[{"data":1,"prerenderedAt":510},["ShallowReactive",2],{"uc-lease-abstraction":3,"uc-regulations":296},{"useCase":4,"evidence":150,"blitsAiDeployments":198,"benchmarks":199,"indicative":200,"related":203,"indexability":294,"includeUnpublished":156},{"title":5,"shortTitle":6,"seoTitle":5,"metaDescription":7,"status":8,"definition":9,"aliases":10,"industries":15,"functions":17,"patterns":20,"channels":24,"audience":27,"autonomy":28,"adoptionStage":29,"segment":30,"problem":31,"problemStats":32,"howItWorks":33,"valueDrivers":34,"kpis":38,"indicativeValue":43,"macroEstimates":72,"feasibility":73,"implementation":84,"risk":117,"blitsAi":129,"faq":131,"related":144,"datePublished":145,"dateModified":145,"lastVerified":145,"changelog":146,"slug":149},"AI lease abstraction for commercial real estate","Lease abstraction","AI reads commercial leases and letters of intent into structured data. Cushman & Wakefield and JLL use it to abstract leases that used to take hours to days.","published","AI that reads a commercial lease, amendment or letter of intent and extracts the key terms, such as parties, dates, rent, escalations, options and renewal notices, into structured data, so a property owner, occupier or brokerage does not retype every clause by hand into its lease administration and portfolio systems.",[11,12,13,14],"AI lease review","lease data extraction","LOI abstraction","lease administration automation",[16],"real-estate",[18,19],"operations","legal",[21,22,23],"document-processing","agentic-workflow","rag-knowledge-assistant",[25,26],"internal-tools","api","employee-facing","supervised-agent","early-adopters","lease-administration","Every commercial lease, amendment and letter of intent has to be read before its terms can be\nused: the dates that trigger a renewal or a break option, the rent and how it escalates, the\nclauses buried in a rider or an exhibit. Unframe's case study on Cushman & Wakefield reports that\nabstracting a single lease could take anywhere from six hours to three days, depending on the\ndocument's complexity, and that none of the vendors or internal approaches the firm evaluated\ncombined the accuracy and the speed it needed at scale.\n\nLeases are also rarely standardized and can run to 100 pages or more, Unframe's case study notes,\nwhich makes consistent, accurate extraction harder to achieve at scale.",[],"1. **Ingest the document.** The lease, amendment or letter of intent arrives as a PDF or scan of\n   any length, in any of the organization's operating languages.\n2. **Extract the standard fields.** The AI reads the document and produces a first pass abstract\n   against a defined schema: parties, term dates, rent, escalations, options, renewal notices and\n   similar fields.\n3. **Flag what does not fit the schema.** Non standard clauses, unusual riders and anything the\n   model is not confident about are flagged rather than guessed.\n4. **A person validates the exceptions.** Lease administration or legal staff review the flagged\n   clauses and the model's confidence, not every field on every lease.\n5. **The data flows downstream.** Confirmed fields post into the lease administration or portfolio\n   management system, so accounting, reporting and renewal tracking work from the same structured\n   record.\n6. **Answer questions from the corpus.** The same extracted data and source documents let brokers\n   and lease administrators ask questions about a specific lease or compare draft letters of\n   intent to each other.",[35,36,37],"cost-to-serve","employee-productivity","speed",[39,40,41,42],"processing-time-reduction","productivity-gain","hours-saved","accuracy",{"referenceOrg":44,"inputs":45,"formula":67,"currency":68,"period":69,"resultLabel":70,"caveat":71},"A commercial real estate services firm abstracting 10,000 leases and LOIs a year",[46,53,60],{"key":47,"label":48,"low":49,"high":50,"unit":51,"note":52},"documentsPerYear","Leases and LOIs abstracted per year",5000,20000,"documents per year","Editorial assumption, replace with your own volume.",{"key":54,"label":55,"low":56,"high":57,"unit":58,"note":59},"hoursSavedPerDocument","Analyst hours saved per document",2,5,"hours saved per document","Conservative against the two deployments on this page: Unframe's case study reports that abstraction used to take six hours to three days per lease before AI, and Cadastral reports JLL's brokerage teams now generate lease and LOI abstracts within seconds.",{"key":61,"label":62,"low":63,"high":64,"unit":65,"note":66},"costPerHour","Fully loaded cost of a lease administration or legal analyst hour",40,90,"USD per hour","Editorial assumption, replace with your own fully loaded cost.","documentsPerYear * hoursSavedPerDocument * costPerHour","USD","per year","Lease abstraction labor cost avoided","Gross labor cost avoided only. It leaves out the software cost, the time still needed to review flagged exceptions, and any recovered revenue from clauses the AI surfaces that a manual review would have missed, which neither deployment on this page reports as a company wide figure.",[],{"complexity":74,"complexityNote":75,"dataPrerequisites":76,"integrations":80},"medium","Extracting standard fields such as dates and base rent is well understood; the hard part is the long tail of non standard clauses, exhibits, amendments and, for a global portfolio, multiple languages and currencies, plus wiring confirmed output into the lease administration system instead of leaving it in a spreadsheet.",[77,78,79],"A defined extraction schema of the fields the organization actually uses downstream","A library of past leases, amendments and letters of intent in digital form","A golden set of correctly abstracted leases to measure accuracy against",[81,82,83],"Document management or electronic signature system where leases are stored","Lease administration or portfolio management system, the destination for extracted data","Optical character recognition for scanned or image based leases",{"steps":85,"guardrails":101,"humanInTheLoop":105,"kpisToInstrument":106,"failureModes":110},[86,89,92,95,98],{"title":87,"detail":88},"Define the schema before the pilot","Agree the exact fields lease administration, accounting and portfolio teams need, not every field a model can technically extract, and use that as the acceptance test.",{"title":90,"detail":91},"Pilot on one lease type","Start with the most common, most standardized lease template in the portfolio before adding ground leases, sale and leaseback structures or multi tenant riders.",{"title":93,"detail":94},"Route exceptions to a person by default","Treat every non standard clause and every low confidence extraction as a review item, not an accepted answer, until the exception rate on that clause type is proven low.",{"title":96,"detail":97},"Validate against a golden set on every change","Keep a fixed set of leases with a confirmed correct abstract and rerun it whenever the model, prompt or schema changes, so accuracy regressions are caught before they reach production.",{"title":99,"detail":100},"Wire the output into the system that uses it","Post confirmed fields into the lease administration or portfolio system automatically; extraction that stays in a standalone tool does not save the downstream re entry it is meant to remove.",[102,103,104],"Every non standard or low confidence clause is routed to a person before the data is relied on","Extracted values are shown next to the source clause so a reviewer can check them in seconds","Version control tracks which document version, and which amendment, an abstract came from","Lease administration or legal staff confirm every flagged exception before it reaches the lease administration system, and a sample of fields the model marked confident are spot checked on a schedule so silent accuracy drift is caught early.",[107,108,109],"Processing time per document, split by lease type and by whether it needed a review","Share of extracted fields confirmed correct on a review sample","Count of clauses, such as escalations or options, recovered that a prior manual process missed",[111,114],{"title":112,"detail":113},"Non standard clauses misread as standard","A clause with unusual wording gets mapped to the wrong field or missed entirely because it does not match the schema; a human review pass and a growing library of confirmed exceptions catch this over time.",{"title":115,"detail":116},"Abstracted data nobody uses","Extraction is treated as a one off clean up project rather than wired into the lease administration and portfolio systems, so the structured data goes stale as new leases and amendments arrive.",{"euAiAct":118,"regulations":121,"guidance":124,"controls":125,"incidents":128},{"tier":119,"basis":120},"context-dependent","Extraction alone is minimal risk: reading and structuring the terms of a commercial contract does not decide credit, employment, insurance, biometric identification or another use listed in Annex III, so it carries only the Article 4 AI literacy duty. The conversational assistant that lets employees ask questions about a lease adds Article 50(1): people who interact directly with it must be told they are dealing with an AI system, unless that is obvious from the context, as it usually is for an internal tool.",[122,123],"eu-ai-act","gdpr",[],[126,127],"A person confirms every non standard or low confidence clause before it is used in financial reporting or a renewal decision","An audit trail records which user confirmed the abstract that downstream numbers rely on",[],{"howToBuild":130},"On Blits.ai, lease abstraction runs as an AI agent with structured output configuration, so the\nmodel returns each document's fields, such as dates, rent and escalations, as machine readable\ndata instead of prose. A knowledge base ingests lease PDFs and images of exhibits and\namendments, and a small, scoped set of custom functions writes the confirmed fields into the\norganization's lease or portfolio management system through its API, once a person has reviewed\nthe fields the model flagged as an exception.\n\nThe same knowledge base, retrieved with hybrid search, powers a conversational assistant that\nanswers questions about a specific lease or compares draft letters of intent, in the employee's\nown language. Test suites regress extraction accuracy on a fixed set of leases before every\nchange, full execution tracing lets a reviewer inspect every agent turn behind an abstract, with\ntoken counts and trace details, and the platform is model agnostic, so a firm can route\nextraction to a different provider without rebuilding the workflow.",[132,135,138,141],{"question":133,"answer":134},"How accurate is AI lease abstraction?","Neither deployment on this page discloses a checked accuracy percentage. Cadastral reports that JLL's brokerage teams now generate lease and LOI abstracts within seconds rather than manually; treat every extraction as a first pass that a person confirms before it feeds financial reporting or a renewal decision.",{"question":136,"answer":137},"What time does lease abstraction actually save?","Unframe's case study reports that abstracting a single lease used to take six hours to three days before deployment; afterward, it reports that Cushman & Wakefield's brokers can access lease insights in real time, without a stated company wide time or cost figure.",{"question":139,"answer":140},"Can it replace a lease administrator?","No. Neither case study on this page describes the review step, so plan for one: route non standard clauses and low confidence extractions to a lease administrator or legal reviewer before the data feeds financial reporting or a renewal decision. Treat the AI as a copilot that produces a first pass, not a replacement for that review.",{"question":142,"answer":143},"Does it also handle letters of intent, not just signed leases?","Yes in the JLL deployment. Cadastral reports that JLL's brokers use the platform to generate LOI abstracts and to compare draft letters of intent to each other, alongside signed lease abstraction.",[],"2026-09-28",[147],{"date":145,"note":148},"First published","lease-abstraction",[151,179],{"title":152,"useCases":153,"organization":154,"vendors":159,"summary":163,"stage":164,"year":165,"channels":166,"languages":167,"metrics":168,"outcomeDisclosed":169,"sources":170,"verification":174,"grade":176,"id":177,"organizationSlug":178},"Cushman & Wakefield: AI lease abstraction with Unframe",[149],{"name":155,"anonymized":156,"country":157,"region":158,"industry":16},"Cushman & Wakefield",false,"US","north-america",[160],{"name":161,"role":162},"Unframe","platform","Cushman & Wakefield evaluated multiple vendors and internal approaches for lease abstraction before selecting Unframe in a competitive tender. Unframe's case study reports that abstracting a single lease used to take six hours to three days; after deployment, the platform processes leases of any length, across multiple languages, and brokers can access lease insights in real time. What started as one use case has grown into more than 15 active Unframe projects across the business.","production",2026,[25],[],[],true,[171],{"url":172,"title":173,"publisher":161},"https://www.unframe.ai/customer-stories/cushman-wakefield","Cushman & Wakefield AI Lease Abstraction: Faster CRE Deals | Unframe AI",{"level":175,"checkedAt":145},"source-verified","C","cushman-wakefield-unframe-lease-abstraction",null,{"title":180,"useCases":181,"organization":182,"vendors":184,"summary":187,"stage":164,"year":165,"channels":188,"languages":189,"metrics":190,"outcomeDisclosed":169,"sources":191,"verification":196,"grade":176,"id":197,"organizationSlug":178},"JLL: AI lease and LOI abstraction with Cadastral",[149],{"name":183,"anonymized":156,"country":157,"region":158,"industry":16},"JLL (Jones Lang LaSalle)",[185],{"name":186,"role":162},"Cadastral","JLL's leasing brokerage business replaced manual lease and letter of intent abstraction with Cadastral, an AI platform later acquired by the legal AI company Legora. Brokers use it to generate lease and LOI abstracts within seconds, answer questions about complex leases through an integrated chat feature, and compare draft LOIs to each other, instead of relying on manual review. Cadastral reports the deployment now produces thousands of abstracts a year and saves JLL hundreds of thousands of dollars annually, without giving an exact figure for either.",[25],[],[],[192],{"url":193,"title":194,"publisher":186,"archivedUrl":195},"https://cadastral.ai/case-studies/jll","LL Leasing: AI-Driven Lease & LOI Abstraction | Cadastral","https://web.archive.org/web/20260208044726/https://cadastral.ai/case-studies/jll",{"level":175,"checkedAt":145},"jll-cadastral-lease-abstraction",0,[],{"low":201,"high":202},400000,9000000,[204,224,255,277],{"slug":205,"title":206,"shortTitle":207,"definition":208,"status":8,"industries":209,"functions":210,"patterns":213,"audience":216,"autonomy":28,"adoptionStage":29,"evidenceCount":217,"publicEvidenceCount":217,"organizations":218,"bestGrade":222,"headline":178,"lastVerified":223,"indexable":169},"apartment-leasing-and-resident-service-agent","AI agent for apartment leasing inquiries and resident service","Leasing and resident service agent","An AI agent that answers rental prospects and residents by chat, text, email and phone for a property manager: it answers questions about apartments and policies, books tours, takes maintenance requests, sends renewal and payment reminders, and hands anything that needs judgment to leasing or service staff.",[16],[211,212,18],"customer-service","sales",[214,215,22,23],"conversational-agent","voice-agent","customer-facing",3,[219,220,221],"Asset Living","AvalonBay Communities","Equity Residential","B","2026-09-27",{"slug":225,"title":226,"shortTitle":227,"definition":228,"status":8,"industries":229,"functions":233,"patterns":236,"audience":27,"autonomy":239,"adoptionStage":29,"segment":240,"evidenceCount":57,"publicEvidenceCount":241,"organizations":242,"bestGrade":222,"headline":247,"lastVerified":223,"indexable":169},"deal-sourcing-and-due-diligence-assistant","AI assistant for deal sourcing and M&A due diligence","Deal sourcing and due diligence","An AI assistant that screens the market for acquisition or investment targets, builds company profiles, and speeds up due diligence by reading data room documents, extracting key terms and risks and drafting the investment or diligence memo, for the deal team to verify and decide.",[230,231,232],"capital-markets","wealth-and-asset-management","professional-services",[234,19,235],"analytics-and-reporting","risk-management",[21,237,23,22,238],"summarization","prediction-and-scoring","copilot","front-office",4,[243,244,245,246],"Datasite","EQT","Freshfields","Rogo",{"kpi":40,"label":248,"unit":249,"n":198,"nUpTo":250,"kind":251,"value":252,"qualifier":253,"claimant":254,"organization":243,"vendorReported":169},"Productivity gain","percent",1,"reported",80,"up-to","vendor",{"slug":256,"title":257,"shortTitle":258,"definition":259,"status":8,"industries":260,"functions":266,"patterns":269,"audience":27,"autonomy":239,"adoptionStage":29,"evidenceCount":271,"publicEvidenceCount":57,"organizations":272,"bestGrade":222,"headline":178,"lastVerified":223,"indexable":169},"procurement-contract-review","AI assistant for procurement and supplier contract review","Procurement and contract review","An assistant for procurement and vendor management that reads supplier contracts and proposals, extracts the key terms, flags deviations from the organization's standard positions, drafts requests for proposal and evaluation matrices, and prepares negotiation positions, with a procurement or legal owner approving every conclusion.",[261,262,263,264,265],"cross-industry","banking","government","retail-and-ecommerce","manufacturing",[267,19,268],"procurement","finance-and-accounting",[21,23,270,22],"content-generation",6,[273,274,275,276],"General Services Administration","Administration for Children and Families","Internal Revenue Service","Walmart",{"slug":278,"title":279,"shortTitle":280,"definition":281,"status":8,"industries":282,"functions":285,"patterns":288,"audience":27,"autonomy":239,"adoptionStage":289,"segment":290,"evidenceCount":217,"publicEvidenceCount":217,"organizations":291,"bestGrade":222,"headline":178,"lastVerified":223,"indexable":169},"supervisory-exam-response-assembly","AI for supervisory exam and information request responses","Exam response assembly","An assistant for the bank's regulatory affairs team that reads a supervisory information request or exam question, retrieves the relevant evidence, policies and prior correspondence, drafts a response for legal and compliance to approve, and tracks every commitment and remediation action through to closure.",[262,283,230,284],"insurance","payments",[286,19,287],"regulatory-compliance","case-management",[23,270,21,22],"emerging","second-line",[292,293],"U.S. Department of Homeland Security","Federal Emergency Management Agency",{"indexable":169,"reasons":295},[],[297,304,309,317,324,330,337,344,352,359,366,372,379,386,392,397,404,410,416,422,428,434,440,445,450,457,464,469,474,481,487,493,499,504],{"id":122,"label":298,"issuer":299,"region":300,"url":301,"description":302,"useCases":303,"indexable":169},"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":123,"label":305,"issuer":299,"region":300,"url":306,"description":307,"useCases":308,"indexable":169},"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":310,"label":311,"issuer":312,"region":313,"url":314,"description":315,"useCases":316,"indexable":169},"iso-42001","ISO/IEC 42001","ISO and IEC","global","https://www.iso.org/standard/81230.html","The international management system standard for AI.",110,{"id":318,"label":319,"issuer":320,"region":158,"url":321,"description":322,"useCases":323,"indexable":169},"nist-ai-rmf","NIST AI Risk Management Framework","NIST","https://www.nist.gov/itl/ai-risk-management-framework","Voluntary US framework to map, measure, manage and govern AI risk, with a generative AI profile.",83,{"id":325,"label":326,"issuer":299,"region":300,"url":327,"description":328,"useCases":329,"indexable":169},"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":331,"label":332,"issuer":333,"region":300,"url":334,"description":335,"useCases":336,"indexable":169},"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":338,"label":339,"issuer":340,"region":300,"url":341,"description":342,"useCases":343,"indexable":169},"uk-consumer-duty","FCA Consumer Duty","Financial Conduct Authority","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":345,"label":346,"issuer":347,"region":348,"url":349,"description":350,"useCases":351,"indexable":169},"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":353,"label":354,"issuer":355,"region":348,"url":356,"description":357,"useCases":358,"indexable":169},"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":360,"label":361,"issuer":362,"region":313,"url":363,"description":364,"useCases":365,"indexable":169},"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":367,"label":368,"issuer":369,"region":158,"url":370,"description":371,"useCases":365,"indexable":169},"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":373,"label":374,"issuer":375,"region":300,"url":376,"description":377,"useCases":378,"indexable":169},"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":380,"label":381,"issuer":382,"region":313,"url":383,"description":384,"useCases":385,"indexable":169},"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":387,"label":388,"issuer":299,"region":300,"url":389,"description":390,"useCases":391,"indexable":169},"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":393,"label":394,"issuer":299,"region":300,"url":395,"description":396,"useCases":391,"indexable":169},"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":398,"label":399,"issuer":400,"region":158,"url":401,"description":402,"useCases":403,"indexable":169},"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":405,"label":406,"issuer":299,"region":300,"url":407,"description":408,"useCases":409,"indexable":169},"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":411,"label":412,"issuer":413,"region":158,"url":414,"description":415,"useCases":409,"indexable":169},"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":417,"label":418,"issuer":419,"region":313,"url":420,"description":421,"useCases":409,"indexable":169},"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":423,"label":424,"issuer":299,"region":300,"url":425,"description":426,"useCases":427,"indexable":169},"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":429,"label":430,"issuer":431,"region":158,"url":432,"description":433,"useCases":427,"indexable":169},"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":435,"label":436,"issuer":347,"region":348,"url":437,"description":438,"useCases":439,"indexable":169},"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":441,"label":442,"issuer":299,"region":300,"url":443,"description":444,"useCases":439,"indexable":169},"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":446,"label":447,"issuer":299,"region":300,"url":448,"description":449,"useCases":439,"indexable":169},"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":451,"label":452,"issuer":453,"region":300,"url":454,"description":455,"useCases":456,"indexable":169},"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":458,"label":459,"issuer":460,"region":158,"url":461,"description":462,"useCases":463,"indexable":169},"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":465,"label":466,"issuer":299,"region":300,"url":467,"description":468,"useCases":463,"indexable":169},"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":470,"label":471,"issuer":299,"region":300,"url":472,"description":473,"useCases":271,"indexable":169},"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":475,"label":476,"issuer":477,"region":478,"url":479,"description":480,"useCases":57,"indexable":169},"cbuae-ai-guidance","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.",{"id":482,"label":483,"issuer":484,"region":300,"url":485,"description":486,"useCases":241,"indexable":169},"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":488,"label":489,"issuer":490,"region":300,"url":491,"description":492,"useCases":241,"indexable":169},"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":494,"label":495,"issuer":496,"region":348,"url":497,"description":498,"useCases":217,"indexable":169},"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":500,"label":501,"issuer":299,"region":300,"url":502,"description":503,"useCases":217,"indexable":169},"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":505,"label":506,"issuer":507,"region":158,"url":508,"description":509,"useCases":217,"indexable":169},"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.",1790598302431]