{"id":898,"date":"2026-10-22T15:09:16","date_gmt":"2026-10-22T04:09:16","guid":{"rendered":"https:\/\/nexiant.ai\/resources\/blogs\/?p=898"},"modified":"2026-10-27T15:14:29","modified_gmt":"2026-10-27T04:14:29","slug":"insights-hkma-ai-transaction-monitoring-banks","status":"publish","type":"post","link":"https:\/\/nexiant.ai\/resources\/blogs\/insights-hkma-ai-transaction-monitoring-banks\/","title":{"rendered":"How the HKMA\u2019s AI Circulars Turn Transaction Monitoring Model Governance Into a Board Question"},"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; 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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\n\n<div class=\"nx-blog\">\n\n  <!-- HERO -->\n  <div class=\"nx-hero\">\n    <span class=\"nx-tag\">Transaction Monitoring &nbsp;\u00b7&nbsp; October 2026 &nbsp;\u00b7&nbsp; Hong Kong<\/span>\n    <p class=\"nx-meta\">The HKMA has set no deployment deadline for AI in transaction monitoring, but it expects Boards to show progress against their plans and to explain how models work, are validated and are kept in check.<\/p>\n  <\/div>\n\n  <!-- INTRODUCTION -->\n  <p><strong>HKMA AI transaction monitoring<\/strong> policy now sits in a short sequence of documents: <a href=\"https:\/\/brdr.hkma.gov.hk\/eng\/doc-ldg\/docId\/getPdf\/20241122-3-EN\/20241122-3-EN.pdf\" class=\"nx-inline-link\" target=\"_blank\" rel=\"noopener\">a circular of 9 September 2024<\/a>, <a href=\"https:\/\/brdr.hkma.gov.hk\/eng\/doc-ldg\/docId\/getPdf\/20251118-3-EN\/20251118-3-EN.pdf\" class=\"nx-inline-link\" target=\"_blank\" rel=\"noopener\">a circular of 19 November 2025<\/a> and <a href=\"https:\/\/brdr.hkma.gov.hk\/eng\/doc-ldg\/docId\/20260622-2-EN\" class=\"nx-inline-link\" target=\"_blank\" rel=\"noopener\">a report sent to banks on 22 June 2026<\/a>. Read together, they show a regulator that backs AI in anti-money laundering monitoring and, in the same breath, puts accountability for it on senior management and Boards.<\/p>\n  <p>The documents do not require every bank to deploy AI by a set date. They ask banks to study the question, plan for it, report progress and, in the words of the June 2026 report, be able to explain and govern what they deploy. That moves the question for a risk leader from which model to buy to who owns it.<\/p>\n  <p>This article follows the documents in order, separates what banks were asked to do from what Boards must be able to show, and sets out a governance map that applies to fraud and anti-money laundering teams alike.<\/p>\n\n  <!-- TOP VISUAL -->\n  <div class=\"nx-media\">\n    <img decoding=\"async\"\n      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'%3EAI governance in monitoring%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'%3EStudy%3C\/text%3E%3Ctext x='410' y='198' text-anchor='middle' font-family='Arial, sans-serif' font-size='17' font-weight='700' fill='%2300184C'%3EPlan%3C\/text%3E%3Ctext x='652' y='198' text-anchor='middle' font-family='Arial, sans-serif' font-size='17' font-weight='700' fill='%2300184C'%3EAdopt%3C\/text%3E%3Ctext x='290' y='338' text-anchor='middle' font-family='Arial, sans-serif' font-size='17' font-weight='700' fill='%2300184C'%3ETrack%3C\/text%3E%3Ctext x='531' y='338' text-anchor='middle' font-family='Arial, sans-serif' font-size='17' font-weight='700' fill='%2300184C'%3EEvidence%3C\/text%3E%3Ctext x='410' y='410' text-anchor='middle' font-family='Arial, sans-serif' font-size='20' font-weight='600' fill='%23AEC9FF'%3EFrom study to Board evidence%3C\/text%3E%3C\/svg%3E\"\n      alt=\"HKMA AI transaction monitoring visual showing the sequence from the HKMA asking banks to study AI in monitoring, through planning and adoption, to the HKMA tracking progress and Boards evidencing governance\"\n      loading=\"lazy\"\n      width=\"820\"\n      height=\"460\"\n    >\n    <p class=\"nx-caption\">The HKMA moved from asking banks to study AI in 2024, to tracking adoption in 2025, to describing in 2026 the governance Boards must be able to evidence.<\/p>\n  <\/div>\n\n  <!-- QUICK ANSWER -->\n  <div class=\"nx-callout\">\n    <div class=\"nx-callout-title\">Quick answer<\/div>\n    <p>The HKMA asked banks with significant Hong Kong operations to study AI in AML monitoring and prepare implementation plans. By its circular of 19 November 2025 it reported that more than 30% of authorized institutions had adopted AI in monitoring and that adoption would pass 80% over the next 12 to 24 months on banks\u2019 own timelines. Its June 2026 report says responsibility for AI-influenced decisions stays with senior management and Boards, who must be able to explain how models operate, how outcomes are validated and how bias, drift and over-reliance are managed.<\/p>\n  <\/div>\n\n  <h2 id=\"what-the-hkma-has-asked-banks-to-do\"><span class=\"ez-toc-section\" id=\"What_the_HKMA_has_asked_banks_to_do\"><\/span>What the HKMA has asked banks to do<span class=\"ez-toc-section-end\"><\/span><\/h2>\n  <p>The HKMA\u2019s thematic review of transaction monitoring systems, shared with banks on 17 April 2024, already expected them to optimise monitoring performance with regard to their size, business scope and ML\/TF risks. It covered governance and oversight, data quality, detection scenarios, threshold setting and periodic review, and included how banks use AI. The circular of 9 September 2024 then asked banks with significant operations in Hong Kong to study AI in their monitoring systems and to prepare implementation plans based on the result.<\/p>\n  <p>By the circular of 19 November 2025, 48 authorized institutions had assessed feasibility. The HKMA reported that more than 30% had already adopted AI in their monitoring systems and that, on the timelines banks gave, adoption would pass 80% over the following 12 to 24 months. Use cases were concentrated in risk detection and alert prioritisation, which the HKMA read as scope for broader and deeper adoption. It said it would keep tracking progress against the submitted plans and keep the effectiveness of banks\u2019 monitoring systems under regular review.<\/p>\n  <p>The circular of 22 June 2026 shared a report with four case studies, announced a second workshop on agentic AI for 23 June 2026 and reminded banks with significant Hong Kong operations that their plans should be kept under review, with progress and new use cases reported when called for.<\/p>\n  <div class=\"nx-flow\">\n    <div class=\"nx-flow-step\">\n      <div class=\"nx-flow-number\">1<\/div>\n      <h4>17 April 2024<\/h4>\n      <p>Thematic review: banks expected to optimise monitoring, including through AI.<\/p>\n    <\/div>\n    <div class=\"nx-flow-step\">\n      <div class=\"nx-flow-number\">2<\/div>\n      <h4>9 September 2024<\/h4>\n      <p>Banks asked to study AI and prepare implementation plans.<\/p>\n    <\/div>\n    <div class=\"nx-flow-step\">\n      <div class=\"nx-flow-number\">3<\/div>\n      <h4>19 November 2025<\/h4>\n      <p>HKMA reports adoption and says it will track progress.<\/p>\n    <\/div>\n    <div class=\"nx-flow-step\">\n      <div class=\"nx-flow-number\">4<\/div>\n      <h4>22 June 2026<\/h4>\n      <p>Report with case studies; plans to stay under review.<\/p>\n    <\/div>\n  <\/div>\n\n  <h2 id=\"where-accountability-sits-when-a-model-influences-a-decision\"><span class=\"ez-toc-section\" id=\"Where_accountability_sits_when_a_model_influences_a_decision\"><\/span>Where accountability sits when a model influences a decision<span class=\"ez-toc-section-end\"><\/span><\/h2>\n  <p>The June 2026 report is direct. Where AI systems influence risk assessments or decision pathways, it says, responsibility remains with senior management and Boards, technology does not replace governance, and institutions must be able to explain how models operate, how outcomes are validated and how risks such as bias, drift or over-reliance are managed. Supervisory tolerance for experimentation exists within well-defined guardrails, not outside them.<\/p>\n  <p>It also says that over the next 24 months Boards and senior management are expected to demonstrate progress across the HKMA\u2019s supervisory priorities, anchored to each institution\u2019s plan. That is the closest any of the documents comes to a deadline, and it is a deadline for evidence rather than for deployment.<\/p>\n  <p>One statement in the report frames the gap. It says that while more than half of financial institutions report AI deployment within risk functions, fewer than one third operate fully integrated model governance frameworks across lines of defence. The report does not name the source of that figure, so it is best read as the HKMA\u2019s characterisation of the problem rather than a measured Hong Kong rate.<\/p>\n  <div class=\"nx-callout\">\n    <div class=\"nx-callout-title\">Reading the expectation precisely<\/div>\n    <p>The documents ask for studies, plans and evidence of progress. They do not prescribe a model type, and the report says the future state is rules integrated within layered intelligence models, not rules removed.<\/p>\n  <\/div>\n\n  <h2 id=\"holistic-architecture-or-building-blocks\"><span class=\"ez-toc-section\" id=\"Holistic_architecture_or_building_blocks\"><\/span>Holistic architecture or building blocks<span class=\"ez-toc-section-end\"><\/span><\/h2>\n  <p>The November 2025 circular describes two routes. Some banks replaced reliance on rules-based monitoring with a holistic approach that rebuilds the data structure and applies advanced analytics to a wider range of customer and transaction data. Others adopt AI as building blocks, adding use cases that make existing processes more effective. The June 2026 report says banks should not adopt AI for its own sake, that early initiatives have mostly been point solutions, and that building-block work should feed an enterprise blueprint designed from data ingestion to alert disposition.<\/p>\n  <p>The report\u2019s four case studies show how different the routes are. The outcomes below are as reported by the banks in the HKMA publication and have not been independently verified.<\/p>\n  <div class=\"nx-table-wrap\">\n    <table class=\"nx-table\">\n      <thead>\n        <tr><th>Case in the report<\/th><th>Approach<\/th><th>Reported outcome<\/th><\/tr>\n      <\/thead>\n      <tbody>\n        <tr>\n          <td><strong>Global bank, dynamic risk assessment<\/strong><\/td>\n          <td>Holistic: cloud AI platform scoring customers on transaction, profile and KYC data<\/td>\n          <td><span class=\"nx-badge nx-badge--blue\">False positives down 60%, detection up 2 to 4 times, investigations about 50% faster<\/span><\/td>\n        <\/tr>\n        <tr>\n          <td><strong>Regional bank, dynamic risk monitoring<\/strong><\/td>\n          <td>Layered: machine learning alert prioritisation first, then a customer-level risk model on top<\/td>\n          <td><span class=\"nx-badge nx-badge--blue\">Reported lesson: solve alert volume first; connecting old and new systems was the hardest part<\/span><\/td>\n        <\/tr>\n        <tr>\n          <td><strong>Digital bank, face watch list<\/strong><\/td>\n          <td>Building block: image models developed in the HKMA GenAI Sandbox<\/td>\n          <td><span class=\"nx-badge nx-badge--blue\">Mule account detection up 30%, monitoring alerts down 25%<\/span><\/td>\n        <\/tr>\n        <tr>\n          <td><strong>Large bank, money mule detection<\/strong><\/td>\n          <td>Building blocks across onboarding and in-life monitoring<\/td>\n          <td><span class=\"nx-badge nx-badge--blue\">Model re-rated 42% of customers from medium to high risk; report states 70% of these were escalated and 40% exited<\/span><\/td>\n        <\/tr>\n      <\/tbody>\n    <\/table>\n  <\/div>\n\n  <h2 id=\"four-governance-controls-the-report-points-to\"><span class=\"ez-toc-section\" id=\"Four_governance_controls_the_report_points_to\"><\/span>Four governance controls the report points to<span class=\"ez-toc-section-end\"><\/span><\/h2>\n  <p>The report says Boards should expect clear evidence that AI improves detection while reducing unnecessary alerts, accelerates investigations without compromising judgement and prioritises genuinely higher-risk activity. Its call to action then lists four things institutions should do. The report does not present them as legal rules, and each can be tested by a Board or an internal audit team.<\/p>\n  <div class=\"nx-obligations\">\n    <div class=\"nx-obl-item\">\n      <h4>Board-approved AI risk appetite<\/h4>\n      <p>A written statement of how much model-driven decision-making the bank accepts and where human judgement stays.<\/p>\n    <\/div>\n    <div class=\"nx-obl-item\">\n      <h4>A cross-line governance committee<\/h4>\n      <p>First line, second line, data and technology sit on one committee that owns model performance.<\/p>\n    <\/div>\n    <div class=\"nx-obl-item\">\n      <h4>Live monitoring of the models<\/h4>\n      <p>Evidence that drift, bias and explainability are tracked after go-live, not only at validation.<\/p>\n    <\/div>\n    <div class=\"nx-obl-item\">\n      <h4>Annual scenario testing<\/h4>\n      <p>Testing aligned to the enterprise\u2019s own test frameworks, repeated each year.<\/p>\n    <\/div>\n  <\/div>\n\n  <h2 id=\"why-one-model-estate-serves-fraud-and-aml-teams\"><span class=\"ez-toc-section\" id=\"Why_one_model_estate_serves_fraud_and_AML_teams\"><\/span>Why one model estate serves fraud and AML teams<span class=\"ez-toc-section-end\"><\/span><\/h2>\n  <p>Two of the report\u2019s cases concern money mules, and mule networks sit across payment fraud and money laundering. In the large-bank case, a business-led team drew in AML, fraud, data and technology staff, used prevention tools to freeze payments in near real time with investigation teams in support, and could respond to a high risk score or a typology match with a due diligence review, an unusual activity report or payment controls. The report says business and risk colleagues validated the model outcomes for explainability and that the models were subject to formal model governance.<\/p>\n  <p>The practical point for a group is that a model scoring a customer for mule risk is a fraud model and an AML model at once. Whoever owns it needs authority over both uses.<\/p>\n  <div class=\"nx-signal-panel\">\n    <h3>Signs model governance has not caught up<\/h3>\n    <p>Each of these makes the explanation the HKMA expects harder to give.<\/p>\n\n    <div class=\"nx-signal-grid\">\n      <div class=\"nx-signal\">\n        <strong>No single owner<\/strong>\n        <span>Fraud and AML each tune the same scores with no shared record.<\/span>\n      <\/div>\n      <div class=\"nx-signal\">\n        <strong>Validation at launch only<\/strong>\n        <span>No evidence of drift or bias checks after go-live.<\/span>\n      <\/div>\n      <div class=\"nx-signal\">\n        <strong>Unrecorded overrides<\/strong>\n        <span>Analysts change outcomes without a recorded reason.<\/span>\n      <\/div>\n      <div class=\"nx-signal\">\n        <strong>Plans out of date<\/strong>\n        <span>The implementation plan given to the HKMA no longer matches what is deployed.<\/span>\n      <\/div>\n    <\/div>\n  <\/div>\n\n  <h2 id=\"how-nexiant-supports-governed-ai-in-monitoring\"><span class=\"ez-toc-section\" id=\"How_Nexiant_supports_governed_AI_in_monitoring\"><\/span>How Nexiant supports governed AI in monitoring<span class=\"ez-toc-section-end\"><\/span><\/h2>\n  <p>FraudShield, Nexiant\u2019s specialist transaction-monitoring brand, is built around real-time detection, configurable rule logic and case-management workflows. For a bank answering the HKMA\u2019s questions, the useful tests of any monitoring capability are whether rules and model outputs sit in one case record, whether a score change can be traced to a reason, and whether analysts can record why they overrode an alert. Nexiant brings these questions together across fraud and anti-money laundering as a design discipline, not as a claim about one integrated system.<\/p>\n\n\n  <hr class=\"nx-divider\">\n\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  <!-- FAQ -->\n  <div class=\"nx-faq\">\n    <div class=\"nx-faq-item\">\n      <button class=\"nx-faq-q\" aria-expanded=\"false\">Does the HKMA require banks to use AI in transaction monitoring?<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><\/button>\n      <div class=\"nx-faq-a\">The documents reviewed for this article set no date by which every bank must deploy AI. The 9 September 2024 circular asked banks with significant Hong Kong operations to study AI and prepare implementation plans, and the HKMA has said it will track progress against those plans. The June 2026 report adds that Boards and senior management are expected to demonstrate progress over the next 24 months.<\/div>\n    <\/div>\n    <div class=\"nx-faq-item\">\n      <button class=\"nx-faq-q\" aria-expanded=\"false\">What did the HKMA report about AI adoption in November 2025?<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><\/button>\n      <div class=\"nx-faq-a\">The circular of 19 November 2025 said 48 authorized institutions had assessed the feasibility of AI in transaction monitoring, that more than 30% had already adopted it and that, on the timelines banks provided, adoption would rise above 80% over the next 12 to 24 months.<\/div>\n    <\/div>\n    <div class=\"nx-faq-item\">\n      <button class=\"nx-faq-q\" aria-expanded=\"false\">Who is accountable when an AI model influences a monitoring decision?<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><\/button>\n      <div class=\"nx-faq-a\">The June 2026 report says responsibility remains with senior management and Boards. Institutions must be able to explain how models operate, how outcomes are validated and how bias, drift and over-reliance are managed.<\/div>\n    <\/div>\n    <div class=\"nx-faq-item\">\n      <button class=\"nx-faq-q\" aria-expanded=\"false\">Should banks replace rules with AI?<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><\/button>\n      <div class=\"nx-faq-a\">The report says the future state is not the elimination of rules but their integration within layered intelligence models. The November 2025 circular describes both a holistic replacement of reliance on rules-based monitoring and a phased building-block approach as being used by banks.<\/div>\n    <\/div>\n    <div class=\"nx-faq-item\">\n      <button class=\"nx-faq-q\" aria-expanded=\"false\">What did the HKMA say about agentic AI?<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><\/button>\n      <div class=\"nx-faq-a\">The 22 June 2026 circular announced a second workshop, scheduled for 23 June 2026, on agentic AI use cases. The report describes the near future as supervised augmentation, with agentic AI acting as a co-pilot to investigators and risk officers while humans define risk appetite, thresholds and escalation within governance that ensures transparency, explainability and auditability.<\/div>\n    <\/div>\n  <\/div>\n\n  <!-- CTA -->\n  <div class=\"nx-cta\">\n    <h3>Put governance around AI in monitoring<\/h3>\n    <p>Speak with a Nexiant expert about model governance across fraud and anti-money laundering monitoring, or explore FraudShield\u2019s case-management and rule-configuration capability.<\/p>\n    <a href=\"https:\/\/nexiant.ai\/contact-us\/\">Speak with an expert<\/a>\n  <\/div>\n\n  <p class=\"nx-disclaimer\">This article was accurate at the time of publication in October 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\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      this.setAttribute('aria-expanded', !expanded);\n      this.nextElementSibling.classList.toggle('open', !expanded);\n    });\n  });\n<\/script>\n\n<script type=\"application\/ld+json\">\n{\n  \"@context\": \"https:\/\/schema.org\",\n  \"@type\": \"FAQPage\",\n  \"mainEntity\": [\n    {\n      \"@type\": \"Question\",\n      \"name\": \"Does the HKMA require banks to use AI in transaction monitoring?\",\n      \"acceptedAnswer\": {\n        \"@type\": \"Answer\",\n        \"text\": \"The documents reviewed for this article set no date by which every bank must deploy AI. 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HKMA AI transaction monitoring policy now sits in a short sequence of [&hellip;]<\/p>\n","protected":false},"author":7,"featured_media":899,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_aioseo_title":"HKMA AI Transaction Monitoring Expectations | Nexiant","_aioseo_description":"The HKMA backs AI in bank AML monitoring and puts accountability on Boards. What its 2024 to 2026 circulars and report mean for model governance.","om_disable_all_campaigns":false,"footnotes":""},"categories":[64],"tags":[18,38],"class_list":["post-898","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-hong-kong","tag-aml","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=\"The HKMA backs AI in bank AML monitoring and puts accountability on Boards. 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