{"id":6995,"date":"2026-04-24T11:06:51","date_gmt":"2026-04-24T11:06:51","guid":{"rendered":"https:\/\/nextagile.ai\/blogs\/?p=6995"},"modified":"2026-04-27T05:17:18","modified_gmt":"2026-04-27T05:17:18","slug":"ai-in-fintech","status":"publish","type":"post","link":"https:\/\/nextagile.ai\/blogs\/gen-ai\/ai-in-fintech\/","title":{"rendered":"AI in Fintech: KYC, AML &#038; Compliance Automation (2026 Guide)"},"content":{"rendered":"<h2>Key Takeaways of AI in Fintech<\/h2>\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"1\">AI in fintech is now essential for scaling KYC, AML, and compliance efficiently<\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\">Machine learning reduces AML false positives by up to 50%+<\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\">Generative AI accelerates regulatory reporting and AML investigations<\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\">Explainable AI is mandatory for RBI-aligned compliance frameworks<\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\">Agentic AI enables continuous, real-time compliance monitoring<\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\">Agile delivery is critical for adapting to regulatory changes in India<\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\">Fintechs that delay AI adoption risk higher fraud losses and slower growth<\/li>\n<\/ul>\n<h2>Introduction<\/h2>\n<p>In 2026, fintech companies that haven\u2019t embedded AI into compliance workflows are already falling behind on onboarding speed, fraud detection, and regulatory responsiveness.<\/p>\n<p>You\u2019ve probably seen this firsthand.<\/p>\n<p>Customers expect instant onboarding. Regulators expect airtight compliance. And fraudsters? They\u2019re getting smarter by the day.<br \/>\nTraditional compliance systems simply weren\u2019t built for this scale.<\/p>\n<p>Here\u2019s what\u2019s changed: AI in fintech is no longer just about automation; it\u2019s about decision intelligence at scale. The ability to interpret regulations, detect risk patterns, and act in real time.<\/p>\n<p>And <a href=\"https:\/\/nextagile.ai\/generative-ai-consulting-services\/\"><b>generative AI consulting services<\/b><\/a> are accelerating that shift even further.<\/p>\n<p>But (and this is where most companies struggle), adopting AI doesn\u2019t guarantee results. Poor implementation, lack of governance, and siloed teams often derail even well-funded initiatives.<\/p>\n<p>In our experience at NextAgile, the difference between success and failure isn\u2019t the model; it\u2019s how you design, deliver, and govern AI systems.<\/p>\n<p>Let\u2019s break down what actually works.<\/p>\n<h2>Why AI and Fintech Are a High-Stakes Combination<\/h2>\n<h3>The Scale of the Problem: Why Traditional Compliance Methods Are Failing Indian Fintech<\/h3>\n<p>Compliance today isn\u2019t just complex; it\u2019s overloaded.<br \/>\nThink about the numbers:<\/p>\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"1\">Millions of transactions daily<\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\">Real-time payment systems<\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\">Increasing fraud sophistication<\/li>\n<\/ul>\n<p>Manual reviews and rule-based systems simply can\u2019t keep up.<br \/>\nWorse, they create hidden inefficiencies:<\/p>\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"1\">AML teams spend up to 70% of time on false positives<\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\">KYC delays lead to 20-30% customer drop-offs<\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\">Compliance backlogs increase regulatory risk<\/li>\n<\/ul>\n<p>This is where AI in fintech changes the game. Instead of static rules, machine learning models continuously learn from transaction patterns, detect anomalies, and prioritize real risks.<br \/>\nOne mid-sized Indian fintech we worked with at NextAgile reduced AML false positives by 52% in under 4 months, cutting the investigation workload nearly in half. That\u2019s not incremental improvement. That\u2019s operational transformation.<\/p>\n<h3>India&#8217;s Regulatory Landscape in 2026: RBI, SEBI, DPDP Act, and What They Mean for AI Deployment<\/h3>\n<p>Regulation in India is evolving fast, and it\u2019s getting stricter.<br \/>\nYou\u2019re dealing with:<\/p>\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"1\">RBI guidelines on digital lending and model governance<\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\">SEBI\u2019s push for advanced market surveillance<\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\">The DPDP Act enforcing strict data privacy and consent<\/li>\n<\/ul>\n<p>Here\u2019s the critical shift: AI increases accountability, not reduces it.<br \/>\nRegulators now expect:<\/p>\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"1\">Explainable decision-making<\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\">Model auditability<\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\">Transparent data usage<\/li>\n<\/ul>\n<p>This makes AI model risk management in banking a top priority.<br \/>\nAt NextAgile, we\u2019ve seen that fintechs that bake compliance into AI architecture early move faster and avoid costly rework later.<\/p>\n<h2>Core AI Use Cases in Fintech Compliance and Regulatory Operations<\/h2>\n<p><img loading=\"lazy\" decoding=\"async\" src=\"https:\/\/nextagile.ai\/blogs\/wp-content\/uploads\/2026\/04\/Core-AI-Use-Cases-in-Fintech-Compliance-and-Regulatory-Operations.png\" alt=\"Core AI Use Cases in Fintech Compliance and Regulatory Operations\" width=\"1200\" height=\"800\" title=\"\"><\/p>\n<h3>1. AI-Powered KYC Automation: Faster Onboarding, Fewer Manual Errors<\/h3>\n<p>KYC is often the biggest friction point.<br \/>\nAI transforms onboarding by:<\/p>\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"1\">Automating document verification<\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\">Using facial recognition for identity matching<\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\">Detecting tampered or synthetic identities<\/li>\n<\/ul>\n<p>Result? Onboarding in minutes and not in days.<br \/>\nMore importantly, it reduces drop-offs and ensures consistency.<\/p>\n<h3>2. AML Transaction Monitoring: Reducing False Positives with Machine Learning<\/h3>\n<p>Traditional AML systems generate noise.<br \/>\nMachine learning reduces it by:<\/p>\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"1\">Learning behavioral patterns<\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\">Identifying real anomalies<\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\">Prioritizing high-risk alerts<\/li>\n<\/ul>\n<p>In practice, this means fewer wasted hours and better risk coverage.<\/p>\n<h3>3. Regulatory Reporting Automation with NLP and Generative AI<\/h3>\n<p>Compliance reporting is repetitive and error-prone.<br \/>\nGenerative AI solves this by:<\/p>\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"1\">Auto-generating reports from structured data<\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\">Standardizing regulatory language<\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\">Tracking policy changes dynamically<\/li>\n<\/ul>\n<p>This is one of the fastest ROI areas for gen AI in fintech.<\/p>\n<h3>4. Credit Risk Scoring: Beyond CIBIL<\/h3>\n<p>Traditional models exclude too many users.<br \/>\nAI enables machine learning credit scoring using:<\/p>\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"1\">Transaction behavior<\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\">Alternative data<\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\">Digital footprints<\/li>\n<\/ul>\n<p>This unlocks lending for thin-file customers, without increasing risk.<\/p>\n<h3>5. Fraud Detection and Market Surveillance<\/h3>\n<p>Fraud patterns are now adaptive, using mule accounts, synthetic identities, and cross-platform laundering that rule-based systems simply can\u2019t catch.<br \/>\nAI detects:<\/p>\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"1\">Real-time anomalies<\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\">Suspicious trading patterns<\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\">Insider signals<\/li>\n<\/ul>\n<p>This is where AI fraud detection in fintech becomes mission-critical.<\/p>\n<h3>6. Explainable AI (XAI) for Audit and Regulatory Accountability<\/h3>\n<p>Black-box AI doesn\u2019t work in finance.<br \/>\nExplainable AI ensures:<\/p>\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"1\">Decisions are traceable<\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\">Models are auditable<\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\">Bias is detectable<\/li>\n<\/ul>\n<p>In 2026, this isn\u2019t optional; it\u2019s regulatory baseline.<\/p>\n<h2>Generative AI in Fintech Compliance: What Actually Changes in 2026<\/h2>\n<p>This is where many blogs stay abstract. Let\u2019s make it real.<\/p>\n<h3>Before vs After Generative AI<\/h3>\n<p><b>Before Gen AI:<\/b><\/p>\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"1\">AML investigator spends 45 minutes reviewing one alert<\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\">Regulatory updates require manual legal interpretation<\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\">Reports take days to compile<\/li>\n<\/ul>\n<p><b>After Gen AI:<\/b><\/p>\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"1\">Alerts summarized in seconds with risk context<\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\">Policy changes interpreted instantly<\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\">Reports generated automatically<\/li>\n<\/ul>\n<p>That\u2019s a 10x productivity shift.<\/p>\n<h3>LLMs for Regulatory Policy Interpretation<\/h3>\n<p>LLMs can:<\/p>\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"1\">Parse complex regulatory documents<\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\">Highlight changes<\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\">Suggest implementation steps<\/li>\n<\/ul>\n<p>This reduces dependency on manual legal reviews.<\/p>\n<h3>Agentic AI for Continuous Compliance Monitoring<\/h3>\n<p>Agentic AI acts autonomously, which is why many fintech teams begin with an <a href=\"https:\/\/nextagile.ai\/workshop\/agentic-ai-workshop\/\"><b>Agentic AI Workshop<\/b><\/a> before scaling enterprise use cases:<\/p>\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"1\">Monitors transactions<\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\">Flags anomalies<\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\">Triggers workflows<\/li>\n<\/ul>\n<p>It\u2019s like a 24\/7 compliance engine.<\/p>\n<h3>Gen AI for AML Investigation Support<\/h3>\n<p>AI assists investigators by:<\/p>\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"1\">Summarizing alerts<\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\">Providing context<\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\">Recommending next actions<\/li>\n<\/ul>\n<p>This shifts focus from data gathering \u2192 decision-making.<\/p>\n<h2>The Delivery Challenge: Why AI in Fintech Compliance Fails Without Agile Teams<\/h2>\n<p>Here\u2019s the uncomfortable truth: most AI compliance projects don\u2019t fail because of technology. They fail because of delivery, which is why many fintech firms invest in <a href=\"https:\/\/nextagile.ai\/agile-consulting-services\/\"><b>agile consulting services<\/b><\/a> to improve execution speed.<\/p>\n<h3>Compliance Is Not a One-Time Milestone<\/h3>\n<p>Regulations change constantly.<br \/>\nTreating compliance as a one-time project leads to:<\/p>\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"1\">Outdated systems<\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\">Increased risk<\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\">Slow response times<\/li>\n<\/ul>\n<h3>How Agile Reduces Time-to-Compliance<\/h3>\n<p>Agile enables:<\/p>\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"1\">Continuous updates<\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\">Faster iterations<\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\">Better alignment<\/li>\n<\/ul>\n<p>Many fintech leaders are now combining compliance systems with <a href=\"https:\/\/nextagile.ai\/blogs\/agile\/ai-and-agile-methodology\/\"><b>AI and agile methodology<\/b><\/a> to improve decision speed.<br \/>\nFor example, regulatory changes that take 3-6 months to implement in traditional setups can be reduced to weeks.<\/p>\n<h3>Building Cross-Functional AI Teams<\/h3>\n<p>Successful fintech AI teams often emerge faster with structured <a href=\"https:\/\/nextagile.ai\/corporate-leadership-training\/\"><b>leadership training programs<\/b><\/a> and cross-functional enablement. Successful fintech AI teams include:<\/p>\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"1\">Compliance experts<\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\">Data scientists<\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\">Engineers<\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\">Risk analysts<\/li>\n<\/ul>\n<p>At NextAgile, we treat AI compliance as a continuous delivery system, embedding regulatory updates into sprint cycles and not static releases.<\/p>\n<h2>AI Compliance Framework for Fintech (Practical Model)<\/h2>\n<p><img loading=\"lazy\" decoding=\"async\" src=\"https:\/\/nextagile.ai\/blogs\/wp-content\/uploads\/2026\/04\/AI-Compliance-Framework-for-Fintech-Practical-Model.png\" alt=\"AI Compliance Framework for Fintech (Practical Model)\" width=\"1200\" height=\"800\" title=\"\"><br \/>\nTo simplify implementation, think in layers:<\/p>\n<ol>\n<li><b>Data Layer<\/b> &#8211; Customer, transaction, and behavioral data<\/li>\n<li><b>Model Layer<\/b> &#8211; ML models for KYC, AML, fraud detection<\/li>\n<li><b>Explainability Layer<\/b> &#8211; XAI tools for transparency<\/li>\n<li><b>Governance Layer<\/b> &#8211; Audit, compliance, and monitoring<\/li>\n<\/ol>\n<p>This layered approach ensures scalability and regulatory alignment, especially for firms preparing for enterprise growth through <a href=\"https:\/\/nextagile.ai\/blogs\/agile\/ai-agile-at-scale-beyond-safe\/\"><b>AI + Agile at Scale<\/b><\/a> models.<\/p>\n<h2>Key Risks and Governance Considerations<\/h2>\n<h3>Model Risk Management<\/h3>\n<p>AI models must be:<\/p>\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"1\">Validated<\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\">Monitored<\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\">Documented<\/li>\n<\/ul>\n<h3>Data Privacy and DPDP Act<\/h3>\n<p>Ensure:<\/p>\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"1\">Consent-driven data usage<\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\">Transparency<\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\">Security<\/li>\n<\/ul>\n<h3>Algorithmic Bias in Credit Scoring<\/h3>\n<p>Bias can lead to:<\/p>\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"1\">Regulatory penalties<\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\">Customer distrust<\/li>\n<\/ul>\n<p>Fairness must be built into models.<\/p>\n<h3>Third-Party AI Vendor Risk<\/h3>\n<p>Using external tools? You\u2019re still accountable.<\/p>\n<h2>The Cost of Inaction: Why Delaying AI Is Risky<\/h2>\n<p>This is where many fintech leaders underestimate the impact.<br \/>\nDelaying AI adoption leads to:<\/p>\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"1\">20-30% higher customer drop-offs due to slow KYC<\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\">Increased fraud exposure<\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\">Higher operational costs<\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\">Slower regulatory response<\/li>\n<\/ul>\n<p>In a competitive market, that\u2019s not just inefficiency; it\u2019s lost revenue.<\/p>\n<h2>How Indian Fintech Leaders Should Build AI + Compliance Capability<\/h2>\n<h3>Step 1 &#8211; Assess Your Compliance Baseline<\/h3>\n<p>Map:<\/p>\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"1\">KYC turnaround time<\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\">AML false positive rates<\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\">Audit backlog<\/li>\n<\/ul>\n<p>Without this, you can\u2019t measure impact. Many organizations use <a href=\"https:\/\/nextagile.ai\/okr-consulting-services\/\"><b>OKR consulting services<\/b><\/a> to track AI compliance outcomes more effectively.<\/p>\n<h3>Step 2 &#8211; Prioritize High-ROI Use Cases<\/h3>\n<p>Start with:<\/p>\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"1\">KYC automation<\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\">Fraud detection<\/li>\n<\/ul>\n<h3>Step 3 &#8211; Design for Explainability<\/h3>\n<p>Don\u2019t retrofit compliance. Build it in.<\/p>\n<h3>Step 4 &#8211; Adopt Agile Governance<\/h3>\n<p>Embed compliance into delivery cycles. At NextAgile, this approach consistently reduces time-to-compliance while improving system resilience.<\/p>\n<h2><\/h2>\n<h2>Conclusion<\/h2>\n<p>AI in fintech isn\u2019t just transforming compliance; it\u2019s redefining it. From KYC automation to generative AI-driven reporting, intelligent systems are becoming the backbone of financial operations.<\/p>\n<p>But success depends on execution. The fintech leaders who win in 2026 won\u2019t just adopt AI, they\u2019ll implement it with governance, explainability, and Agile delivery at the core.<\/p>\n<p>Because the real question isn\u2019t whether AI matters.<\/p>\n<p>It\u2019s whether your organization is ready to use it effectively. If your fintech teams are struggling with compliance bottlenecks, rising fraud risks, or slow regulatory response cycles, a structured AI-driven compliance approach becomes essential.<\/p>\n<p>At NextAgile <a href=\"https:\/\/nextagile.ai\/generative-ai-consulting-services\/\">AI Consulting services<\/a>, we help enterprises redesign their operating models to embed AI into decision systems, ensuring organizations move beyond delivery efficiency to decision intelligence at scale. You can reach us at <a href=\"mailto:consult@nextagile.ai\">consult@nextagile.ai<\/a> to explore more.<\/p>\n<h2 style=\"color: #000;\">Frequently Asked Questions<\/h2>\n<h3>Q1: How is AI used in fintech compliance in India?<\/h3>\n<p>AI is used for KYC automation, AML monitoring, fraud detection, and regulatory reporting.<\/p>\n<h3>Q2: What are RBI guidelines on AI?<\/h3>\n<p>They focus on explainability, governance, and model risk management.<\/p>\n<h3>Q3: AI vs generative AI in fintech?<\/h3>\n<p>AI analyzes data; generative AI creates reports, summaries, and insights.<\/p>\n<h3>Q4: How to stay compliant with DPDP Act?<\/h3>\n<p>Ensure consent-based, transparent, and secure data usage.<\/p>\n<h3>Q5: Why do AI projects fail in fintech?<\/h3>\n<p>Poor delivery models and lack of Agile execution are the main reasons.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Key Takeaways of AI in Fintech AI in fintech is now essential for scaling KYC, AML, and compliance efficiently Machine learning reduces AML false positives by up to 50%+ Generative AI accelerates regulatory reporting and AML investigations Explainable AI is mandatory for RBI-aligned compliance frameworks Agentic AI enables continuous, real-time compliance monitoring Agile delivery is&#8230;<\/p>\n","protected":false},"author":2,"featured_media":6996,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"content-type":"","footnotes":""},"categories":[145],"tags":[],"class_list":["post-6995","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-gen-ai"],"_links":{"self":[{"href":"https:\/\/nextagile.ai\/blogs\/wp-json\/wp\/v2\/posts\/6995","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/nextagile.ai\/blogs\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/nextagile.ai\/blogs\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/nextagile.ai\/blogs\/wp-json\/wp\/v2\/users\/2"}],"replies":[{"embeddable":true,"href":"https:\/\/nextagile.ai\/blogs\/wp-json\/wp\/v2\/comments?post=6995"}],"version-history":[{"count":8,"href":"https:\/\/nextagile.ai\/blogs\/wp-json\/wp\/v2\/posts\/6995\/revisions"}],"predecessor-version":[{"id":7066,"href":"https:\/\/nextagile.ai\/blogs\/wp-json\/wp\/v2\/posts\/6995\/revisions\/7066"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/nextagile.ai\/blogs\/wp-json\/wp\/v2\/media\/6996"}],"wp:attachment":[{"href":"https:\/\/nextagile.ai\/blogs\/wp-json\/wp\/v2\/media?parent=6995"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/nextagile.ai\/blogs\/wp-json\/wp\/v2\/categories?post=6995"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/nextagile.ai\/blogs\/wp-json\/wp\/v2\/tags?post=6995"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}