{"id":2,"date":"2026-08-31T22:36:36","date_gmt":"2026-08-31T20:36:36","guid":{"rendered":"https:\/\/verif-ai.eu\/?page_id=2"},"modified":"2026-08-31T22:54:09","modified_gmt":"2026-08-31T20:54:09","slug":"verif-ai","status":"publish","type":"page","link":"https:\/\/verif-ai.eu\/","title":{"rendered":"Verif-ai"},"content":{"rendered":"<div id=\"verif-ai-landing-1\" class=\"verif-ai-landing\" data-verif-ai-landing><header class=\"site-header\">\n    <a class=\"brand\" href=\"#verif-ai-landing-1-top\" aria-label=\"VERIF-AI home\"><img decoding=\"async\" src=\"https:\/\/verif-ai.eu\/wp-content\/plugins\/verif-ai-landing-shortcode\/assets\/logo.png\" alt=\"VERIF-AI\"><\/a>\n    <nav aria-label=\"Main navigation\"><a href=\"#verif-ai-landing-1-problem\">Problem<\/a><a href=\"#verif-ai-landing-1-security-gap\">Security gap<\/a><a href=\"#verif-ai-landing-1-approach\">Approach<\/a><a href=\"#verif-ai-landing-1-pilot\">Pilot<\/a><\/nav>\n    <a class=\"button button-outline\" href=\"#verif-ai-landing-1-pilot\">Book a pilot<\/a>\n  <\/header>\n\n  <main id=\"verif-ai-landing-1-top\">\n    <section class=\"hero section-shell\">\n      <div class=\"hero-copy\">\n        <p class=\"eyebrow\">INTELLIGENT PAYMENT PROTECTION<\/p>\n        <h1>The scam succeeds when the payment goes through.<\/h1>\n        <p class=\"lead\">Banks are good at verifying who is making a payment. The harder challenge is knowing whether a genuine customer is being manipulated into making it. <strong>VERIF-AI<\/strong> detects behavioural risk before funds are released, adding an extra layer of protection at the moment it matters most.<\/p>\n        <div class=\"button-row\"><a class=\"button button-primary\" href=\"#verif-ai-landing-1-approach\">See how VERIF-AI works <span aria-hidden=\"true\">\u2192<\/span><\/a><a class=\"button button-outline\" href=\"#verif-ai-landing-1-problem\">Why it matters <span aria-hidden=\"true\">\u2192<\/span><\/a><\/div>\n        <ul class=\"feature-line\" aria-label=\"Core VERIF-AI capabilities\"><li>Transaction-level analysis<\/li><li>Behaviour-based risk scoring<\/li><li>Human review for high-risk cases<\/li><\/ul>\n      <\/div>\n      <article class=\"transaction-review\" aria-label=\"Example transaction review\">\n        <header><h2>Transaction review<\/h2><span class=\"hold-badge\">\u25c7 Hold before release<\/span><\/header>\n        <dl class=\"review-facts\"><div><dt>Beneficiary<\/dt><dd>New payee<\/dd><\/div><div><dt>Amount<\/dt><dd>\u20ac8,950<\/dd><\/div><div><dt>Device<\/dt><dd>Trusted<\/dd><\/div><div><dt>Behaviour<\/dt><dd>Unusual urgency<\/dd><\/div><\/dl>\n        <div class=\"review-decision\"><div><span>Behaviour risk score<\/span><strong>78<small>\/100<\/small><\/strong><\/div><p><b>Verification required<\/b><span>before payment approval<\/span><\/p><\/div>\n        <div class=\"score-track\" aria-label=\"Behaviour risk score 78 out of 100\"><span><\/span><\/div>\n      <\/article>\n    <\/section>\n\n    <section class=\"problem section-block\" id=\"verif-ai-landing-1-problem\"><div class=\"section-shell\">\n      <p class=\"eyebrow\">01 \/ THE PROBLEM<\/p>\n      <div class=\"split-heading\"><h2>The customer approves the <span>payment<\/span>.<br>The scammer controls the decision.<\/h2><p>Scammers bypass security by exploiting the customer, not the bank. A genuine customer authenticates, adds the payee and approves the transfer, making the transaction appear legitimate until it is too late to recover the funds.<\/p><\/div>\n      <div class=\"fraud-stats\">\n        <article class=\"stat-feature\"><strong>\u20ac4.2B<\/strong><div><h3>Payment fraud reported across the EEA<\/h3><p>EBA and ECB reporting demonstrates the scale of payment fraud across Europe, reinforcing the need for prevention before funds are released.<\/p><a href=\"https:\/\/www.eba.europa.eu\/\" rel=\"noreferrer\">EBA \/ ECB source <span aria-hidden=\"true\">\u2192<\/span><\/a><\/div><\/article>\n        <div class=\"metric-grid\"><article><strong>\u00a3576.4M<\/strong><span>APP fraud losses<\/span><\/article><article><strong>\u00a3354.3M<\/strong><span>Reimbursed to victims<\/span><\/article><article><strong>+30%<\/strong><span>Reported phishing cases<\/span><\/article><\/div>\n      <\/div>\n    <\/div><\/section>\n\n    <section class=\"phishing section-block\" id=\"verif-ai-landing-1-security-gap\"><div class=\"section-shell\">\n      <div class=\"centered-heading\"><p class=\"eyebrow\">WHY PHISHING KEEPS WORKING<\/p><h2>The attacker doesn\u2019t need to break the bank.<br><span>They break the customer<\/span><\/h2><p>Modern fraud bypasses secure systems, MFA and login protections by manipulating real people. By the time fraud is detected, the money is already gone.<\/p><\/div>\n      <div class=\"phishing-content\">\n        <ol class=\"attack-steps\"><li><span class=\"step-icon\">01<\/span><div><h3>Impersonation creates trust<\/h3><p>Attackers pose as a bank, police, supplier or executive and create a believable reason to move money.<\/p><\/div><\/li><li><span class=\"step-icon\">02<\/span><div><h3>Urgency bypasses judgement<\/h3><p>Pressure, fear and time limits push customers to act before they verify what is happening.<\/p><\/div><\/li><li><span class=\"step-icon\">03<\/span><div><h3>Authentication looks legitimate<\/h3><p>The user is genuine, the device may be genuine and the login may be genuine\u2014but the intent has been manipulated.<\/p><\/div><\/li><li><span class=\"step-icon\">04<\/span><div><h3>The loss happens at payment<\/h3><p>Once the transaction is executed, the bank is left with recovery, reimbursement and customer-impact costs.<\/p><\/div><\/li><\/ol>\n        <blockquote class=\"quote-card\"><span class=\"quote-mark\">\u201c<\/span><p>Banks don\u2019t lose money because phishing exists. Banks lose money because fraudulent transactions are still executed.<\/p><footer>VERIF-AI \u00b7 Intelligent Payment Protection<\/footer><\/blockquote>\n      <\/div>\n    <\/div><\/section>\n\n    <section class=\"missing-layer section-block\"><div class=\"section-shell\">\n      <div class=\"centered-heading\"><p class=\"eyebrow\">THE MISSING LAYER<\/p><h2>Banks secure access.<br>VERIF-AI secures the <span>decision to pay<\/span><\/h2><p>MFA, awareness training, secure apps, email protection and fraud monitoring are essential. But when a genuine customer is being manipulated, every traditional control can still pass. VERIF-AI adds a <strong>decision layer<\/strong> at the point of payment.<\/p><\/div>\n      <ul class=\"control-chips\" aria-label=\"Traditional security controls\"><li>\u2713 MFA<\/li><li>\u2713 Awareness<\/li><li>\u2713 Secure apps<\/li><li>\u2713 Email protection<\/li><li>\u2713 Fraud monitoring<\/li><\/ul>\n      <ol class=\"decision-flow\"><li><span>01<\/span><div><h3>Authentication<\/h3><p>Identity and access checks succeed.<\/p><\/div><\/li><li><span>02<\/span><div><h3>Payment decision<\/h3><p>A genuine customer initiates and approves the transfer.<\/p><\/div><\/li><li class=\"danger\"><span>03<\/span><div><h3>Money gone<\/h3><p>The manipulation is discovered after the funds have moved.<\/p><\/div><\/li><\/ol>\n      <div class=\"decision-layer\"><span class=\"shield-mini\">\u2713<\/span><div><h3>VERIF-AI acts where traditional security stops.<\/h3><p>We analyse transaction context and customer behaviour before release\u2014while there is still time to intervene.<\/p><\/div><a class=\"button button-primary\" href=\"#verif-ai-landing-1-pilot\">Intervene before release <span aria-hidden=\"true\">\u2192<\/span><\/a><\/div>\n    <\/div><\/section>\n\n    <section class=\"how-it-works section-block\" id=\"verif-ai-landing-1-approach\"><div class=\"section-shell approach-grid\">\n      <div class=\"approach-copy\"><p class=\"eyebrow\">HOW VERIF-AI WORKS<\/p><h2>A payment can look legitimate.<br><span>The behaviour behind it may not be.<\/span><\/h2><p>This illustrative transaction shows how VERIF-AI evaluates payment context and customer behaviour to identify signs of manipulation before funds are released.<\/p><div class=\"dot-pattern\" aria-hidden=\"true\"><\/div><\/div>\n      <article class=\"analysis-card\"><header><h3>Illustrative transfer #VX-29481<\/h3><span>\u25cf Transaction analysis<\/span><\/header><dl class=\"analysis-facts\"><div><dt>Amount<\/dt><dd>\u20ac12,400<\/dd><\/div><div><dt>Beneficiary<\/dt><dd>First-time recipient<\/dd><\/div><div><dt>Device<\/dt><dd>Trusted mobile<\/dd><\/div><div><dt>Behaviour<\/dt><dd>Unusual navigation speed<\/dd><\/div><\/dl><div class=\"analysis-result\"><div><span>Behavioural risk score<\/span><strong>78<small>\/100<\/small><\/strong><div class=\"risk-scale\"><i><\/i><\/div><\/div><div><span>Decision<\/span><strong>Verification<br>required<\/strong><\/div><\/div><div class=\"risk-alert\"><b>Behavioural risk detected before release<\/b><span>VERIF-AI adds a decision layer at the point of payment.<\/span><\/div><small class=\"disclaimer\">Illustrative example only. No real customer or transaction data is shown.<\/small><\/article>\n    <\/div><\/section>\n\n    <section class=\"risk section-block\"><div class=\"section-shell risk-grid\">\n      <div class=\"risk-copy\"><p class=\"eyebrow\">HOW VERIF-AI ASSESSES RISK<\/p><h2>Risk is revealed by the pattern, not by a single signal.<\/h2><p>A first-time beneficiary is not automatically fraud. Neither is a large payment, a new location or unusual behaviour on its own. VERIF-AI evaluates these signals together against the customer\u2019s normal pattern.<\/p><ul class=\"tag-list\"><li>Combined signals<\/li><li>Behavioural context<\/li><li>Customer baseline<\/li><\/ul><\/div>\n      <div class=\"risk-map\" aria-label=\"Risk signals assessed together\"><img decoding=\"async\" src=\"https:\/\/verif-ai.eu\/wp-content\/plugins\/verif-ai-landing-shortcode\/assets\/orbit-shield.png\" alt=\"Shield surrounded by protective orbit\"><div class=\"risk-center\"><strong>Risk context<\/strong><span>Signals assessed together<\/span><\/div><div class=\"risk-node n1\"><b>\u20ac<\/b><span>Payment amount<\/span><\/div><div class=\"risk-node n2\"><b>\u25af<\/b><span>Device<\/span><\/div><div class=\"risk-node n3\"><b>\u25cb<\/b><span>Behaviour<\/span><\/div><div class=\"risk-node n4\"><b>\u2316<\/b><span>Location<\/span><\/div><div class=\"risk-node n5\"><b>\u25a3<\/b><span>Payment history<\/span><\/div><div class=\"risk-node n6\"><b>\u25cb<\/b><span>Beneficiary<\/span><\/div><\/div>\n    <\/div><\/section>\n\n    <section class=\"oversight section-block\"><div class=\"section-shell oversight-grid\">\n      <div><p class=\"eyebrow\">HUMAN OVERSIGHT<\/p><h2>AI identifies the risk.<br>People remain in control.<\/h2><p>VERIF-AI helps banks identify unusual payment behaviour and prioritise the cases that need attention. Low-risk payments continue normally, while uncertain or high-risk transactions can be verified or escalated to trained specialists before funds are released.<\/p><ul class=\"tag-list\"><li>Risk-based escalation<\/li><li>Human review<\/li><li>Decision before release<\/li><\/ul><\/div>\n      <div class=\"decision-cards\"><article class=\"low\"><span class=\"decision-icon\">\u2713<\/span><div><small>Low risk<\/small><h3>Normal payment flow<\/h3><p>Routine activity continues without unnecessary friction.<\/p><\/div><b>\u2192<\/b><\/article><article class=\"medium\"><span class=\"decision-icon\">?<\/span><div><small>Medium risk<\/small><h3>Customer verification<\/h3><p>Additional checks help confirm that the payment reflects the customer\u2019s genuine intent.<\/p><\/div><b>\u2192<\/b><\/article><article class=\"high\"><span class=\"decision-icon\">!<\/span><div><small>High risk<\/small><h3>Specialist review<\/h3><p>High-risk or unclear cases are escalated for human assessment before release.<\/p><\/div><b>\u2192<\/b><\/article><p class=\"ai-note\">AI informs the decision. People stay in control.<\/p><\/div>\n    <\/div><\/section>\n\n    <section class=\"value section-block\"><div class=\"section-shell\">\n      <p class=\"eyebrow\">VALUE FOR BANKS<\/p><div class=\"centered-heading compact\"><h2>Prevent fraud before it becomes a loss and reduce the cost that follows.<\/h2><p>Stopping a fraudulent payment before release can also reduce reimbursement, investigations, support demand and the wider impact on customer trust.<\/p><\/div>\n      <div class=\"value-layout\"><img decoding=\"async\" src=\"https:\/\/verif-ai.eu\/wp-content\/plugins\/verif-ai-landing-shortcode\/assets\/growth-shield.png\" alt=\"Growing protection and loss prevention\"><div class=\"value-cards\"><article><span>01<\/span><div><h3>Lower fraud losses<\/h3><p>Intervene before a manipulated payment is released and becomes a financial loss.<\/p><\/div><b>\u2192<\/b><\/article><article><span>02<\/span><div><h3>Lower operational pressure<\/h3><p>Reduce avoidable support calls, complaints, recovery work and fraud investigations.<\/p><\/div><b>\u2192<\/b><\/article><article><span>03<\/span><div><h3>Stronger customer trust<\/h3><p>Protect customers from sophisticated manipulation without adding unnecessary friction.<\/p><\/div><b>\u2192<\/b><\/article><article><span>04<\/span><div><h3>Better use of fraud specialists<\/h3><p>Prioritise human review around transactions carrying the strongest risk signals.<\/p><\/div><b>\u2192<\/b><\/article><\/div><\/div>\n    <\/div><\/section>\n\n    <section class=\"pilot section-block\" id=\"verif-ai-landing-1-pilot\"><div class=\"section-shell pilot-grid\">\n      <ol class=\"pilot-steps\"><li><span>01<\/span><div><h3>Define<\/h3><p>Agree the transaction scope, risk signals, decision logic and success criteria.<\/p><\/div><b>Design \u2192<\/b><\/li><li><span>02<\/span><div><h3>Pilot<\/h3><p>Test VERIF-AI within a controlled transaction segment and operating environment.<\/p><\/div><b>Test \u2192<\/b><\/li><li><span>03<\/span><div><h3>Validate<\/h3><p>Measure fraud detection, false positives, customer friction and operational impact.<\/p><\/div><b>Measure \u2192<\/b><\/li><li><span>04<\/span><div><h3>Scale<\/h3><p>Expand into wider fraud and risk operations only when the evidence supports it.<\/p><\/div><b>Scale \u2192<\/b><\/li><\/ol>\n      <div class=\"pilot-copy\"><p class=\"eyebrow\">PILOT PROPOSAL<\/p><h2>Prove the value in a controlled pilot, then scale with evidence.<\/h2><p>Start with a defined transaction segment and clear success criteria. The pilot validates detection quality, customer impact and operational value before broader deployment.<\/p><a class=\"button button-primary\" href=\"mailto:&#104;&#101;llo&#064;&#118;&#101;&#114;if&#045;ai.c&#111;&#109;\">Discuss a pilot <span aria-hidden=\"true\">\u2192<\/span><\/a><\/div>\n    <\/div><\/section>\n\n    <section class=\"trust\"><article><img decoding=\"async\" src=\"https:\/\/verif-ai.eu\/wp-content\/plugins\/verif-ai-landing-shortcode\/assets\/supplied-trust-left.png\" alt=\"Protection shield\"><h2>Build confidence before<br>broader deployment<\/h2><\/article><article><img decoding=\"async\" src=\"https:\/\/verif-ai.eu\/wp-content\/plugins\/verif-ai-landing-shortcode\/assets\/supplied-trust-right.png\" alt=\"Connected protection shield\"><h2>Protecting customer trust<br>before money moves<\/h2><\/article><\/section>\n  <\/main>\n\n  <footer class=\"site-footer\"><img decoding=\"async\" src=\"https:\/\/verif-ai.eu\/wp-content\/plugins\/verif-ai-landing-shortcode\/assets\/logo.png\" alt=\"VERIF-AI\"><p>\u00a9 2026 VERIF-AI. Intelligent Payment Protection.<\/p><\/footer><\/div>\n","protected":false},"excerpt":{"rendered":"","protected":false},"author":1,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"footnotes":""},"class_list":["post-2","page","type-page","status-publish","hentry"],"_links":{"self":[{"href":"https:\/\/verif-ai.eu\/index.php?rest_route=\/wp\/v2\/pages\/2","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/verif-ai.eu\/index.php?rest_route=\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/verif-ai.eu\/index.php?rest_route=\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/verif-ai.eu\/index.php?rest_route=\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/verif-ai.eu\/index.php?rest_route=%2Fwp%2Fv2%2Fcomments&post=2"}],"version-history":[{"count":9,"href":"https:\/\/verif-ai.eu\/index.php?rest_route=\/wp\/v2\/pages\/2\/revisions"}],"predecessor-version":[{"id":22,"href":"https:\/\/verif-ai.eu\/index.php?rest_route=\/wp\/v2\/pages\/2\/revisions\/22"}],"wp:attachment":[{"href":"https:\/\/verif-ai.eu\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=2"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}