{"id":399,"date":"2026-03-19T17:58:45","date_gmt":"2026-03-19T06:58:45","guid":{"rendered":"https:\/\/nexiant.ai\/resources\/blogs\/?p=399"},"modified":"2026-03-31T18:00:08","modified_gmt":"2026-03-31T07:00:08","slug":"aml-false-positive-reduction","status":"publish","type":"post","link":"https:\/\/nexiant.ai\/resources\/blogs\/aml-false-positive-reduction\/","title":{"rendered":"Reduce AML False Positives by 70%: AI-Powered Screening"},"content":{"rendered":"\n<style>\n  .nx-blog * { box-sizing: border-box; margin: 0; padding: 0; }\n  .nx-blog {\n    font-family: -apple-system, BlinkMacSystemFont, 'Segoe UI', Roboto, sans-serif;\n    font-size: 16px;\n    line-height: 1.75;\n    color: #1a1a2e;\n    max-width: 820px;\n    margin: 0 auto;\n  }\n\n  \/* \u2500\u2500 Typography \u2500\u2500 *\/\n  .nx-blog h2 {\n    font-size: 1.55rem;\n    font-weight: 700;\n    color: #00184C;\n    margin: 2.5rem 0 0.75rem;\n    padding-bottom: 0.4rem;\n    border-bottom: 3px solid #073EA1;\n  }\n  .nx-blog h3 {\n    font-size: 1.15rem;\n    font-weight: 700;\n    color: #073EA1;\n    margin: 1.75rem 0 0.5rem;\n  }\n  .nx-blog h4 {\n    font-size: 0.98rem;\n    font-weight: 700;\n    color: #00184C;\n    margin: 1.25rem 0 0.35rem;\n  }\n  .nx-blog p { margin-bottom: 1rem; 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color: #fff; }\n  .nx-table th { text-align: left; padding: 10px 14px; font-weight: 600; }\n  .nx-table td { padding: 9px 14px; border-bottom: 1px solid #e0e7f5; color: #1a1a2e; vertical-align: top; }\n  .nx-table tbody tr:nth-child(even) { background: #f5f8ff; }\n  .nx-badge {\n    display: inline-block;\n    font-size: 0.73rem;\n    font-weight: 600;\n    padding: 2px 9px;\n    border-radius: 20px;\n  }\n  .nx-badge--reduce { background: #fde8e8; color: #A30000; }\n  .nx-badge--improve { background: #e6f4ea; color: #1a6630; }\n  .nx-badge--stable { background: #EEF2FF; color: #073EA1; }\n\n  \/* \u2500\u2500 FAQ Accordion \u2500\u2500 *\/\n  .nx-faq { margin: 1.25rem 0 1.75rem; }\n  .nx-faq-item { border: 1px solid #d0daf5; border-radius: 8px; margin-bottom: 8px; overflow: hidden; }\n  .nx-faq-q {\n    width: 100%;\n    background: #fff;\n    border: none;\n    text-align: left;\n    padding: 1rem 1.25rem;\n    font-size: 0.95rem;\n    font-weight: 600;\n    color: #00184C;\n    cursor: pointer;\n    display: flex;\n    justify-content: space-between;\n    align-items: center;\n    gap: 1rem;\n  }\n  .nx-faq-q:hover { background: #f5f8ff; }\n  .nx-faq-q .nx-chevron {\n    flex-shrink: 0;\n    width: 20px;\n    height: 20px;\n    border-radius: 50%;\n    background: #EEF2FF;\n    display: flex;\n    align-items: center;\n    justify-content: center;\n    transition: transform 0.25s;\n  }\n  .nx-faq-q .nx-chevron svg { width: 10px; height: 10px; stroke: #073EA1; fill: none; }\n  .nx-faq-q[aria-expanded=\"true\"] .nx-chevron { transform: rotate(180deg); background: #073EA1; }\n  .nx-faq-q[aria-expanded=\"true\"] .nx-chevron svg { stroke: #fff; }\n  .nx-faq-a {\n    display: none;\n    padding: 0 1.25rem 1rem;\n    font-size: 0.92rem;\n    color: #333;\n    line-height: 1.7;\n    background: #fff;\n  }\n  .nx-faq-a.open { display: block; }\n\n  \/* \u2500\u2500 CTA \u2500\u2500 *\/\n  .nx-cta {\n    background: linear-gradient(135deg, #00184C 0%, #073EA1 100%);\n    border-radius: 12px;\n    padding: 2rem;\n    text-align: center;\n    margin-top: 2.5rem;\n  }\n  .nx-cta h3 { color: #fff; font-size: 1.3rem; font-weight: 700; margin-bottom: 0.5rem; }\n  .nx-cta p { color: #AEC9FF; font-size: 0.95rem; margin-bottom: 1.25rem; }\n  .nx-cta a {\n    display: inline-block;\n    background: #E11A1A;\n    color: #fff;\n    font-weight: 700;\n    font-size: 0.95rem;\n    padding: 0.7rem 1.8rem;\n    border-radius: 6px;\n    text-decoration: none;\n    transition: background 0.2s;\n  }\n  .nx-cta a:hover { background: #A30000; }\n\n  \/* \u2500\u2500 Divider \u2500\u2500 *\/\n  .nx-divider { border: none; border-top: 1px solid #e0e7f5; margin: 2rem 0; }\n\n  \/* \u2500\u2500 Disclaimer \u2500\u2500 *\/\n  .nx-disclaimer { font-size: 0.8rem; color: #888; font-style: italic; text-align: center; margin-top: 1.5rem; }\n<\/style>\n\n<div class=\"nx-blog\">\n\n  <!-- \u2500\u2500 Hero \u2500\u2500 -->\n  <div class=\"nx-hero\">\n    <span class=\"nx-tag\">Solution Guide &nbsp;\u00b7&nbsp; March 2026<\/span>\n    <p class=\"nx-meta\">How AI-powered screening helps compliance teams dramatically cut false positive rates while maintaining \u2014 and often improving \u2014 detection effectiveness.<\/p>\n  <\/div>\n\n  <!-- \u2500\u2500 Introduction \u2500\u2500 -->\n  <p>False positives represent one of the most significant operational challenges in AML compliance. When compliance teams spend the majority of their time investigating alerts that turn out to be legitimate activity, the cost \u2014 both financial and human \u2014 is substantial.<\/p>\n\n  <p>In 2026, leading organisations are achieving dramatic reductions in false positive rates through AI-powered screening approaches: reduced operational burden, improved investigator focus, and better regulatory outcomes.<\/p>\n\n  <div class=\"nx-stat-bar\">\n    <div class=\"nx-stat\">\n      <div class=\"nx-stat-num\">90\u201395%<\/div>\n      <div class=\"nx-stat-label\">of alerts in traditional systems are false positives<\/div>\n    <\/div>\n    <div class=\"nx-stat\">\n      <div class=\"nx-stat-num\">70%<\/div>\n      <div class=\"nx-stat-label\">false positive reduction achievable with AI screening<\/div>\n    <\/div>\n    <div class=\"nx-stat\">\n      <div class=\"nx-stat-num\">12\u201318mo<\/div>\n      <div class=\"nx-stat-label\">typical timeline to full reduction at scale<\/div>\n    <\/div>\n  <\/div>\n\n  <!-- \u2500\u2500 The False Positive Problem \u2500\u2500 -->\n  <h2 id=\"the-false-positive-problem\"><span class=\"ez-toc-section\" id=\"The_False_Positive_Problem\"><\/span>The False Positive Problem<span class=\"ez-toc-section-end\"><\/span><\/h2>\n\n  <h3 id=\"understanding-false-positives\">Understanding False Positives<\/h3>\n  <p>A false positive in AML screening occurs when a transaction or customer triggers an alert that, upon investigation, proves to be legitimate activity. The alert was a &#8220;positive&#8221; signal, but it was &#8220;false&#8221; \u2014 there was no actual suspicious activity. In traditional rules-based systems, these can represent 90\u201395% of all alerts generated.<\/p>\n\n  <h3 id=\"impact-of-the-challenge\">Impact of the Challenge<\/h3>\n  <p>High false positive rates create cascading problems across the compliance function:<\/p>\n\n  <div class=\"nx-grid\">\n    <div class=\"nx-card nx-card--red\">\n      <div class=\"nx-card-icon\">\n        <svg viewBox=\"0 0 20 20\"><path d=\"M10 2a8 8 0 100 16A8 8 0 0010 2zm1 11H9V9h2v4zm0-6H9V5h2v2z\"\/><\/svg>\n      <\/div>\n      <h4>Operational Burden<\/h4>\n      <p>Investigators spend their days clearing legitimate alerts rather than focusing on genuine threats<\/p>\n    <\/div>\n    <div class=\"nx-card nx-card--red\">\n      <div class=\"nx-card-icon\">\n        <svg viewBox=\"0 0 20 20\"><path d=\"M10 1a9 9 0 100 18A9 9 0 0010 1zm1 13H9v-2h2v2zm0-4H9V6h2v5z\"\/><\/svg>\n      <\/div>\n      <h4>Financial Cost<\/h4>\n      <p>Each investigation costs analyst time and overhead \u2014 high volumes translate to substantial compliance expenditure<\/p>\n    <\/div>\n    <div class=\"nx-card nx-card--red\">\n      <div class=\"nx-card-icon\">\n        <svg viewBox=\"0 0 20 20\"><path d=\"M13 6a3 3 0 11-6 0 3 3 0 016 0zm5 10a7 7 0 00-14 0h14z\"\/><\/svg>\n      <\/div>\n      <h4>Talent Impact<\/h4>\n      <p>Repetitive false positive work leads to investigator burnout and turnover, creating ongoing recruitment challenges<\/p>\n    <\/div>\n    <div class=\"nx-card nx-card--red\">\n      <div class=\"nx-card-icon\">\n        <svg viewBox=\"0 0 20 20\"><path d=\"M2 4h16v2H2zM4 8h12v2H4zM6 12h8v2H6z\"\/><\/svg>\n      <\/div>\n      <h4>Regulatory Scrutiny<\/h4>\n      <p>Regulators increasingly examine alert quality, not just quantity \u2014 high false positive rates can indicate programme weakness<\/p>\n    <\/div>\n  <\/div>\n\n  <h3 id=\"why-traditional-systems-struggle\">Why Traditional Systems Struggle<\/h3>\n  <p>Rules-based screening generates excessive false positives because fixed thresholds cannot account for customer diversity. Rules are generic rather than customer-specific, meaning normal activity for one customer is flagged for another. Meanwhile, sophisticated laundering typologies are specifically designed to avoid triggering simple rule-based alerts.<\/p>\n\n  <!-- \u2500\u2500 How AI Reduces False Positives \u2500\u2500 -->\n  <h2 id=\"how-ai-reduces-false-positives\"><span class=\"ez-toc-section\" id=\"How_AI_Reduces_False_Positives\"><\/span>How AI Reduces False Positives<span class=\"ez-toc-section-end\"><\/span><\/h2>\n\n  <h3 id=\"machine-learning-approaches\">Machine Learning Approaches<\/h3>\n  <p>AI-powered screening applies machine learning to understand normal customer behaviour and distinguish it from genuinely suspicious activity. Rather than applying fixed thresholds, AI evaluates each customer and transaction in context:<\/p>\n\n  <div class=\"nx-tech-grid\">\n    <div class=\"nx-tech-card\">\n      <h4>Behavioural Baselines<\/h4>\n      <p>AI establishes individual baselines for each customer based on their transaction history, industry, and relationship type. Activity within a customer&#8217;s normal pattern is far less likely to generate an alert.<\/p>\n    <\/div>\n    <div class=\"nx-tech-card\">\n      <h4>Contextual Analysis<\/h4>\n      <p>Transactions are evaluated not in isolation but within the full context of the customer&#8217;s relationship and history, significantly improving accuracy.<\/p>\n    <\/div>\n    <div class=\"nx-tech-card\">\n      <h4>Adaptive Learning<\/h4>\n      <p>AI continuously learns from investigation outcomes, improving its ability to distinguish true positives from false positives over time.<\/p>\n    <\/div>\n  <\/div>\n\n  <h3 id=\"specific-techniques\">Specific Techniques<\/h3>\n  <ul>\n    <li><strong>Customer segmentation:<\/strong> Applying appropriate monitoring thresholds per segment rather than one-size-fits-all rules<\/li>\n    <li><strong>Counterparty analysis:<\/strong> Understanding counterparty relationships to distinguish normal business from suspicious activity<\/li>\n    <li><strong>Temporal pattern recognition:<\/strong> Recognising seasonal variations, payroll cycles, and billing patterns to reduce alerts on expected activity<\/li>\n    <li><strong>Velocity analysis:<\/strong> Understanding normal transaction velocity per customer, reducing alerts on typical frequencies<\/li>\n  <\/ul>\n\n  <!-- \u2500\u2500 Achieving 70% Reduction \u2500\u2500 -->\n  <h2 id=\"achieving-70-percent-reduction\"><span class=\"ez-toc-section\" id=\"Achieving_70_Reduction\"><\/span>Achieving 70% Reduction<span class=\"ez-toc-section-end\"><\/span><\/h2>\n\n  <h3 id=\"what-70-percent-looks-like\">What 70% Reduction Looks Like in Practice<\/h3>\n  <p>A 70% reduction transforms the daily reality of the compliance function. For every 100 alerts generated previously, 70 are eliminated \u2014 leaving 30 high-quality alerts that genuinely warrant investigation.<\/p>\n\n  <div class=\"nx-compare\">\n    <div class=\"nx-compare-card before\">\n      <div class=\"nx-compare-label\">Before AI screening<\/div>\n      <ul>\n        <li>100 alerts per day<\/li>\n        <li>90\u201395 are false positives<\/li>\n        <li>Investigators overwhelmed<\/li>\n        <li>Genuine threats deprioritised<\/li>\n        <li>High compliance cost<\/li>\n      <\/ul>\n    <\/div>\n    <div class=\"nx-compare-card after\">\n      <div class=\"nx-compare-label\">After AI screening<\/div>\n      <ul>\n        <li>30 alerts per day<\/li>\n        <li>Higher proportion of true positives<\/li>\n        <li>Investigators focused on real risk<\/li>\n        <li>Better SAR quality and outcomes<\/li>\n        <li>Reduced compliance cost<\/li>\n      <\/ul>\n    <\/div>\n  <\/div>\n\n  <div class=\"nx-callout nx-callout--warning\">\n    <div class=\"nx-callout-title\">\u26a0 Detection effectiveness must be maintained<\/div>\n    <p>False positive reduction must never come at the cost of missing genuine threats. Effective implementations monitor detection rates alongside false positive rates and conduct regular back-testing against known laundering scenarios.<\/p>\n  <\/div>\n\n  <!-- \u2500\u2500 Implementation \u2500\u2500 -->\n  <h2 id=\"implementation-approaches\"><span class=\"ez-toc-section\" id=\"Implementation_Approaches\"><\/span>Implementation Approaches<span class=\"ez-toc-section-end\"><\/span><\/h2>\n\n  <h3 id=\"phased-deployment\">Phased Deployment<\/h3>\n  <p>Most organisations implement AI false positive reduction in structured phases to manage risk and build confidence:<\/p>\n\n  <div class=\"nx-steps\">\n    <div class=\"nx-step\">\n      <div class=\"nx-step-num\">1<\/div>\n      <div class=\"nx-step-body\">\n        <h4>Pilot<\/h4>\n        <p>Select a specific product line, customer segment, or alert type for initial implementation and baseline measurement<\/p>\n      <\/div>\n    <\/div>\n    <div class=\"nx-step\">\n      <div class=\"nx-step-num\">2<\/div>\n      <div class=\"nx-step-body\">\n        <h4>Expansion<\/h4>\n        <p>Extend coverage to additional segments based on pilot learnings and validated performance<\/p>\n      <\/div>\n    <\/div>\n    <div class=\"nx-step\">\n      <div class=\"nx-step-num\">3<\/div>\n      <div class=\"nx-step-body\">\n        <h4>Optimisation<\/h4>\n        <p>Fine-tune models based on production performance, expand coverage, and embed into investigator workflows<\/p>\n      <\/div>\n    <\/div>\n  <\/div>\n\n  <h3 id=\"change-management\">Change Management<\/h3>\n  <p>Introducing AI screening requires active change management \u2014 training investigators on AI-assisted workflows, communicating expected outcomes to stakeholders, managing expectations during transition periods, and building confidence in AI decision quality over time.<\/p>\n\n  <div class=\"nx-callout\">\n    <div class=\"nx-callout-title\">\ud83d\udca1 Integration considerations<\/div>\n    <p>AI screening must connect with existing case management systems, SAR filing processes, reporting dashboards, and investigator workflows to deliver its full value \u2014 not operate as a standalone tool.<\/p>\n  <\/div>\n\n  <!-- \u2500\u2500 Measuring Success \u2500\u2500 -->\n  <h2 id=\"measuring-success\"><span class=\"ez-toc-section\" id=\"Measuring_Success\"><\/span>Measuring Success<span class=\"ez-toc-section-end\"><\/span><\/h2>\n\n  <div class=\"nx-table-wrap\">\n    <table class=\"nx-table\">\n      <thead>\n        <tr>\n          <th>Metric<\/th>\n          <th>Definition<\/th>\n          <th>Expected Direction<\/th>\n        <\/tr>\n      <\/thead>\n      <tbody>\n        <tr>\n          <td><strong>False Positive Rate<\/strong><\/td>\n          <td>Percentage of alerts that prove to be false positives<\/td>\n          <td><span class=\"nx-badge nx-badge--reduce\">Reduce<\/span><\/td>\n        <\/tr>\n        <tr>\n          <td><strong>True Positive Rate<\/strong><\/td>\n          <td>Percentage of genuine suspicious activity detected<\/td>\n          <td><span class=\"nx-badge nx-badge--stable\">Maintain or improve<\/span><\/td>\n        <\/tr>\n        <tr>\n          <td><strong>Alert Volume<\/strong><\/td>\n          <td>Total alerts requiring investigation<\/td>\n          <td><span class=\"nx-badge nx-badge--reduce\">Reduce<\/span><\/td>\n        <\/tr>\n        <tr>\n          <td><strong>Investigation Time<\/strong><\/td>\n          <td>Average time per alert investigation<\/td>\n          <td><span class=\"nx-badge nx-badge--stable\">May rise initially as quality improves<\/span><\/td>\n        <\/tr>\n        <tr>\n          <td><strong>SAR Quality<\/strong><\/td>\n          <td>Quality of Suspicious Activity Reports filed<\/td>\n          <td><span class=\"nx-badge nx-badge--improve\">Improve<\/span><\/td>\n        <\/tr>\n      <\/tbody>\n    <\/table>\n  <\/div>\n\n  <p>AI model performance requires ongoing monitoring including regular performance validation, drift detection, comparison against actual outcomes, and alignment with regulatory reporting requirements.<\/p>\n\n  <hr class=\"nx-divider\">\n\n  <!-- \u2500\u2500 FAQ \u2500\u2500 -->\n  <h2 id=\"frequently-asked-questions\"><span class=\"ez-toc-section\" id=\"Frequently_Asked_Questions\"><\/span>Frequently Asked Questions<span class=\"ez-toc-section-end\"><\/span><\/h2>\n\n  <div class=\"nx-faq\">\n    <div class=\"nx-faq-item\">\n      <button class=\"nx-faq-q\" aria-expanded=\"false\">\n        How long does it take to see false positive reductions?\n        <span class=\"nx-chevron\"><svg viewBox=\"0 0 10 6\" stroke-width=\"1.5\" stroke-linecap=\"round\" stroke-linejoin=\"round\"><path d=\"M1 1l4 4 4-4\"\/><\/svg><\/span>\n      <\/button>\n      <div class=\"nx-faq-a\">\n        Initial improvements often appear within the first few months of deployment as AI models begin processing live transactions. More substantial reductions typically materialise over 6\u201312 months as models mature and tuning optimises performance. Significant reductions of 50\u201370% are typically achievable within 12\u201318 months of full deployment.\n      <\/div>\n    <\/div>\n    <div class=\"nx-faq-item\">\n      <button class=\"nx-faq-q\" aria-expanded=\"false\">\n        Will AI screening catch everything our rules do?\n        <span class=\"nx-chevron\"><svg viewBox=\"0 0 10 6\" stroke-width=\"1.5\" stroke-linecap=\"round\" stroke-linejoin=\"round\"><path d=\"M1 1l4 4 4-4\"\/><\/svg><\/span>\n      <\/button>\n      <div class=\"nx-faq-a\">\n        AI and rules serve complementary purposes. AI typically catches complex patterns and reduces false positives more effectively, whilst rules ensure coverage of specific regulatory requirements and known-bad scenarios. Most organisations maintain a hybrid approach combining AI-powered screening with targeted rules for comprehensive coverage.\n      <\/div>\n    <\/div>\n    <div class=\"nx-faq-item\">\n      <button class=\"nx-faq-q\" aria-expanded=\"false\">\n        What if AI makes a mistake and misses something?\n        <span class=\"nx-chevron\"><svg viewBox=\"0 0 10 6\" stroke-width=\"1.5\" stroke-linecap=\"round\" stroke-linejoin=\"round\"><path d=\"M1 1l4 4 4-4\"\/><\/svg><\/span>\n      <\/button>\n      <div class=\"nx-faq-a\">\n        AI screening, like any system, is not perfect. Effective implementations include human oversight, regular model validation, and monitoring of detection rates. The goal is improving overall effectiveness \u2014 not achieving perfection, but better outcomes than legacy approaches. Regular testing against known laundering scenarios helps validate detection capability.\n      <\/div>\n    <\/div>\n    <div class=\"nx-faq-item\">\n      <button class=\"nx-faq-q\" aria-expanded=\"false\">\n        How do we explain AI decisions to regulators?\n        <span class=\"nx-chevron\"><svg viewBox=\"0 0 10 6\" stroke-width=\"1.5\" stroke-linecap=\"round\" stroke-linejoin=\"round\"><path d=\"M1 1l4 4 4-4\"\/><\/svg><\/span>\n      <\/button>\n      <div class=\"nx-faq-a\">\n        Modern AI screening systems include explainability capabilities that provide clear reasoning for alerts. Investigators can see why a transaction was flagged \u2014 the relevant factors and risk indicators. This transparency supports regulatory examination and accountability requirements, and is a key consideration when selecting an AI screening solution.\n      <\/div>\n    <\/div>\n  <\/div>\n\n  <!-- \u2500\u2500 CTA \u2500\u2500 -->\n  <div class=\"nx-cta\">\n    <h3>Ready to reduce your false positive burden?<\/h3>\n    <p>Find out how Nexiant can support your AML screening with AI-powered solutions.<\/p>\n    <a href=\"\/contact-us\">Get in touch with our team<\/a>\n  <\/div>\n\n  <p class=\"nx-disclaimer\">This article is for informational purposes only and does not constitute legal or compliance advice. Organisations should consult with qualified legal professionals for guidance specific to their circumstances.<\/p>\n\n<\/div>\n\n<script>\n  document.querySelectorAll('.nx-faq-q').forEach(function(btn) {\n    btn.addEventListener('click', function() {\n      var expanded = this.getAttribute('aria-expanded') === 'true';\n      var answer = this.nextElementSibling;\n      this.setAttribute('aria-expanded', !expanded);\n      answer.classList.toggle('open', !expanded);\n    });\n  });\n<\/script>\n\n\n\n<p><\/p>\n","protected":false},"excerpt":{"rendered":"<p>How AI-powered screening helps compliance teams dramatically cut false positive rates while maintaining \u2014 and often improving \u2014 detection effectiveness.<\/p>\n","protected":false},"author":2,"featured_media":402,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"om_disable_all_campaigns":false,"_monsterinsights_skip_tracking":false,"_monsterinsights_sitenote_active":false,"_monsterinsights_sitenote_note":"","_monsterinsights_sitenote_category":0,"footnotes":""},"categories":[14,44,15],"tags":[],"class_list":["post-399","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-aml-2","category-artificial-intelligence-ai","category-risk-management"],"blocksy_meta":[],"aioseo_notices":[],"_links":{"self":[{"href":"https:\/\/nexiant.ai\/resources\/blogs\/wp-json\/wp\/v2\/posts\/399","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/nexiant.ai\/resources\/blogs\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/nexiant.ai\/resources\/blogs\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/nexiant.ai\/resources\/blogs\/wp-json\/wp\/v2\/users\/2"}],"replies":[{"embeddable":true,"href":"https:\/\/nexiant.ai\/resources\/blogs\/wp-json\/wp\/v2\/comments?post=399"}],"version-history":[{"count":3,"href":"https:\/\/nexiant.ai\/resources\/blogs\/wp-json\/wp\/v2\/posts\/399\/revisions"}],"predecessor-version":[{"id":403,"href":"https:\/\/nexiant.ai\/resources\/blogs\/wp-json\/wp\/v2\/posts\/399\/revisions\/403"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/nexiant.ai\/resources\/blogs\/wp-json\/wp\/v2\/media\/402"}],"wp:attachment":[{"href":"https:\/\/nexiant.ai\/resources\/blogs\/wp-json\/wp\/v2\/media?parent=399"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/nexiant.ai\/resources\/blogs\/wp-json\/wp\/v2\/categories?post=399"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/nexiant.ai\/resources\/blogs\/wp-json\/wp\/v2\/tags?post=399"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}