{"id":927,"date":"2026-10-26T15:14:00","date_gmt":"2026-10-26T04:14:00","guid":{"rendered":"https:\/\/nexiant.ai\/resources\/blogs\/?p=927"},"modified":"2026-11-06T14:58:36","modified_gmt":"2026-11-06T03:58:36","slug":"ai-complements-transaction-monitoring-rules","status":"publish","type":"post","link":"https:\/\/nexiant.ai\/resources\/blogs\/ai-complements-transaction-monitoring-rules\/","title":{"rendered":"AI Shouldn&#8217;t Replace the Rules. It Should Help Us See What They Miss"},"content":{"rendered":"\n<style>\n  .nx-blog * { box-sizing: border-box; margin: 0; padding: 0; }\n  .nx-blog { font-family: -apple-system, BlinkMacSystemFont, 'Segoe UI', Roboto, sans-serif; font-size: 16px; line-height: 1.75; color: #1a1a2e; max-width: 820px; margin: 0 auto; }\n  .nx-blog h2 { font-size: 1.55rem; font-weight: 700; color: #00184C; margin: 2.5rem 0 0.75rem; padding-bottom: 0.4rem; border-bottom: 3px solid #073EA1; }\n  .nx-blog h3 { font-size: 1.15rem; font-weight: 700; color: #073EA1; margin: 1.75rem 0 0.5rem; }\n  .nx-blog h4 { font-size: 0.98rem; font-weight: 700; color: #00184C; margin: 1.25rem 0 0.35rem; }\n  .nx-blog p { margin-bottom: 1rem; }\n  .nx-blog ul, .nx-blog ol { margin: 0.5rem 0 1rem 1.4rem; }\n  .nx-blog li { margin-bottom: 0.4rem; }\n  .nx-blog strong { color: #00184C; }\n\n  .nx-hero { background: linear-gradient(135deg, #00184C 0%, #073EA1 100%); color: #fff; border-radius: 12px; padding: 1.75rem 2rem; margin-bottom: 2rem; 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justify-content: center; transition: transform 0.25s; }\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 { display: none; padding: 0 1.25rem 1rem; font-size: 0.92rem; color: #333; line-height: 1.7; background: #fff; }\n  .nx-faq-a.open { display: block; }\n\n  .nx-cta { background: linear-gradient(135deg, #00184C 0%, #073EA1 100%); border-radius: 12px; padding: 2rem; text-align: center; margin-top: 2.5rem; }\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 { display: inline-block; background: #E11A1A; color: #fff; font-weight: 700; font-size: 0.95rem; padding: 0.7rem 1.8rem; border-radius: 6px; text-decoration: none; transition: background 0.2s; }\n  .nx-cta a:hover { background: #A30000; }\n  .nx-divider { border: none; border-top: 1px solid #e0e7f5; margin: 2rem 0; }\n  .nx-disclaimer { font-size: 0.8rem; color: #888; font-style: italic; text-align: center; margin-top: 1.5rem; }\n\n  @media (max-width: 720px) {\n    .nx-flow { grid-template-columns: 1fr; }\n    .nx-flow-step::after { content: \"\u2193\"; right: 50%; top: auto; bottom: -18px; transform: translateX(50%); }\n    .nx-stack-layer { flex-direction: column; }\n  }\n<\/style>\n<div class=\"nx-blog\">\n  <div class=\"nx-hero\">\n    <span class=\"nx-tag\">Transaction Monitoring &nbsp;\u00b7&nbsp; November 2026 &nbsp;\u00b7&nbsp; Global<\/span>\n    <p class=\"nx-meta\">Why monitoring rules and separate AI scores can work together without surrendering investigative control.<\/p>\n  <\/div>\n  <p>Financial crime teams have spent years building monitoring rules around known risk scenarios, internal policies and the activity they need to investigate. It would be strange to assume that a new AI model makes all of that knowledge irrelevant overnight.<\/p>\n  <p>The more interesting question is how the two approaches can work together. Rules provide clarity about defined conditions; AI can potentially offer an additional perspective on activity that does not fit neatly into a triggered\/not-triggered outcome.<\/p>\n  <!-- TOP VISUAL: encoded SVG so this image has no external file dependency -->\n  <div class=\"nx-media\">\n    <img decoding=\"async\" src=\"data:image\/svg+xml,%3Csvg xmlns='http:\/\/www.w3.org\/2000\/svg' width='820' height='460' viewBox='0 0 820 460'%3E%3Cdefs%3E%3ClinearGradient id='bg' x1='0' y1='0' x2='1' y2='1'%3E%3Cstop offset='0' stop-color='%2300184C'\/%3E%3Cstop offset='1' stop-color='%23073EA1'\/%3E%3C\/linearGradient%3E%3C\/defs%3E%3Crect width='820' height='460' rx='22' fill='url(%23bg)'\/%3E%3Ctext x='410' y='70' text-anchor='middle' font-family='Arial, sans-serif' font-size='30' font-weight='700' fill='%23ffffff'%3ERules%20and%20AI%20Together%3C\/text%3E%3Crect x='86' y='132' width='165' height='92' rx='18' fill='%23ffffff' opacity='0.92'\/%3E%3Crect x='328' y='132' width='165' height='92' rx='18' fill='%23ffffff' opacity='0.88'\/%3E%3Crect x='570' y='132' width='165' height='92' rx='18' fill='%23ffffff' opacity='0.84'\/%3E%3Crect x='207' y='272' width='165' height='92' rx='18' fill='%23ffffff' opacity='0.86'\/%3E%3Crect x='449' y='272' width='165' height='92' rx='18' fill='%23ffffff' opacity='0.90'\/%3E%3Ccircle cx='168' cy='162' r='16' fill='%23073EA1'\/%3E%3Ccircle cx='410' cy='162' r='16' fill='%23073EA1'\/%3E%3Ccircle cx='652' cy='162' r='16' fill='%23073EA1'\/%3E%3Ccircle cx='290' cy='302' r='16' fill='%23073EA1'\/%3E%3Ccircle cx='531' cy='302' r='16' fill='%23073EA1'\/%3E%3Ctext x='168' y='198' text-anchor='middle' font-family='Arial, sans-serif' font-size='17' font-weight='700' fill='%2300184C'%3ERule%20plan%3C\/text%3E%3Ctext x='410' y='198' text-anchor='middle' font-family='Arial, sans-serif' font-size='17' font-weight='700' fill='%2300184C'%3ERule%20result%3C\/text%3E%3Ctext x='652' y='198' text-anchor='middle' font-family='Arial, sans-serif' font-size='17' font-weight='700' fill='%2300184C'%3EAI%20score%3C\/text%3E%3Ctext x='290' y='338' text-anchor='middle' font-family='Arial, sans-serif' font-size='17' font-weight='700' fill='%2300184C'%3EInvestigator%3C\/text%3E%3Ctext x='531' y='338' text-anchor='middle' font-family='Arial, sans-serif' font-size='17' font-weight='700' fill='%2300184C'%3EDecision%3C\/text%3E%3Ctext x='410' y='410' text-anchor='middle' font-family='Arial, sans-serif' font-size='20' font-weight='600' fill='%23AEC9FF'%3EDistinct%20signals%20within%20an%20accountable%20process%3C\/text%3E%3C\/svg%3E\" alt=\"FraudShield keeps triggered rule results and the AI score separate so reviewers can examine where they agree or differ.\" loading=\"lazy\" width=\"820\" height=\"460\">\n    <p class=\"nx-caption\">FraudShield keeps triggered rule results and the AI score separate so reviewers can examine where they agree or differ.<\/p>\n  <\/div>\n  <!-- ILLUSTRATIVE WORKFLOW: steps are editorial summaries of the article -->\n  <div class=\"nx-flow\" aria-label=\"Illustrative decision workflow\">\n    <div class=\"nx-flow-step\">\n      <div class=\"nx-flow-number\">1<\/div>\n      <h4>Define rules<\/h4>\n      <p>Use a controlled rule plan for known monitoring conditions.<\/p>\n    <\/div>\n    <div class=\"nx-flow-step\">\n      <div class=\"nx-flow-number\">2<\/div>\n      <h4>Assess activity<\/h4>\n      <p>Keep rule outcomes separate from AI proximity scoring.<\/p>\n    <\/div>\n    <div class=\"nx-flow-step\">\n      <div class=\"nx-flow-number\">3<\/div>\n      <h4>Interpret signals<\/h4>\n      <p>Check explanations, discrepancies and available evidence.<\/p>\n    <\/div>\n    <div class=\"nx-flow-step\">\n      <div class=\"nx-flow-number\">4<\/div>\n      <h4>Apply policy<\/h4>\n      <p>Investigators determine whether further review is warranted.<\/p>\n    <\/div>\n  <\/div>\n  <div class=\"nx-callout\">\n    <div class=\"nx-callout-title\">Quick answer<\/div>\n    <p>FraudShield\u2019s AI risk scoring complements rather than replaces transaction monitoring rules. The rule outcome and AI score are separate; investigators use both within the organisation\u2019s monitoring policy.<\/p>\n  <\/div>\n  <h2 id=\"why-rules-remain-important\"><span class=\"ez-toc-section\" id=\"Why_rules_remain_important\"><\/span>Why rules remain important<span class=\"ez-toc-section-end\"><\/span><\/h2>\n  <p>A monitoring rule is a deliberate choice. It identifies a pattern or condition that an organisation has decided should generate attention under its risk framework.<\/p>\n  <p>Rules can be reviewed, changed through controlled procedures and tested against relevant scenarios. They create an understandable basis for many monitoring actions, although their effectiveness still depends on their design and the data available.<\/p>\n  <p>A binary outcome is not a weakness in itself. It is what makes a threshold actionable. The weakness appears when the organisation mistakes that outcome for a complete understanding of every transaction.<\/p>\n  <h2 id=\"the-space-between-thresholds\"><span class=\"ez-toc-section\" id=\"The_space_between_thresholds\"><\/span>The space between thresholds<span class=\"ez-toc-section-end\"><\/span><\/h2>\n  <p>Imagine a transaction that stays just inside several rule limits. It may not trigger any rule, even though its overall profile is close to the conditions the organisation monitors.<\/p>\n  <p>An additional assessment can help highlight that proximity. The key is to understand what the assessment actually measures rather than treating every score as a general-purpose fraud prediction.<\/p>\n  <p>FraudShield&#8217;s published AI Engine offers one specific example: its AI risk score is trained against a finalised rule plan and reflects how close processed activity comes to breaching the rules in that plan.<\/p>\n  <p>That scope is important. It is an adjunct to the existing monitoring logic, not an independent declaration of criminality.<\/p>\n  <h2 id=\"two-signals-one-accountable-workflow\"><span class=\"ez-toc-section\" id=\"Two_signals_one_accountable_workflow\"><\/span>Two signals, one accountable workflow<span class=\"ez-toc-section-end\"><\/span><\/h2>\n  <p>FraudShield keeps the rule result separate from the AI score. A triggered rule remains triggered even if the AI score is comparatively low. Equally, a high AI score does not rewrite a rule result that was not flagged.<\/p>\n  <p>This separation allows investigators to see where the two signals agree and where they differ.<\/p>\n  <p>The operational value comes from deciding what to do with that difference. A high score on an unflagged transaction may justify further review under a chosen policy. A triggered rule still requires its normal treatment even when the AI score adds little confirmation.<\/p>\n  <p>Configured case workflows help turn the information into a process, with human investigators responsible for evaluating what the evidence means.<\/p>\n  <h2 id=\"avoiding-the-illusion-of-certainty\"><span class=\"ez-toc-section\" id=\"Avoiding_the_illusion_of_certainty\"><\/span>Avoiding the illusion of certainty<span class=\"ez-toc-section-end\"><\/span><\/h2>\n  <p>AI can make a risk dashboard appear more precise by adding a number with decimal places. That precision does not automatically make a conclusion more reliable.<\/p>\n  <p>Reviewers need to know what the score represents, what inputs contributed and how it should influence\u2014but not dictate\u2014the next step. A helpful interface makes those boundaries visible.<\/p>\n  <p>FraudShield&#8217;s AI risk assessment includes a breakdown of rule impact and influence. This supports review of the factors behind the score, although it does not eliminate the need for judgement or validation of the model&#8217;s usefulness.<\/p>\n  <div class=\"nx-callout nx-callout--warning\">\n    <div class=\"nx-callout-title\">Important distinction<\/div>\n    <p>A numerical AI score can appear precise without establishing a reliable conclusion. Investigators must understand what the score measures, review its rule-level explanations and avoid treating it as a fraud verdict.<\/p>\n  <\/div>\n  <h2 id=\"testing-the-partnership-in-practice\"><span class=\"ez-toc-section\" id=\"Testing_the_partnership_in_practice\"><\/span>Testing the partnership in practice<span class=\"ez-toc-section-end\"><\/span><\/h2>\n  <p>A team introducing an additional AI signal should consider several questions. Does it surface transactions that warrant review? How many new cases are created? Are the reasons understandable? Do staff know how to handle a high score with no triggered rule?<\/p>\n  <p>They should also test what happens when the AI component is unavailable. FraudShield&#8217;s documentation states that rule processing continues even if AI scoring cannot be completed; the AI score is then unavailable.<\/p>\n  <p>That separation reflects an important operational principle: supplementary intelligence should not unexpectedly break the established monitoring process.<\/p>\n  <h2 id=\"a-better-question-than-ai-or-rules\"><span class=\"ez-toc-section\" id=\"A_better_question_than_%E2%80%98AI_or_rules\"><\/span>A better question than &#8216;AI or rules?&#8217;<span class=\"ez-toc-section-end\"><\/span><\/h2>\n  <p>The strongest monitoring strategy may not involve choosing one approach at the expense of the other. Instead, organisations can design how different signals contribute to prioritisation, case creation and investigation while preserving clear ownership of decisions.<\/p>\n  <p>This does not remove the need to assess false positives, monitoring coverage or regulatory obligations. It provides another way to examine activity close to conditions that matter to the business.<\/p>\n  <p>The role of Decision AI is not necessarily to replace the rules. It may be to make the spaces between them more visible, so investigators can decide what deserves attention.<\/p>\n  <h2 id=\"further-reading\"><span class=\"ez-toc-section\" id=\"Further_reading\"><\/span>Further reading<span class=\"ez-toc-section-end\"><\/span><\/h2>\n  <p>See <a class=\"nx-inline-link\" href=\"https:\/\/nexiant.ai\/fraudshield\/docs\/guides\/ai-engine\/how-ai-risk-scoring-works\/\" rel=\"noopener noreferrer\">How AI Risk Scoring Works in FraudShield<\/a> and <a class=\"nx-inline-link\" href=\"https:\/\/nexiant.ai\/fraudshield\/docs\/\" rel=\"noopener noreferrer\">the FraudShield overview<\/a>.<\/p>\n  <hr class=\"nx-divider\">\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  <div class=\"nx-faq\">\n    <div class=\"nx-faq-item\">\n      <button class=\"nx-faq-q\" type=\"button\" aria-expanded=\"false\" aria-controls=\"nx-faq-answer-02-1\">Does FraudShield\u2019s AI score replace transaction monitoring rules?<span class=\"nx-chevron\" aria-hidden=\"true\"><svg viewBox=\"0 0 10 6\" stroke-width=\"1.5\" stroke-linecap=\"round\" stroke-linejoin=\"round\"><path d=\"M1 1l4 4 4-4\"><\/path><\/svg><\/span><\/button>\n      <div id=\"nx-faq-answer-02-1\" class=\"nx-faq-a\">No. Rule processing and AI scoring are separate. The AI score supplements the outcome of the configured rule plan.<\/div>\n    <\/div>\n    <div class=\"nx-faq-item\">\n      <button class=\"nx-faq-q\" type=\"button\" aria-expanded=\"false\" aria-controls=\"nx-faq-answer-02-2\">What does FraudShield\u2019s AI risk score measure?<span class=\"nx-chevron\" aria-hidden=\"true\"><svg viewBox=\"0 0 10 6\" stroke-width=\"1.5\" stroke-linecap=\"round\" stroke-linejoin=\"round\"><path d=\"M1 1l4 4 4-4\"><\/path><\/svg><\/span><\/button>\n      <div id=\"nx-faq-answer-02-2\" class=\"nx-faq-a\">It reflects how close processed activity comes to breaching rules in the finalised active rule plan, rather than a general probability of fraud.<\/div>\n    <\/div>\n    <div class=\"nx-faq-item\">\n      <button class=\"nx-faq-q\" type=\"button\" aria-expanded=\"false\" aria-controls=\"nx-faq-answer-02-3\">Can a rule trigger even if its AI score is low?<span class=\"nx-chevron\" aria-hidden=\"true\"><svg viewBox=\"0 0 10 6\" stroke-width=\"1.5\" stroke-linecap=\"round\" stroke-linejoin=\"round\"><path d=\"M1 1l4 4 4-4\"><\/path><\/svg><\/span><\/button>\n      <div id=\"nx-faq-answer-02-3\" class=\"nx-faq-a\">Yes. A triggered rule remains triggered even when the separate AI score is comparatively low.<\/div>\n    <\/div>\n    <div class=\"nx-faq-item\">\n      <button class=\"nx-faq-q\" type=\"button\" aria-expanded=\"false\" aria-controls=\"nx-faq-answer-02-4\">What happens if AI scoring is unavailable?<span class=\"nx-chevron\" aria-hidden=\"true\"><svg viewBox=\"0 0 10 6\" stroke-width=\"1.5\" stroke-linecap=\"round\" stroke-linejoin=\"round\"><path d=\"M1 1l4 4 4-4\"><\/path><\/svg><\/span><\/button>\n      <div id=\"nx-faq-answer-02-4\" class=\"nx-faq-a\">FraudShield\u2019s documentation states that rule processing continues even when AI scoring cannot be completed; the AI score is then unavailable.<\/div>\n    <\/div>\n    <div class=\"nx-faq-item\">\n      <button class=\"nx-faq-q\" type=\"button\" aria-expanded=\"false\" aria-controls=\"nx-faq-answer-02-5\">How should teams use an AI score that differs from the rule result?<span class=\"nx-chevron\" aria-hidden=\"true\"><svg viewBox=\"0 0 10 6\" stroke-width=\"1.5\" stroke-linecap=\"round\" stroke-linejoin=\"round\"><path d=\"M1 1l4 4 4-4\"><\/path><\/svg><\/span><\/button>\n      <div id=\"nx-faq-answer-02-5\" class=\"nx-faq-a\">They should apply defined monitoring procedures, examine the underlying factors and let investigators decide whether further review is justified.<\/div>\n    <\/div>\n  <\/div>\n  <div class=\"nx-cta\">\n    <h3>Bring rules and AI into the same conversation<\/h3>\n    <p>Explore how FraudShield\u2019s documented AI scoring and monitoring workflows may fit your team\u2019s existing controls.<\/p>\n    <a href=\"https:\/\/nexiant.ai\/contact-us\/\">Contact Nexiant<\/a>\n  <\/div>\n  <p class=\"nx-disclaimer\">This article was accurate at the time of publication in November 2026 and is intended for general informational purposes only. It does not constitute legal, regulatory or compliance advice. Organisations should seek qualified professional guidance in relation to their specific obligations.<\/p>\n<\/div>\n<script>\n  document.querySelectorAll('.nx-blog .nx-faq-q').forEach(function(btn) {\n    btn.addEventListener('click', function() {\n      var expanded = this.getAttribute('aria-expanded') === 'true';\n      this.setAttribute('aria-expanded', String(!expanded));\n      this.nextElementSibling.classList.toggle('open', !expanded);\n    });\n  });\n<\/script>\n<script type=\"application\/ld+json\">\n{\n  \"@context\": \"https:\/\/schema.org\",\n  \"@type\": \"FAQPage\",\n  \"mainEntity\": [\n    {\n      \"@type\": \"Question\",\n      \"name\": \"Does FraudShield\u2019s AI score replace transaction monitoring rules?\",\n      \"acceptedAnswer\": {\n        \"@type\": \"Answer\",\n        \"text\": \"No. Rule processing and AI scoring are separate. 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Financial crime teams have spent years building monitoring rules around known risk scenarios, internal policies and the activity they need to investigate. It would be strange to assume that a new AI model makes [&hellip;]<\/p>\n","protected":false},"author":7,"featured_media":929,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_aioseo_title":"AI and Rules in Transaction Monitoring | Nexiant","_aioseo_description":"Explore how FraudShield combines rules with a separate AI score to highlight near-threshold transactions and support accountable investigations.","om_disable_all_campaigns":false,"footnotes":""},"categories":[1,14],"tags":[83,38],"class_list":["post-927","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-fraudprevention","category-aml-2","tag-decision-ai","tag-transaction-monitoring"],"aioseo_notices":[],"aioseo_head":"\n\t\t<!-- All in One SEO 5.0.4 - aioseo.com -->\n\t<meta name=\"description\" content=\"Explore how FraudShield combines rules with a separate AI score to highlight near-threshold transactions and support accountable investigations.\" \/>\n\t<meta name=\"robots\" content=\"max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n\t<meta name=\"author\" content=\"Zeeshan Rizvi\"\/>\n\t<link rel=\"canonical\" href=\"https:\/\/nexiant.ai\/resources\/blogs\/ai-complements-transaction-monitoring-rules\/\" \/>\n\t\t<meta name=\"generator\" content=\"All in One SEO (AIOSEO) 5.0.4\" \/>\n\t\t<meta property=\"og:locale\" content=\"en_US\" \/>\n\t\t<meta property=\"og:site_name\" content=\"AI-Assisted Fraud Prevention and Risk Management - Nexiant\" \/>\n\t\t<meta property=\"og:type\" content=\"article\" \/>\n\t\t<meta property=\"og:title\" content=\"AI and Rules in Transaction Monitoring | Nexiant\" \/>\n\t\t<meta property=\"og:description\" content=\"Explore how FraudShield combines rules with a separate AI score to highlight near-threshold transactions and support accountable investigations.\" \/>\n\t\t<meta property=\"og:url\" content=\"https:\/\/nexiant.ai\/resources\/blogs\/ai-complements-transaction-monitoring-rules\/\" \/>\n\t\t<meta property=\"og:image\" content=\"https:\/\/nexiant.ai\/resources\/blogs\/wp-content\/uploads\/2025\/11\/Nexiant-Dark-Logo-Apr.png\" \/>\n\t\t<meta property=\"og:image:secure_url\" content=\"https:\/\/nexiant.ai\/resources\/blogs\/wp-content\/uploads\/2025\/11\/Nexiant-Dark-Logo-Apr.png\" \/>\n\t\t<meta property=\"article:published_time\" content=\"2026-10-26T04:14:00+00:00\" \/>\n\t\t<meta property=\"article:modified_time\" content=\"2026-11-06T03:58:36+00:00\" \/>\n\t\t<meta name=\"twitter:card\" content=\"summary_large_image\" \/>\n\t\t<meta name=\"twitter:title\" content=\"AI and Rules in Transaction Monitoring | Nexiant\" \/>\n\t\t<meta name=\"twitter:description\" content=\"Explore how FraudShield combines rules with a separate AI score to highlight near-threshold transactions and support accountable investigations.\" \/>\n\t\t<meta name=\"twitter:image\" content=\"https:\/\/nexiant.ai\/resources\/blogs\/wp-content\/uploads\/2025\/11\/Nexiant-Dark-Logo-Apr.png\" \/>\n\t\t<script type=\"application\/ld+json\" class=\"aioseo-schema\">\n\t\t\t{\"@context\":\"https:\\\/\\\/schema.org\",\"@graph\":[{\"@type\":\"BlogPosting\",\"@id\":\"https:\\\/\\\/nexiant.ai\\\/resources\\\/blogs\\\/ai-complements-transaction-monitoring-rules\\\/#blogposting\",\"name\":\"AI and Rules in Transaction Monitoring | Nexiant\",\"headline\":\"AI Shouldn&#8217;t Replace the Rules. 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