[{"data":1,"prerenderedAt":502},["ShallowReactive",2],{"uc-shariah-compliance-screening":3,"uc-regulations":293},{"useCase":4,"evidence":184,"blitsAiDeployments":219,"benchmarks":220,"indicative":227,"related":230,"indexability":291,"includeUnpublished":190},{"title":5,"shortTitle":6,"seoTitle":7,"metaDescription":8,"status":9,"definition":10,"aliases":11,"industries":16,"functions":20,"patterns":24,"channels":29,"audience":31,"autonomy":32,"adoptionStage":33,"segment":34,"problem":35,"problemStats":36,"howItWorks":42,"valueDrivers":43,"kpis":47,"indicativeValue":53,"macroEstimates":88,"feasibility":89,"implementation":101,"risk":136,"blitsAi":161,"faq":163,"related":173,"datePublished":178,"dateModified":178,"lastVerified":179,"changelog":180,"slug":183},"AI assistant for Shariah compliance screening and review","Shariah compliance screening","AI for Shariah compliance screening and review","AI flags riba, gharar and prohibited exposure in contracts and investments, citing the standard and fatwa, while the Shariah board keeps sole authority over rulings.","published","An AI assistant that screens Islamic financing contracts, deal structures and investments for Shariah compliance risks such as riba, gharar and exposure to prohibited activities, retrieves the relevant standards and fatwas, drafts the Shariah review documentation and flags issues for the Shariah board, which keeps sole authority over any ruling.",[12,13,14,15],"Shariah screening AI","Islamic finance compliance assistant","riba and gharar detection","Shariah review copilot",[17,18,19],"banking","wealth-and-asset-management","insurance",[21,22,23],"regulatory-compliance","legal","product-and-pricing",[25,26,27,28],"rag-knowledge-assistant","document-processing","classification-and-routing","content-generation",[30],"internal-tools","employee-facing","copilot","emerging","specialized-businesses","Every Islamic financing product, contract and investment has to comply with Shariah principles:\nno interest (riba), no excessive uncertainty (gharar), no exposure to prohibited sectors, and the\nstructure must follow the approved contract type. Shariah compliance teams review contracts clause\nby clause, check structures against the institution's approved standards and the fatwas of its\nShariah board, screen investments against financial ratios, and document every review for the\nboard and for Shariah audit.\n\nMuch of this work is manual and depends on specialists who understand both finance and fiqh. The\nIslamic Financial Services Board reports that the industry keeps growing and that new products and\nstructures increasingly mimic conventional banking characteristics. AI can find clauses, compare\nthem with standards and draft documentation, but an error in the tool can lead to a Shariah non\ncompliance finding, so rulings must stay with qualified scholars.",[37],{"statement":38,"sourceTitle":39,"sourceUrl":40,"year":41},"The Islamic Financial Services Board reports that the global Islamic financial services industry reached approximately USD 4.4 trillion in total assets in 2025.","IFSB Releases Islamic Financial Stability Report 2026 Highlighting Emerging Hybrid Risks in Islamic Banking","https://www.ifsb.org/press-releases/ifsb-releases-islamic-financial-stability-report-2026-highlighting-emerging-hybrid-risks-in-islamic-banking/",2026,"1. **Load the reference base.** The institution's approved standards (for example AAOIFI based\n   policies), its Shariah board's fatwas and resolutions, and product templates are indexed.\n2. **Read the contract or structure.** Document AI splits the contract into clauses and\n   identifies the contract type, pricing, penalties, ownership transfer and asset terms.\n3. **Screen.** Each clause is compared with the approved template and standards, and the model\n   flags possible riba, gharar, prohibited activities or deviations, citing the standard.\n4. **Screen investments.** For equities and funds, business activity and financial ratio screens\n   run on current data, with changes in status monitored.\n5. **Draft the review.** It drafts the Shariah review memo with findings and references; the\n   Shariah compliance officer completes it and the Shariah board decides.",[44,45,46],"compliance","employee-productivity","speed",[48,49,50,51,52],"time-saved-per-task","processing-time-reduction","accuracy","productivity-gain","users-served",{"referenceOrg":54,"inputs":55,"formula":83,"currency":84,"period":85,"resultLabel":86,"caveat":87},"An Islamic bank reviewing 1,500 financing contracts and structures a year",[56,62,69,76],{"key":57,"label":58,"low":59,"high":59,"unit":60,"note":61},"reviews","Shariah reviews per year",1500,"reviews per year","The reference bank.",{"key":63,"label":64,"low":65,"high":66,"unit":67,"note":68},"hoursPerReview","Compliance hours per review",3,6,"hours per review","Editorial assumption, replace with your own time study.",{"key":70,"label":71,"low":72,"high":73,"unit":74,"note":75},"timeSaved","Share of review time saved",0.2,0.35,"fraction of time","Editorial assumption; no verified public benchmark was found.",{"key":77,"label":78,"low":79,"high":80,"unit":81,"note":82},"hourlyCost","Loaded cost of a Shariah compliance hour",70,120,"USD per hour","Editorial assumption, replace with your own loaded cost.","reviews * hoursPerReview * timeSaved * hourlyCost","USD","per year","Shariah compliance time released, valued at loaded cost","Values compliance time only. It leaves out faster product approval, fewer Shariah non compliance events and income purification, and the cost of building and validating the reference base.",[],{"complexity":90,"complexityNote":91,"dataPrerequisites":92,"integrations":97},"medium","The retrieval and drafting are standard; the hard parts are a curated, versioned reference base of the institution's own standards and fatwas, Arabic and local language sources, and scholar trust in the outputs.",[93,94,95,96],"The institution's approved Shariah standards, policies and product templates","Shariah board fatwas and resolutions, versioned","Past reviews with findings, for testing","Financial data for investment screening",[98,99,100],"Document management and contract repository","Product approval workflow","Market and financial data for investment screening",{"steps":102,"guardrails":115,"humanInTheLoop":120,"kpisToInstrument":121,"failureModes":126},[103,106,109,112],{"title":104,"detail":105},"Curate the reference base with the Shariah board","Agree which standards, fatwas and resolutions the assistant may use, who maintains them and how superseded rulings are marked, before any screening.",{"title":107,"detail":108},"Start with templates and deviations","Begin by comparing contracts with approved templates and highlighting deviations, which is verifiable, before asking the model to judge substance.",{"title":110,"detail":111},"Test on past reviews","Run the assistant on past contracts with known findings, including hard cases such as hybrid and cross border structures, and share the results with the board.",{"title":113,"detail":114},"Draft documentation, not rulings","Use the assistant to draft review memos and audit working papers with references, leaving conclusions to the compliance officer and rulings to the board.",[116,117,118,119],"The assistant never issues or implies a Shariah ruling; it flags and cites","Only board approved standards and fatwas are in the reference base, with version control","Every finding cites the clause and the standard or fatwa it relies on","Uncertain and complex structures are routed to scholars without a suggested conclusion","Shariah compliance officers review every finding and complete every memo. The Shariah board retains sole authority over rulings and approves the reference base. Shariah audit samples the assistant's work, and the board is told how the tool works and where it is weak.",[122,123,124,125],"Review time per contract type, before and after","Findings confirmed or rejected by compliance officers","Issues found later in Shariah audit that the assistant missed","Share of findings with a correct citation",[127,130,133],{"title":128,"detail":129},"Confident errors on complex structures","A model may catch explicit interest clauses yet misjudge hybrid or novel structures. Route these to scholars and measure accuracy by structure type.",{"title":131,"detail":132},"Outdated or foreign rulings","The assistant cites a superseded fatwa or another institution's standard. Keep only approved, versioned sources in the reference base.",{"title":134,"detail":135},"Loss of scholar trust","Opaque outputs make the board reject the tool. Show sources for every finding and involve scholars in testing.",{"euAiAct":137,"regulations":140,"guidance":148,"controls":155,"incidents":160},{"tier":138,"basis":139},"minimal","An internal assistant that screens contracts for compliance with Shariah standards is not listed in Annex III: it assesses contracts, structures and securities, not the creditworthiness of natural persons (Annex III point 5(b)). If a customer facing version answers product questions, it must disclose that people are interacting with an AI system under Article 50(1). National Islamic finance regulators set their own Shariah governance expectations.",[141,142,143,144,145,146,147],"eu-ai-act","gdpr","sdaia-ai-ethics","cbuae-ai-guidance","iso-42001","bnm-shariah-governance","aaoifi-shariah-standards",[149],{"title":150,"issuer":151,"region":152,"url":153,"note":154},"NIST AI Risk Management Framework","NIST","north-america","https://www.nist.gov/itl/ai-risk-management-framework","A general structure to document how the assistant works, test it and manage its limits, which supports the transparency a Shariah board needs.",[156,157,158,159],"Board approved reference base with version control and an owner","Model documentation shared with the Shariah board, including known limitations","Audit trail of findings, sources and human decisions per review","Periodic Shariah audit sampling of assistant supported reviews",[],{"howToBuild":162},"On Blits.ai this is an **AI agent** for the Shariah compliance team with a **knowledge base** of\nthe institution's approved standards, fatwas and templates, stored in the document library with\n**version control** and retrieved with hybrid search. Contracts are uploaded in PDF or Word and\ncompared with templates; **structured output** returns the findings with the clause and the\nsource for each. **Arabic support**, including Arabic normalisation and regional Arabic\nmodels such as Humain and Fanar, helps with Arabic sources.\n\n**Guardrails** stop the agent from phrasing findings as rulings, **test suites** replay past\nreviews with known findings on every change, and **execution tracing** shows what was retrieved\nfor each answer. The platform is model agnostic and runs in the EU or UAE region for data\nresidency.",[164,167,170],{"question":165,"answer":166},"Can AI decide whether a product is Shariah compliant?","No. It can find clauses, compare them with approved standards and draft documentation, but rulings belong to the Shariah board. The assistant should flag and cite, never conclude.",{"question":168,"answer":169},"Is automated Shariah screening already in use?","For listed investments, yes, although not as generative AI: Zoya says it publishes Shariah compliance reports for over 60,000 stocks, applies the AAOIFI screening methodology under the guidance of its Shariah advisors and is trusted by more than 400,000 investors. For bank contracts we did not find a verified public deployment with results.",{"question":171,"answer":172},"Where does AI struggle in Shariah review?","With hybrid instruments, cross border structures and new products, where judgement depends on context. These cases should go to scholars without a suggested conclusion.",[174,175,176,177],"credit-memo-drafting-agent","policy-drafting-and-gap-analysis","regulatory-horizon-scanning","trade-document-examination","2026-09-27","2026-09-26",[181],{"date":178,"note":182},"First published","shariah-compliance-screening",[185],{"title":186,"useCases":187,"organization":188,"vendors":192,"summary":195,"stage":196,"year":41,"channels":197,"languages":199,"metrics":201,"outcomeDisclosed":210,"sources":211,"verification":214,"grade":216,"id":217,"organizationSlug":218},"Zoya: Shariah compliance screening of stocks and funds at retail scale",[183],{"name":189,"anonymized":190,"country":191,"region":152,"industry":18},"Zoya",false,"US",[193],{"name":189,"role":194},"in-house","Zoya, a halal investing app, publishes Shariah compliance reports for more than 60,000 stocks and screens ETFs and mutual funds, then monitors holdings and alerts users when a stock's compliance status changes. It shows how screening against published Shariah criteria works at retail scale. The app does not describe its screening as generative AI, and it does not replace a Shariah board's review of a bank's own contracts.","scaled",[198],"mobile-app",[200],"en",[202],{"kpi":52,"value":203,"unit":204,"qualifier":205,"period":206,"claimant":207,"quote":208,"sourceUrl":209},400000,"count","at-least","investors","organization","Trusted by 400,000+ Investors","https://zoya.finance/",true,[212],{"url":209,"title":213,"publisher":189},"Zoya -",{"level":215,"checkedAt":179},"source-verified","B","zoya-shariah-stock-screening",null,0,[221],{"kpi":52,"label":222,"unit":204,"aggregate":190,"higherIsBetter":210,"n":223,"nUpTo":219,"median":203,"min":203,"max":203,"byClaimant":224,"vendorOnly":190,"points":225},"Users served",1,{"organization":223,"vendor":219,"regulator":219,"independent":219},[226],{"evidenceId":217,"organization":189,"value":203,"qualifier":205,"claimant":207,"grade":216,"pooled":210},{"low":228,"high":229},63000,378000,[231,247,264,277],{"slug":174,"title":232,"shortTitle":233,"definition":234,"status":9,"industries":235,"functions":236,"patterns":240,"audience":31,"autonomy":32,"adoptionStage":242,"segment":34,"evidenceCount":243,"publicEvidenceCount":243,"organizations":244,"bestGrade":216,"headline":218,"lastVerified":178,"indexable":210},"AI agent for corporate credit analysis and credit memo drafting","Credit underwriting and memos","An AI agent that gathers a corporate borrower's documents and data, spreads the financials into the bank's template, calculates ratios and covenant headroom, pulls bureau and news information, and drafts a committee ready credit memo in which every figure links to its source, for the relationship and credit teams to challenge, complete and sign.",[17],[237,238,239],"lending-and-credit","underwriting","risk-management",[26,241,25,28],"agentic-workflow","early-adopters",2,[245,246],"Banestes","DBS Bank",{"slug":175,"title":248,"shortTitle":249,"definition":250,"status":9,"industries":251,"functions":255,"patterns":257,"audience":31,"autonomy":32,"adoptionStage":33,"segment":259,"evidenceCount":65,"publicEvidenceCount":65,"organizations":260,"bestGrade":216,"headline":218,"lastVerified":179,"indexable":210},"AI for policy drafting and policy gap analysis","Policy drafting and gaps","An assistant that takes a new or changed obligation, finds every internal policy, standard and procedure it touches, flags clauses that now conflict or are silent, and drafts the updated wording in house style as a redline for the policy owner to approve.",[252,17,19,253,254],"cross-industry","capital-markets","government",[21,22,256],"knowledge-management",[25,28,26,258],"summarization","second-line",[261,262,263],"Federal Deposit Insurance Corporation","Administration for Children and Families","Health Resources and Services Administration",{"slug":176,"title":265,"shortTitle":266,"definition":267,"status":9,"industries":268,"functions":271,"patterns":272,"audience":31,"autonomy":273,"adoptionStage":242,"segment":44,"evidenceCount":274,"publicEvidenceCount":243,"organizations":275,"bestGrade":216,"headline":218,"lastVerified":178,"indexable":210},"AI regulatory horizon scanning and obligation mapping","Regulatory horizon scanning","An AI system that continuously reads publications from the regulators and standard setters an organization answers to, classifies each item by relevance and urgency, breaks new rules into individual obligations and maps them to the internal policies and controls that meet them, so compliance owners see what changed and where the gaps are.",[252,17,19,269,18,270,254],"payments","pharma-and-life-sciences",[21,22,239],[27,26,25,258,241],"assist",4,[276,262],"Financial Conduct Authority",{"slug":177,"title":278,"shortTitle":279,"definition":280,"status":9,"industries":281,"functions":282,"patterns":284,"audience":285,"autonomy":286,"adoptionStage":242,"segment":34,"evidenceCount":65,"publicEvidenceCount":65,"organizations":287,"bestGrade":216,"headline":218,"lastVerified":178,"indexable":210},"AI examination of trade documents under letters of credit and collections","Trade document examination","AI that reads the full document presentation under a letter of credit or collection (bill of lading, commercial invoice, packing list, certificates), extracts and cross checks the data, tests it against the instructions and the ICC rules (for letters of credit, the credit terms, UCP 600 and ISBP), and lists discrepancies by severity with the rule cited, so qualified examiners focus on the genuine exceptions.",[17],[283,21],"operations",[26,27,241,258],"back-office","supervised-agent",[288,289,290],"ANZ, HSBC and Lloyds Banking Group","Rand Merchant Bank","Stanbic Bank Uganda",{"indexable":210,"reasons":292},[],[294,301,306,313,317,323,330,336,344,351,358,364,371,378,384,389,396,402,408,414,420,426,432,437,442,449,456,461,466,473,479,485,491,496],{"id":141,"label":295,"issuer":296,"region":297,"url":298,"description":299,"useCases":300,"indexable":210},"EU AI Act","European Union","europe","https://eur-lex.europa.eu/eli/reg/2024/1689/oj","Regulation (EU) 2024/1689: risk based rules for AI systems, with obligations for high risk systems listed in Annex III and transparency duties under Article 50.",197,{"id":142,"label":302,"issuer":296,"region":297,"url":303,"description":304,"useCases":305,"indexable":210},"GDPR","https://eur-lex.europa.eu/eli/reg/2016/679/oj","General Data Protection Regulation, including Article 22 on decisions based solely on automated processing.",180,{"id":145,"label":307,"issuer":308,"region":309,"url":310,"description":311,"useCases":312,"indexable":210},"ISO/IEC 42001","ISO and IEC","global","https://www.iso.org/standard/81230.html","The international management system standard for AI.",110,{"id":314,"label":150,"issuer":151,"region":152,"url":153,"description":315,"useCases":316,"indexable":210},"nist-ai-rmf","Voluntary US framework to map, measure, manage and govern AI risk, with a generative AI profile.",83,{"id":318,"label":319,"issuer":296,"region":297,"url":320,"description":321,"useCases":322,"indexable":210},"dora","DORA","https://eur-lex.europa.eu/eli/reg/2022/2554/oj","Digital Operational Resilience Act for financial entities: ICT risk, incident reporting and third party risk, including AI providers.",66,{"id":324,"label":325,"issuer":326,"region":297,"url":327,"description":328,"useCases":329,"indexable":210},"uk-gdpr","UK GDPR","Information Commissioner's Office","https://ico.org.uk/for-organisations/uk-gdpr-guidance-and-resources/","The UK's version of the GDPR, including rules on solely automated decisions.",64,{"id":331,"label":332,"issuer":276,"region":297,"url":333,"description":334,"useCases":335,"indexable":210},"uk-consumer-duty","FCA Consumer Duty","https://www.fca.org.uk/firms/consumer-duty","UK rules that require firms to deliver good outcomes for retail customers, including through automated channels.",47,{"id":337,"label":338,"issuer":339,"region":340,"url":341,"description":342,"useCases":343,"indexable":210},"mas-ai-risk-management","MAS AI risk management guidelines","Monetary Authority of Singapore","asia-pacific","https://www.mas.gov.sg/news/media-releases/2025/mas-guidelines-for-artificial-intelligence-risk-management","Singapore's supervisory expectations for AI risk management at financial institutions, building on the FEAT principles.",36,{"id":345,"label":346,"issuer":347,"region":340,"url":348,"description":349,"useCases":350,"indexable":210},"apra-cps-230","APRA CPS 230","Australian Prudential Regulation Authority","https://www.apra.gov.au/operational-risk-management","Australian operational risk standard covering critical operations and material service providers.",25,{"id":352,"label":353,"issuer":354,"region":309,"url":355,"description":356,"useCases":357,"indexable":210},"pci-dss","PCI DSS","PCI Security Standards Council","https://www.pcisecuritystandards.org/","Security standard for any system that stores, processes or transmits cardholder data.",20,{"id":359,"label":360,"issuer":361,"region":152,"url":362,"description":363,"useCases":357,"indexable":210},"us-sr-11-7","SR 11-7 model risk management","Federal Reserve and OCC","https://www.federalreserve.gov/boarddocs/srletters/2011/sr1107.htm","US supervisory guidance on model risk management, applied by banks to AI and machine learning models.",{"id":365,"label":366,"issuer":367,"region":297,"url":368,"description":369,"useCases":370,"indexable":210},"uk-atrs","UK Algorithmic Transparency Recording Standard","UK Government","https://www.gov.uk/government/collections/algorithmic-transparency-recording-standard-hub","Mandatory transparency records for algorithmic tools used by UK central government.",16,{"id":372,"label":373,"issuer":374,"region":309,"url":375,"description":376,"useCases":377,"indexable":210},"fatf-recommendations","FATF Recommendations","Financial Action Task Force","https://www.fatf-gafi.org/en/publications/Fatfrecommendations/Fatf-recommendations.html","Global standards for anti money laundering and counter terrorist financing that national rules implement.",15,{"id":379,"label":380,"issuer":296,"region":297,"url":381,"description":382,"useCases":383,"indexable":210},"eu-amlr","EU Anti Money Laundering Regulation","https://eur-lex.europa.eu/eli/reg/2024/1624/oj","Regulation (EU) 2024/1624: the single EU rulebook for customer due diligence, beneficial ownership and suspicious transaction reporting.",14,{"id":385,"label":386,"issuer":296,"region":297,"url":387,"description":388,"useCases":383,"indexable":210},"nis2","NIS2 Directive","https://eur-lex.europa.eu/eli/dir/2022/2555/oj","Directive (EU) 2022/2555 on cybersecurity for essential and important entities, including telecom networks, energy and public administration.",{"id":390,"label":391,"issuer":392,"region":152,"url":393,"description":394,"useCases":395,"indexable":210},"us-bsa","Bank Secrecy Act","FinCEN","https://www.fincen.gov/resources/statutes-and-regulations/bank-secrecy-act","US anti money laundering law: customer due diligence, suspicious activity reports and record keeping.",13,{"id":397,"label":398,"issuer":296,"region":297,"url":399,"description":400,"useCases":401,"indexable":210},"eu-accessibility-act","European Accessibility Act","https://eur-lex.europa.eu/eli/dir/2019/882/oj","Directive (EU) 2019/882: accessibility requirements for banking services, ecommerce and other digital services, applicable since June 2025.",12,{"id":403,"label":404,"issuer":405,"region":152,"url":406,"description":407,"useCases":401,"indexable":210},"hipaa","HIPAA","US Department of Health and Human Services","https://www.hhs.gov/hipaa/index.html","US rules for the privacy and security of protected health information.",{"id":409,"label":410,"issuer":411,"region":309,"url":412,"description":413,"useCases":401,"indexable":210},"telecom-consumer-rules","Telecom consumer protection rules","National telecom regulators","https://www.berec.europa.eu/","National rules on telecom contracts, switching, billing disputes and marketing consent.",{"id":415,"label":416,"issuer":296,"region":297,"url":417,"description":418,"useCases":419,"indexable":210},"eecc","European Electronic Communications Code","https://eur-lex.europa.eu/eli/dir/2018/1972/oj","Directive (EU) 2018/1972: consumer protection, contract, switching and security rules for telecom operators.",11,{"id":421,"label":422,"issuer":423,"region":152,"url":424,"description":425,"useCases":419,"indexable":210},"us-tcpa","Telephone Consumer Protection Act","Federal Communications Commission","https://www.fcc.gov/consumers/guides/stop-unwanted-robocalls-and-texts","US consent rules for automated and prerecorded calls and texts; the FCC has confirmed AI generated voices count as artificial voices.",{"id":427,"label":428,"issuer":339,"region":340,"url":429,"description":430,"useCases":431,"indexable":210},"mas-notice-626","MAS Notice 626","https://www.mas.gov.sg/regulation/notices/notice-626","Singapore's anti money laundering and counter terrorism financing requirements for banks.",10,{"id":433,"label":434,"issuer":296,"region":297,"url":435,"description":436,"useCases":431,"indexable":210},"mifid-ii","MiFID II","https://eur-lex.europa.eu/eli/dir/2014/65/oj","Directive 2014/65/EU on markets in financial instruments: suitability and appropriateness of advice, record keeping and product governance.",{"id":438,"label":439,"issuer":296,"region":297,"url":440,"description":441,"useCases":431,"indexable":210},"eu-psd2","PSD2","https://eur-lex.europa.eu/eli/dir/2015/2366/oj","Payment Services Directive 2: strong customer authentication, transaction risk analysis exemptions and open banking access.",{"id":443,"label":444,"issuer":445,"region":297,"url":446,"description":447,"useCases":448,"indexable":210},"eba-loan-origination","EBA Guidelines on loan origination and monitoring","European Banking Authority","https://www.eba.europa.eu/regulation-and-policy/credit-risk/guidelines-on-loan-origination-and-monitoring","Expectations for credit decisioning, including the use of automated models.",9,{"id":450,"label":451,"issuer":452,"region":152,"url":453,"description":454,"useCases":455,"indexable":210},"us-ecoa-reg-b","ECOA and Regulation B","Consumer Financial Protection Bureau","https://www.consumerfinance.gov/rules-policy/regulations/1002/9/","US fair lending rules, including specific reasons in adverse action notices, which also apply when credit decisions use AI models.",8,{"id":457,"label":458,"issuer":296,"region":297,"url":459,"description":460,"useCases":455,"indexable":210},"solvency-ii","Solvency II","https://eur-lex.europa.eu/eli/dir/2009/138/oj","Directive 2009/138/EC: risk based capital, governance and model requirements for insurers.",{"id":462,"label":463,"issuer":296,"region":297,"url":464,"description":465,"useCases":66,"indexable":210},"eu-idd","Insurance Distribution Directive","https://eur-lex.europa.eu/eli/dir/2016/97/oj","Directive (EU) 2016/97: conduct rules for selling insurance, including demands and needs testing and advice.",{"id":144,"label":467,"issuer":468,"region":469,"url":470,"description":471,"useCases":472,"indexable":210},"CBUAE guidance on AI and ML","Central Bank of the UAE","middle-east","https://www.centralbank.ae/","UAE central bank expectations for the enabling technologies, AI and machine learning used by licensed financial institutions.",5,{"id":474,"label":475,"issuer":476,"region":297,"url":477,"description":478,"useCases":274,"indexable":210},"pra-ss1-23","PRA SS1/23 model risk management","Prudential Regulation Authority","https://www.bankofengland.co.uk/prudential-regulation/publication/2023/may/model-risk-management-principles-for-banks-ss","UK model risk management principles for banks, covering AI and machine learning models.",{"id":480,"label":481,"issuer":482,"region":297,"url":483,"description":484,"useCases":274,"indexable":210},"uk-psr-app-reimbursement","UK APP scam reimbursement rules","Payment Systems Regulator","https://www.psr.org.uk/our-work/app-scams/","Mandatory reimbursement of authorised push payment scam victims by UK payment firms, which shifts scam losses onto banks.",{"id":486,"label":487,"issuer":488,"region":340,"url":489,"description":490,"useCases":65,"indexable":210},"au-scams-prevention-framework","Australian Scams Prevention Framework","Australian Treasury","https://treasury.gov.au/consultation/c2024-573813","Economy wide obligations for banks, telcos and digital platforms to prevent, detect, disrupt and respond to scams.",{"id":492,"label":493,"issuer":296,"region":297,"url":494,"description":495,"useCases":65,"indexable":210},"eu-mar","EU Market Abuse Regulation","https://eur-lex.europa.eu/eli/reg/2014/596/oj","Regulation (EU) 596/2014: insider dealing and market manipulation, including the duty to detect and report suspicious orders and transactions.",{"id":497,"label":498,"issuer":499,"region":152,"url":500,"description":501,"useCases":65,"indexable":210},"us-fcra","Fair Credit Reporting Act","Federal Trade Commission","https://www.ftc.gov/legal-library/browse/statutes/fair-credit-reporting-act","US rules on consumer reports, their accuracy and permissible use, relevant to credit scoring and screening.",1790598297214]