\u7684\u6807\u9898\uff0c\u5e2e\u6211\u751f\u6210\u51e0\u5f20\u9996\u56fe\uff0c\u4e0d\u8981\u592a\u82b1\uff0c\u4e0d\u8981\u6587\u5b57-1536x860.jpg 1536w, https:\/\/wordpress.buvei.com\/wp-content\/uploads\/2025\/10\/AI-for-Virtual-Card-Fraud-Detection\uff0c\uff0c\uff0c\uff0c\uff0c\uff0c\uff0c\uff0c-\u8fd9\u662f\u6211\u7684\u6807\u9898\uff0c\u5e2e\u6211\u751f\u6210\u51e0\u5f20\u9996\u56fe\uff0c\u4e0d\u8981\u592a\u82b1\uff0c\u4e0d\u8981\u6587\u5b57-400x224.jpg 400w, https:\/\/wordpress.buvei.com\/wp-content\/uploads\/2025\/10\/AI-for-Virtual-Card-Fraud-Detection\uff0c\uff0c\uff0c\uff0c\uff0c\uff0c\uff0c\uff0c-\u8fd9\u662f\u6211\u7684\u6807\u9898\uff0c\u5e2e\u6211\u751f\u6210\u51e0\u5f20\u9996\u56fe\uff0c\u4e0d\u8981\u592a\u82b1\uff0c\u4e0d\u8981\u6587\u5b57-800x448.jpg 800w, https:\/\/wordpress.buvei.com\/wp-content\/uploads\/2025\/10\/AI-for-Virtual-Card-Fraud-Detection\uff0c\uff0c\uff0c\uff0c\uff0c\uff0c\uff0c\uff0c-\u8fd9\u662f\u6211\u7684\u6807\u9898\uff0c\u5e2e\u6211\u751f\u6210\u51e0\u5f20\u9996\u56fe\uff0c\u4e0d\u8981\u592a\u82b1\uff0c\u4e0d\u8981\u6587\u5b57-832x466.jpg 832w, https:\/\/wordpress.buvei.com\/wp-content\/uploads\/2025\/10\/AI-for-Virtual-Card-Fraud-Detection\uff0c\uff0c\uff0c\uff0c\uff0c\uff0c\uff0c\uff0c-\u8fd9\u662f\u6211\u7684\u6807\u9898\uff0c\u5e2e\u6211\u751f\u6210\u51e0\u5f20\u9996\u56fe\uff0c\u4e0d\u8981\u592a\u82b1\uff0c\u4e0d\u8981\u6587\u5b57-1248x699.jpg 1248w\" sizes=\"auto, (max-width: 1600px) 100vw, 1600px\" \/><\/p>\n<h2 data-start=\"1162\" data-end=\"1218\">Real-Time Anomaly Detection<a href=\"https:\/\/buvei.com\/blog\/virtual-cards-payment-security-tokenization-dynamic-cvv\/\"> via Machine Learning<\/a><\/h2>\n<p data-start=\"1220\" data-end=\"1519\">One of the foundational uses of AI in fraud detection is <strong data-start=\"1277\" data-end=\"1298\">anomaly detection<\/strong>: identifying transactions that deviate from established behavioral norms. For virtual cards, this means learning patterns of usage (amount ranges, merchant types, geographic locations, frequency) and flagging outliers.<\/p>\n<ul data-start=\"1521\" data-end=\"2360\">\n<li data-start=\"1521\" data-end=\"1781\">\n<p data-start=\"1523\" data-end=\"1781\"><strong data-start=\"1523\" data-end=\"1543\">Isolation Forest<\/strong> is a popular unsupervised algorithm for anomaly detection. It isolates anomalies by partitioning the data space; transactions that are easier to isolate (i.e. \u201coutliers\u201d) are flagged as suspicious.<\/p>\n<\/li>\n<li data-start=\"1521\" data-end=\"1781\">\n<p data-start=\"1523\" data-end=\"1781\"><strong data-start=\"1784\" data-end=\"1804\">Ensemble methods<\/strong> or <strong data-start=\"1808\" data-end=\"1843\">gradient-boosted decision trees<\/strong> (e.g. XGBoost) are trained on historic labeled data to classify whether a transaction is legitimate or fraudulent. Combined with anomaly scores, they achieve high precision.<\/p>\n<\/li>\n<li data-start=\"2058\" data-end=\"2360\">\n<p data-start=\"2060\" data-end=\"2360\"><strong data-start=\"2060\" data-end=\"2113\">Graph-based models \/ Graph Neural Networks (GNNs)<\/strong> treat transactions, cards, merchants, and devices as nodes in a graph and learn relational patterns. These can detect collusive fraud or network-level schemes that are invisible to transaction-level models.<\/p>\n<\/li>\n<\/ul>\n<p data-start=\"2362\" data-end=\"2518\">In practice, a hybrid approach\u2014combining anomaly detection, supervised classifiers, and graph insights\u2014yields stronger detection with fewer false positives.<\/p>\n<p data-start=\"2362\" data-end=\"2518\"><a href=\"https:\/\/app.buvei.com\/?s=blog\"><img loading=\"lazy\" decoding=\"async\" class=\"alignnone size-full wp-image-17247\" src=\"https:\/\/wordpress.buvei.com\/wp-content\/uploads\/2025\/10\/39805008-9713-4734-8f50-1fc13313bbeb-18272166-1.png\" alt=\"\" width=\"1024\" height=\"307\" srcset=\"https:\/\/wordpress.buvei.com\/wp-content\/uploads\/2025\/10\/39805008-9713-4734-8f50-1fc13313bbeb-18272166-1.png 1024w, https:\/\/wordpress.buvei.com\/wp-content\/uploads\/2025\/10\/39805008-9713-4734-8f50-1fc13313bbeb-18272166-1-300x90.png 300w, https:\/\/wordpress.buvei.com\/wp-content\/uploads\/2025\/10\/39805008-9713-4734-8f50-1fc13313bbeb-18272166-1-768x230.png 768w, https:\/\/wordpress.buvei.com\/wp-content\/uploads\/2025\/10\/39805008-9713-4734-8f50-1fc13313bbeb-18272166-1-400x120.png 400w, https:\/\/wordpress.buvei.com\/wp-content\/uploads\/2025\/10\/39805008-9713-4734-8f50-1fc13313bbeb-18272166-1-800x240.png 800w, https:\/\/wordpress.buvei.com\/wp-content\/uploads\/2025\/10\/39805008-9713-4734-8f50-1fc13313bbeb-18272166-1-832x249.png 832w\" sizes=\"auto, (max-width: 1024px) 100vw, 1024px\" \/><\/a><\/p>\n<h2 data-start=\"2525\" data-end=\"2567\">Risk Scoring &amp; <a href=\"https:\/\/buvei.com\/blog\/virtual-cards-transform-metaverse-purchases\/\">Dynamic Decisioning<\/a><\/h2>\n<p data-start=\"2569\" data-end=\"2742\">Once a transaction is evaluated, AI models assign a <strong data-start=\"2621\" data-end=\"2635\">risk score<\/strong> that quantifies how likely it is to be fraudulent. Depending on the score, different actions can be taken:<\/p>\n<ul data-start=\"2744\" data-end=\"2940\">\n<li data-start=\"2744\" data-end=\"2790\">\n<p data-start=\"2746\" data-end=\"2790\"><strong data-start=\"2746\" data-end=\"2771\">Approve automatically<\/strong> when risk is low<\/p>\n<\/li>\n<li data-start=\"2791\" data-end=\"2843\">\n<p data-start=\"2793\" data-end=\"2843\"><strong data-start=\"2793\" data-end=\"2813\">Decline outright<\/strong> when risk is extremely high<\/p>\n<\/li>\n<li data-start=\"2844\" data-end=\"2940\">\n<p data-start=\"2846\" data-end=\"2940\"><strong data-start=\"2846\" data-end=\"2893\">Challenge \/ require additional verification<\/strong> (e.g. 2FA, identity checks) in the gray zone<\/p>\n<\/li>\n<\/ul>\n<p data-start=\"2942\" data-end=\"3408\">This <strong data-start=\"2947\" data-end=\"2970\">dynamic decisioning<\/strong> allows the system to adapt to new fraud patterns without overly restricting legitimate usage. Large players like Visa have added <strong data-start=\"3100\" data-end=\"3114\">VAAI Score<\/strong>, a generative-AI powered risk model, to better identify enumeration attacks and reduce false positives. Mastercard has similarly used generative AI to accelerate compromised card detection and lower false positives.<\/p>\n<p data-start=\"3410\" data-end=\"3595\">For a virtual card platform like Buvei, integrating such scoring systems can empower issuing and authorization decisions, minimizing friction for good users while catching fraud faster.<\/p>\n<h2 data-start=\"3602\" data-end=\"3646\">Continuous Learning &amp; <a href=\"https:\/\/buvei.com\/blog\/cftc-and-sec-launch-crypto-sprint-for-digital-asset-rules\/\">Feedback Loops<\/a><\/h2>\n<p data-start=\"3648\" data-end=\"3805\">Fraud evolves constantly\u2014models trained on historical data degrade over time. To keep detection robust, continuous learning and feedback loops are essential:<\/p>\n<ul data-start=\"3807\" data-end=\"4322\">\n<li data-start=\"3807\" data-end=\"3914\">\n<p data-start=\"3809\" data-end=\"3914\"><strong data-start=\"3809\" data-end=\"3834\">Online model updating<\/strong>: Retrain models periodically (daily, hourly) with the latest data and labels.<\/p>\n<\/li>\n<li data-start=\"3915\" data-end=\"4035\">\n<p data-start=\"3917\" data-end=\"4035\"><strong data-start=\"3917\" data-end=\"3936\">Active learning<\/strong>: For uncertain cases, send them to human review; incorporate reviewer feedback to refine models.<\/p>\n<\/li>\n<li data-start=\"4036\" data-end=\"4171\">\n<p data-start=\"4038\" data-end=\"4171\"><strong data-start=\"4038\" data-end=\"4061\">Adaptive thresholds<\/strong>: Adjust risk score thresholds dynamically based on transaction volumes, seasonal patterns, or fraud spikes.<\/p>\n<\/li>\n<li data-start=\"4172\" data-end=\"4322\">\n<p data-start=\"4174\" data-end=\"4322\"><strong data-start=\"4174\" data-end=\"4201\">Concept drift detection<\/strong>: Monitor model performance metrics (false positive rate, precision, recall) and trigger alerts when drift is detected.<\/p>\n<\/li>\n<\/ul>\n<p data-start=\"4324\" data-end=\"4460\">Such a lifecycle ensures the AI system adapts to new fraud strategies and does not become stale.<\/p>\n<h2 data-start=\"4467\" data-end=\"4516\">Multi-Modal Signals &amp; Feature Engineering<\/h2>\n<p data-start=\"4518\" data-end=\"4654\">Relying only on transaction data is insufficient. Robust fraud detection systems integrate <strong data-start=\"4609\" data-end=\"4632\">multi-modal signals<\/strong> and curated features:<\/p>\n<ul data-start=\"4656\" data-end=\"5235\">\n<li data-start=\"4656\" data-end=\"4758\">\n<p data-start=\"4658\" data-end=\"4758\"><strong data-start=\"4658\" data-end=\"4706\">Device fingerprinting &amp; environment features<\/strong>: Browser, OS, IP address, device ID, geolocation.<\/p>\n<\/li>\n<li data-start=\"4759\" data-end=\"4849\">\n<p data-start=\"4761\" data-end=\"4849\"><strong data-start=\"4761\" data-end=\"4786\">Behavioral biometrics<\/strong>: Typing rhythm, mouse movement, transaction timing patterns.<\/p>\n<\/li>\n<li data-start=\"4850\" data-end=\"4966\">\n<p data-start=\"4852\" data-end=\"4966\"><strong data-start=\"4852\" data-end=\"4872\">Merchant context<\/strong>: Merchant risk scores, merchant category codes, transaction velocity to specific merchants.<\/p>\n<\/li>\n<li data-start=\"4967\" data-end=\"5057\">\n<p data-start=\"4969\" data-end=\"5057\"><strong data-start=\"4969\" data-end=\"4995\">Network\/graph features<\/strong>: Connections among cards, accounts, devices, and merchants.<\/p>\n<\/li>\n<li data-start=\"5058\" data-end=\"5146\">\n<p data-start=\"5060\" data-end=\"5146\"><strong data-start=\"5060\" data-end=\"5081\">Temporal features<\/strong>: Time since last transaction, periodic usage patterns, bursts.<\/p>\n<\/li>\n<li data-start=\"5147\" data-end=\"5235\">\n<p data-start=\"5149\" data-end=\"5235\"><strong data-start=\"5149\" data-end=\"5174\">External data sources<\/strong>: Blacklists, fraud intelligence feeds, chargeback history.<\/p>\n<\/li>\n<\/ul>\n<p data-start=\"5237\" data-end=\"5438\">Strong feature engineering amplifies the signal-to-noise ratio for AI models. The more dimensions of data you feed in, the more subtle fraud patterns the system can detect, while reducing false alarms.<\/p>\n<h2 data-start=\"5445\" data-end=\"5493\">Enhancing Reliability: Strategies for Buvei<\/h2>\n<p data-start=\"5495\" data-end=\"5605\">To make AI-based fraud detection robust and trustworthy for your virtual card platform, Buvei should consider:<\/p>\n<ul data-start=\"5607\" data-end=\"6951\">\n<li data-start=\"5607\" data-end=\"5801\">\n<p data-start=\"5609\" data-end=\"5801\"><strong data-start=\"5609\" data-end=\"5641\">Risk tiering &amp; card controls<\/strong>: Issue virtual cards with customizable limits, merchant restrictions, or usage windows. These built-in controls reduce exposure and simplify fraud detection.<\/p>\n<\/li>\n<li data-start=\"5802\" data-end=\"6003\">\n<p data-start=\"5804\" data-end=\"6003\"><strong data-start=\"5804\" data-end=\"5832\">Hybrid human + AI review<\/strong>: For borderline or high-value transactions flagged by AI, route them to fraud analysts for human validation. This helps reduce false declines and trains models further.<\/p>\n<\/li>\n<li data-start=\"6004\" data-end=\"6139\">\n<p data-start=\"6006\" data-end=\"6139\"><strong data-start=\"6006\" data-end=\"6039\">Explainability &amp; transparency<\/strong>: Use interpretable models or explainable AI layers so that decisions can be audited and debugged.<\/p>\n<\/li>\n<li data-start=\"6140\" data-end=\"6288\">\n<p data-start=\"6142\" data-end=\"6288\"><strong data-start=\"6142\" data-end=\"6170\">Simulation &amp; red teaming<\/strong>: Regularly test your fraud system using synthetic fraud attacks and adversarial techniques to identify blind spots.<\/p>\n<\/li>\n<li data-start=\"6289\" data-end=\"6445\">\n<p data-start=\"6291\" data-end=\"6445\"><strong data-start=\"6291\" data-end=\"6324\">Anomaly alerting &amp; dashboards<\/strong>: Build real-time monitoring dashboards to visualize fraud trends, KPI shifts, and alert operators to sudden anomalies.<\/p>\n<\/li>\n<li data-start=\"6446\" data-end=\"6622\">\n<p data-start=\"6448\" data-end=\"6622\"><strong data-start=\"6448\" data-end=\"6483\">Credit and behavioral profiling<\/strong>: Enrich user profiles with verified identity data and behavior over time (e.g., login history, account age) to strengthen fraud signals.<\/p>\n<\/li>\n<li data-start=\"6623\" data-end=\"6781\">\n<p data-start=\"6625\" data-end=\"6781\"><strong data-start=\"6625\" data-end=\"6654\">Data protection &amp; privacy<\/strong>: Ensure compliance with data regulations (e.g. GDPR, CCPA) and anonymize or pseudonymize data when used for training models.<\/p>\n<\/li>\n<li data-start=\"6782\" data-end=\"6951\">\n<p data-start=\"6784\" data-end=\"6951\"><strong data-start=\"6784\" data-end=\"6822\">Partnerships &amp; shared intelligence<\/strong>: Participate in fraud intel networks or consortiums so suspicious patterns detected by other platforms can inform your models.<\/p>\n<\/li>\n<\/ul>\n<p data-start=\"6953\" data-end=\"7084\">By embedding these strategies into your virtual card infrastructure, Buvei can offer differentiated security with minimal friction.<\/p>\n<h2 data-start=\"7091\" data-end=\"7106\"><img loading=\"lazy\" decoding=\"async\" class=\"alignnone size-full wp-image-17667\" src=\"https:\/\/wordpress.buvei.com\/wp-content\/uploads\/2025\/10\/\u672a\u547d\u540d\u7684\u8bbe\u8ba1-22.jpg\" alt=\"\" width=\"1600\" height=\"896\" srcset=\"https:\/\/wordpress.buvei.com\/wp-content\/uploads\/2025\/10\/\u672a\u547d\u540d\u7684\u8bbe\u8ba1-22.jpg 1600w, https:\/\/wordpress.buvei.com\/wp-content\/uploads\/2025\/10\/\u672a\u547d\u540d\u7684\u8bbe\u8ba1-22-300x168.jpg 300w, https:\/\/wordpress.buvei.com\/wp-content\/uploads\/2025\/10\/\u672a\u547d\u540d\u7684\u8bbe\u8ba1-22-1024x573.jpg 1024w, https:\/\/wordpress.buvei.com\/wp-content\/uploads\/2025\/10\/\u672a\u547d\u540d\u7684\u8bbe\u8ba1-22-768x430.jpg 768w, https:\/\/wordpress.buvei.com\/wp-content\/uploads\/2025\/10\/\u672a\u547d\u540d\u7684\u8bbe\u8ba1-22-1536x860.jpg 1536w, https:\/\/wordpress.buvei.com\/wp-content\/uploads\/2025\/10\/\u672a\u547d\u540d\u7684\u8bbe\u8ba1-22-400x224.jpg 400w, https:\/\/wordpress.buvei.com\/wp-content\/uploads\/2025\/10\/\u672a\u547d\u540d\u7684\u8bbe\u8ba1-22-800x448.jpg 800w, https:\/\/wordpress.buvei.com\/wp-content\/uploads\/2025\/10\/\u672a\u547d\u540d\u7684\u8bbe\u8ba1-22-832x466.jpg 832w, https:\/\/wordpress.buvei.com\/wp-content\/uploads\/2025\/10\/\u672a\u547d\u540d\u7684\u8bbe\u8ba1-22-1248x699.jpg 1248w\" sizes=\"auto, (max-width: 1600px) 100vw, 1600px\" \/><\/h2>\n<h2 data-start=\"7091\" data-end=\"7106\">Conclusion<\/h2>\n<p data-start=\"7108\" data-end=\"7702\">As virtual cards gain in popularity, fraudsters will continue to innovate. Deploying effective <strong data-start=\"7203\" data-end=\"7225\">AI fraud detection<\/strong> tailored to virtual transactions is no longer optional\u2014it\u2019s essential. By combining <strong data-start=\"7310\" data-end=\"7402\">real-time anomaly detection, risk scoring, continuous learning, and multi-modal features<\/strong>, a platform like Buvei can proactively guard against threats while delivering a frictionless user experience. With strategic enhancements\u2014controls, human oversight, explainability, and intelligence sharing\u2014Buvei can position itself as a trusted, secure virtual card platform in a competitive market.<\/p>\n<p data-start=\"7108\" data-end=\"7702\"><a href=\"https:\/\/app.buvei.com\/?s=blog\"><img loading=\"lazy\" decoding=\"async\" class=\"alignnone size-full wp-image-17247\" src=\"https:\/\/wordpress.buvei.com\/wp-content\/uploads\/2025\/10\/39805008-9713-4734-8f50-1fc13313bbeb-18272166-1.png\" alt=\"\" width=\"1024\" height=\"307\" srcset=\"https:\/\/wordpress.buvei.com\/wp-content\/uploads\/2025\/10\/39805008-9713-4734-8f50-1fc13313bbeb-18272166-1.png 1024w, https:\/\/wordpress.buvei.com\/wp-content\/uploads\/2025\/10\/39805008-9713-4734-8f50-1fc13313bbeb-18272166-1-300x90.png 300w, https:\/\/wordpress.buvei.com\/wp-content\/uploads\/2025\/10\/39805008-9713-4734-8f50-1fc13313bbeb-18272166-1-768x230.png 768w, https:\/\/wordpress.buvei.com\/wp-content\/uploads\/2025\/10\/39805008-9713-4734-8f50-1fc13313bbeb-18272166-1-400x120.png 400w, https:\/\/wordpress.buvei.com\/wp-content\/uploads\/2025\/10\/39805008-9713-4734-8f50-1fc13313bbeb-18272166-1-800x240.png 800w, https:\/\/wordpress.buvei.com\/wp-content\/uploads\/2025\/10\/39805008-9713-4734-8f50-1fc13313bbeb-18272166-1-832x249.png 832w\" sizes=\"auto, (max-width: 1024px) 100vw, 1024px\" \/><\/a><\/p>\n","protected":false},"excerpt":{"rendered":"In an era of rapidly expanding digital payments, virtual cards have become a popular way to enhance security&hellip;","protected":false},"author":4,"featured_media":17667,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"csco_singular_sidebar":"","csco_page_header_type":"","csco_page_load_nextpost":""},"categories":[2516,1],"tags":[10910,67,10916,10912,10914,262,5568],"class_list":{"0":"post-17659","1":"post","2":"type-post","3":"status-publish","4":"format-standard","5":"has-post-thumbnail","7":"category-case-studies","8":"category-payment-basics","9":"tag-ai-fraud-detection","10":"tag-buvei","11":"tag-generative-ai","12":"tag-machine-learning","13":"tag-risk-scoring","14":"tag-virtual-card-en","15":"tag-virtual-card-platform","16":"cs-entry"},"aioseo_notices":[],"_links":{"self":[{"href":"https:\/\/buvei.com\/blog\/wp-json\/wp\/v2\/posts\/17659","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/buvei.com\/blog\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/buvei.com\/blog\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/buvei.com\/blog\/wp-json\/wp\/v2\/users\/4"}],"replies":[{"embeddable":true,"href":"https:\/\/buvei.com\/blog\/wp-json\/wp\/v2\/comments?post=17659"}],"version-history":[{"count":0,"href":"https:\/\/buvei.com\/blog\/wp-json\/wp\/v2\/posts\/17659\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/buvei.com\/blog\/wp-json\/wp\/v2\/media\/17667"}],"wp:attachment":[{"href":"https:\/\/buvei.com\/blog\/wp-json\/wp\/v2\/media?parent=17659"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/buvei.com\/blog\/wp-json\/wp\/v2\/categories?post=17659"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/buvei.com\/blog\/wp-json\/wp\/v2\/tags?post=17659"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}