[{"data":1,"prerenderedAt":555},["ShallowReactive",2],{"uc-ecommerce-order-fraud-screening":3,"uc-regulations":329},{"useCase":4,"evidence":171,"blitsAiDeployments":223,"benchmarks":224,"indicative":225,"related":228,"indexability":327,"includeUnpublished":177},{"title":5,"shortTitle":6,"seoTitle":7,"metaDescription":8,"status":9,"definition":10,"aliases":11,"industries":17,"functions":19,"patterns":21,"channels":26,"audience":28,"autonomy":29,"adoptionStage":30,"segment":31,"problem":32,"problemStats":33,"howItWorks":34,"valueDrivers":35,"kpis":39,"indicativeValue":43,"macroEstimates":76,"feasibility":77,"implementation":89,"risk":127,"blitsAi":146,"faq":148,"related":161,"datePublished":166,"dateModified":166,"lastVerified":166,"changelog":167,"slug":170},"AI order fraud screening for ecommerce checkout","Ecommerce order fraud screening","AI fraud screening for ecommerce orders","AI decides in real time whether to accept an online order, often backed by a vendor guarantee against the chargebacks that slip through.","published","AI that decides, at the moment an online order is placed, whether to accept it, hold it for review or decline it, from the buyer's device, behaviour, identity and order history, so an online retailer or marketplace can approve genuine orders quickly and stop fraudulent ones before they ship, often backed by a vendor guarantee against the chargebacks that slip through.",[12,13,14,15,16],"ecommerce fraud detection","guaranteed fraud protection","order screening AI","chargeback guarantee fraud scoring","marketplace order fraud detection",[18],"retail-and-ecommerce",[20],"fraud-prevention",[22,23,24,25],"prediction-and-scoring","anomaly-detection","classification-and-routing","agentic-workflow",[27],"api","back-office","autonomous","mainstream","checkout","Every online order is a small bet. Approve it, and most of the time a genuine customer gets\ntheir package; occasionally the order was placed with a stolen card, a fake identity or a\nfriendly fraud claim waiting to happen, and the merchant absorbs the chargeback, the lost goods\nand a fee on top. Decline it, and most of the time fraud was avoided; occasionally a loyal\ncustomer was turned away for looking unusual on paper, and a merchant that never checks its\nfalse decline rate has no way of knowing how many of those customers quietly stop coming back.\n\nManual review works at a small scale, but reviewers cannot see the patterns that only show up\nacross thousands of merchants. Static rules age quickly: a rule written for last season's attack\nblocks genuine customers long after the attack has moved on. The shift is to a model that scores\nthe whole order in the time a checkout page can wait, plus a guarantee from the vendor that puts\nits own money behind the call, which is what turns a fraud score into a decision a merchant is\nwilling to automate.",[],"1. **Score the order the instant it is placed.** Device, browser, email, shipping and billing\n   details, behaviour on the site and payment data feed a model trained across many merchants,\n   not just this one's own history.\n2. **Add the wider picture.** The score is checked against known fraud rings, stolen identity\n   lists and the buyer's own order history with this merchant and, where the vendor pools data\n   across its network, with others.\n3. **Decide inside checkout.** The order is accepted, held for review or declined within the\n   time the checkout page can wait, before the customer moves on to payment confirmation.\n4. **Guarantee the call.** Vendors that offer a chargeback guarantee cover the fraud and, often,\n   \"item not received\" losses on orders they accepted, and take on the manual review and the\n   chargeback dispute themselves.\n5. **Learn from outcomes.** Confirmed fraud, disputed chargebacks and false declines feed back\n   into the model and the merchant's own risk policy.",[36,37,38],"risk-reduction","revenue-growth","cost-to-serve",[40,41,42],"fraud-loss-reduction","cost-reduction","revenue-uplift",{"referenceOrg":44,"inputs":45,"formula":72,"currency":56,"period":73,"resultLabel":74,"caveat":75},"An online retailer with 500,000 orders a year and an 80 USD average order value",[46,52,58,65],{"key":47,"label":48,"low":49,"high":49,"unit":50,"note":51},"orders","Orders per year",500000,"orders per year","The reference retailer.",{"key":53,"label":54,"low":55,"high":55,"unit":56,"note":57},"aov","Average order value",80,"USD","Editorial assumption for a mid sized online retailer, replace with your own.",{"key":59,"label":60,"low":61,"high":62,"unit":63,"note":64},"chargebackRate","Fraud chargeback rate before screening",0.003,0.008,"fraction of orders","Editorial assumption, replace with your own chargeback rate.",{"key":66,"label":67,"low":68,"high":69,"unit":70,"note":71},"reduction","Share of fraud chargeback losses avoided",0.4,0.7,"fraction of chargeback losses","Editorial assumption, replace with your own. Neither evidence record on this page reports a reduction in fraud losses specifically: Signifyd's published figures for Cymbiotika and Rainbow Shops are reductions in the overall chargeback rate, which mixes fraud and non fraud or abusive chargebacks, not a clean before and after fraud loss number, so they are not used to set this range.","orders * aov * chargebackRate * reduction","per year","Annual fraud chargeback losses avoided","Gross avoided chargeback loss only. It leaves out the fee charged for the screening or guarantee service, any change in the approval rate for genuine customers, and the operational cost of running a checkout integration.",[],{"complexity":78,"complexityNote":79,"dataPrerequisites":80,"integrations":84},"medium","A first launch is often quick because most vendors ship a ready made plugin for platforms such as Shopify. The real work is agreeing which categories, price bands and countries get a different policy, cleaning up the chargeback and refund history the model or the guarantee contract will be judged against, and rebuilding that baseline whenever the checkout platform changes.",[81,82,83],"Historical order, chargeback and refund data with fraud outcomes labelled","Device, browser and behavioural signals captured at checkout","Product catalogue and shipping data to flag high risk categories",[85,86,87,88],"Ecommerce platform or checkout","Payment service provider and card network chargeback feeds","Order management system for holds and cancellations","Customer account and order history",{"steps":90,"guardrails":106,"humanInTheLoop":111,"kpisToInstrument":112,"failureModes":117},[91,94,97,100,103],{"title":92,"detail":93},"Separate the policy from the model","Decide, as a business, which categories, countries and price bands need a stricter or looser policy, and write it down before choosing thresholds, so a change in the model does not silently change the business rule.",{"title":95,"detail":96},"Agree what the guarantee actually covers","Read the contract for carve outs, for example policy abuse, promotion abuse or a cut off for filing a dispute, and keep a short internal note of what is and is not covered so support staff do not promise more than the guarantee delivers.",{"title":98,"detail":99},"Feed it the full order context","Connect device, behaviour, shipping and account history, not just the payment details, so the model has as much signal as a human reviewer once had.",{"title":101,"detail":102},"Watch the false decline rate as closely as fraud","Sample declined orders every week and call or message a portion of the customers to check whether they were genuine, since a merchant only sees the fraud it caught, not the customers it turned away.",{"title":104,"detail":105},"Rebaseline after every checkout change","A platform migration changes the device and session signals the model sees; treat the weeks after a checkout replatforming as a fresh baseline for chargeback and false decline rates, not a continuation of the old ones.",[107,108,109,110],"Every automatic decline is logged with the reasons and can be challenged by the customer","A held order stops the shipment before a decision, never after","The score and its inputs are retained for the length of the chargeback dispute window","Payment and personal data reach the model only through the vendor's own pipeline, never typed into a general purpose prompt","Once a guarantee is in place, individual orders are rarely reviewed by the retailer's own staff: that manual review sits with the vendor's analysts, who investigate borderline orders and defend disputed chargebacks with the merchant's evidence. On the retailer's side, a person still owns the risk policy, reviews declined and disputed orders in aggregate every month, and signs off before any threshold or policy change goes live.",[113,114,115,116],"Chargeback rate and chargeback win rate, before and after","Approval rate, checked against a manually confirmed sample for false declines","Fraud losses net of the fee paid for the guarantee","Manual review queue depth and time to decision, where the merchant still reviews orders itself",[118,121,124],{"title":119,"detail":120},"Good customers declined at the worst moment","Aggressive thresholds turn away genuine repeat customers, particularly on subscription renewals. Watch complaints and repeat purchase rate by segment, not only the chargeback number: at Cymbiotika, false positives cancelled legitimate subscriptions and drove hundreds of support inquiries a day, which is why it moved to a fraud vendor aimed at reducing false positives.",{"title":122,"detail":123},"The guarantee covers less than assumed","Guarantee contracts vary by vendor and plan. Check yours for carve outs such as policy abuse, promotion abuse or a cut off for filing a dispute, and treat every declined chargeback type separately rather than assuming a single guarantee number covers all of them.",{"title":125,"detail":126},"Drift after a platform migration","Moving checkout platforms changes the device and session signals the model sees; treat the weeks after a migration as a fresh baseline for chargeback and false decline rates, not a continuation of the old ones. Confirm the new platform has a proven integration with your fraud vendor before you commit to the move: a weak integration is what pushed Rainbow Shops to switch vendors when it replatformed onto Shopify.",{"euAiAct":128,"regulations":131,"guidance":134,"controls":141,"incidents":145},{"tier":129,"basis":130},"minimal","Deciding whether to accept a commercial order is not listed in Annex III: it is not a creditworthiness, employment, essential service or biometric decision about a natural person. It stays minimal risk provided the decision is limited to a commercial transaction and does not extend into scoring the buyer's general creditworthiness or blocking access to an essential service.",[132,133],"gdpr","pci-dss",[135],{"title":136,"issuer":137,"region":138,"url":139,"note":140},"Guidelines on automated individual decision making and profiling (WP251rev.01)","Article 29 Working Party, endorsed by the European Data Protection Board","europe","https://ec.europa.eu/newsroom/article29/items/612053","The supervisory guidance behind Article 22: it explains when a solely automated decision, which a firm outright order decline can be, has a legal or similarly significant effect, and what that gives the customer a right to, including human intervention and a way to contest the decision.",[142,143,144],"A documented risk policy with an owner, reviewed whenever thresholds or the model change","A route for a declined customer to reach a human and contest the decision","Regular checks of decline rates across regions, price bands and payment methods for unfair bias",[],{"howToBuild":147},"The order scoring model itself is bought from a specialised fraud vendor and runs in the\ncheckout pipeline, because it has to answer before the page moves on. Blits.ai builds the\nworkflow around the decisions that need a person. An **agentic workflow**, triggered through\nthe API when an order is held for review, pulls the retailer's own risk policy from a\n**knowledge base**, gathers the order, customer and chargeback history through **custom\nfunctions** with REST or SQL calls into the order management and payment systems, and drafts a\nrecommendation with the evidence behind it as **structured output**.\n\n**Human in the loop approval** holds any recommendation to decline, cancel or refund above a\nset value for an analyst, and the **tool execution policy** limits which systems the workflow\nmay write to on its own. When a held order needs the customer's own input, such as confirming a\nchanged shipping address, an **agent** reaches them on **web chat, WhatsApp or email** and\npasses the answer back into the workflow. **Guardrails** and **PII masking** keep card and\nidentity data out of free text prompts, every run keeps a **full audit trail**, and **test\nsuites** run the workflow against a labelled set of past cases before any change to the policy\ngoes live. The\nplatform is model agnostic, so the risk team can choose the model per workflow, with EU and UAE\ndata residency options.",[149,152,155,158],{"question":150,"answer":151},"How is order fraud screening different from card fraud scoring?","Authorization fraud scoring runs in milliseconds while the card authorization is still open, done by the issuer, the network, the acquirer, or a merchant side payment tool such as Stripe Radar. Order fraud screening also runs at checkout, but decides on the whole order, not just the card: it can pull in shipping, account and behavioural history alongside the payment data, and is often paired with a chargeback guarantee that shifts the fraud risk onto the vendor.",{"question":153,"answer":154},"How much can order fraud screening reduce chargebacks?","It depends heavily on the merchant's starting point and product mix, and on which product actually moved the number. Signifyd's own case study on the supplement brand Cymbiotika credits a chargeback reduction to Complete Chargeback Protection, a guarantee product that covers both fraud and non fraud chargebacks, not to the order screening decision on its own, so it is not a clean read on what screening by itself achieves for any specific business.",{"question":156,"answer":157},"Does a chargeback guarantee remove the retailer's own fraud risk entirely?","No. A guarantee only covers what its contract says: commonly fraud and, on some plans, \"item not received\" chargebacks on orders the vendor accepted. Check your own contract for carve outs such as policy abuse, promotion abuse or a cut off for filing a dispute, rather than assuming it covers every chargeback type.",{"question":159,"answer":160},"Can an online retailer decline an order by AI alone under GDPR?","A solely automated decision with a significant effect on a customer, which an outright decline can be, gives EU customers a right under Article 22 to ask for human review. In practice, offer a support channel so a declined customer can ask a person to look again, rather than routing every single decision through a human up front.",[162,163,164,165],"real-time-fraud-scoring","chargeback-and-representment","merchant-underwriting-and-risk-monitoring","application-and-identity-fraud-detection","2026-09-29",[168],{"date":166,"note":169},"First published","ecommerce-order-fraud-screening",[172,200],{"title":173,"useCases":174,"organization":175,"vendors":180,"summary":184,"stage":185,"year":186,"channels":187,"languages":188,"metrics":189,"outcomeDisclosed":177,"sources":190,"verification":195,"grade":197,"id":198,"organizationSlug":199},"Rainbow Shops: chargeback protection through a Shopify replatforming",[170],{"name":176,"anonymized":177,"country":178,"region":179,"industry":18},"Rainbow Shops",false,"US","north-america",[181],{"name":182,"role":183},"Signifyd","platform","Rainbow Shops is a US value apparel retailer for women and children. It ran a machine learning fraud tool that worked until it moved its checkout from Salesforce Commerce Cloud to Shopify and found its fraud provider had no strong Shopify integration. It switched to Signifyd for order screening, the chargeback guarantee and automated chargeback disputes, so its own staff no longer file disputes by hand.","production",2022,[],[],[],[191],{"url":192,"title":193,"publisher":182,"date":194},"https://www.signifyd.com/wp-content/uploads/frontify/case-study-rainbowshops-interactive.pdf","Signifyd customer case study: Rainbow Shops","2022-12-22",{"level":196,"checkedAt":166},"source-verified","C","rainbow-shops-chargeback-protection",null,{"title":201,"useCases":202,"organization":203,"vendors":205,"summary":207,"stage":185,"year":208,"channels":209,"languages":210,"metrics":211,"outcomeDisclosed":177,"sources":212,"verification":221,"grade":197,"id":222,"organizationSlug":199},"Cymbiotika: guaranteed fraud protection for a subscription supplement brand",[170],{"name":204,"anonymized":177,"region":179,"industry":18},"Cymbiotika",[206],{"name":182,"role":183},"Cymbiotika is a supplements and wellness brand on Shopify, founded in 2018, with a large share of recurring subscription orders. As order volume grew, false positives led to many legitimate subscriptions being cancelled, causing friction for loyal customers and driving hundreds of support inquiries a day. Cymbiotika became a Signifyd customer in 2021, using order screening to decide in real time whether to accept each order, plus Complete Chargeback Protection, which Signifyd's own site describes as cover against both fraud and non fraud chargebacks.",2021,[],[],[],[213,217],{"url":214,"title":215,"publisher":182,"date":216},"https://www.signifyd.com/customers/cymbiotika/","How Cymbiotika reduced chargebacks by 93% and boosted approvals to 98%","2024-12-29",{"url":218,"title":219,"publisher":182,"date":220},"https://www.signifyd.com/wp-content/uploads/frontify/did-4916-case-study-cymbiotika-interactive.pdf","Signifyd customer case study: Cymbiotika","2025-03-11",{"level":196,"checkedAt":166},"cymbiotika-guaranteed-fraud-protection",0,[],{"low":226,"high":227},48000,224000,[229,260,276,299],{"slug":162,"title":230,"shortTitle":231,"definition":232,"status":9,"industries":233,"functions":236,"patterns":237,"audience":28,"autonomy":29,"adoptionStage":30,"segment":238,"evidenceCount":239,"publicEvidenceCount":239,"organizations":240,"bestGrade":249,"headline":250,"lastVerified":258,"indexable":259},"Real time fraud scoring for card and instant payments","Real time fraud scoring","Machine learning that decides in milliseconds, without any conversation, how likely each card authorization and account to account payment is to be fraudulent, combining behavioural, device and network signals, so the bank can approve, challenge or block a payment before the money leaves. Working the resulting alerts and talking to the customer about them are separate use cases.",[234,235],"banking","payments",[20],[22,23],"middle-office",9,[241,242,243,244,245,246,247,248],"ANZ, Commonwealth Bank, NAB, Suncorp Bank and Westpac (BioCatch Trust Australia)","Commonwealth Bank of Australia","Mastercard","NatWest Group","Pay.UK","Revolut","Stripe","Visa","B",{"kpi":40,"label":251,"unit":252,"n":253,"nUpTo":223,"kind":254,"value":255,"qualifier":256,"claimant":257,"organization":199,"vendorReported":177},"Fraud loss reduction","percent",3,"median",30,"exact","organization","2026-09-27",true,{"slug":163,"title":261,"shortTitle":262,"definition":263,"status":9,"industries":264,"functions":265,"patterns":268,"audience":28,"autonomy":271,"adoptionStage":272,"segment":28,"evidenceCount":253,"publicEvidenceCount":273,"organizations":274,"bestGrade":249,"headline":199,"lastVerified":258,"indexable":259},"AI for chargeback and representment operations","Chargeback and representment","AI that runs the dispute engine room for issuers, acquirers and merchants: it maps each dispute to the network reason code, gathers the matching evidence, assembles a network compliant chargeback or representment package, drafts the rebuttal, tracks every deadline and processes pre dispute alerts so a refund can be issued before a chargeback lands.",[235,234,18],[266,20,267],"operations","customer-service",[25,269,270,24],"document-processing","content-generation","supervised-agent","early-adopters",2,[275,248],"GitHub",{"slug":164,"title":277,"shortTitle":278,"definition":279,"status":9,"industries":280,"functions":282,"patterns":285,"audience":28,"autonomy":271,"adoptionStage":272,"evidenceCount":287,"publicEvidenceCount":287,"organizations":288,"bestGrade":249,"headline":292,"lastVerified":258,"indexable":259},"AI for merchant underwriting and risk monitoring","Merchant underwriting and monitoring","AI that helps acquirers, payment facilitators and software platforms with embedded payments decide which merchants to accept and on what terms, by checking what a business really sells and how risky it is at onboarding, and then watches every active merchant for changes in behaviour, ranking the few that need an analyst so fraud, prohibited trade and credit losses are caught early.",[235,281,234],"technology",[283,20,284],"onboarding-and-kyc","risk-management",[22,23,24,286,25],"summarization",4,[289,290,248,291],"Airwallex","Tekmetric","Weave Communications",{"kpi":293,"label":294,"unit":252,"n":273,"nUpTo":223,"kind":295,"value":296,"qualifier":297,"claimant":298,"organization":291,"vendorReported":259},"alert-volume-reduction","Alert volume reduction","reported",89,"approximately","vendor",{"slug":165,"title":300,"shortTitle":301,"definition":302,"status":9,"industries":303,"functions":307,"patterns":309,"audience":28,"autonomy":271,"adoptionStage":272,"segment":311,"evidenceCount":312,"publicEvidenceCount":312,"organizations":313,"bestGrade":249,"headline":320,"lastVerified":326,"indexable":259},"AI for application and identity fraud detection","Application and identity fraud","AI that checks incoming account and loan applications for forged or AI generated documents, synthetic and stolen identities, and coordinated application rings, by analysing documents, device and application data across the whole queue and cross checking against bureau and official sources.",[234,235,304,305,306],"cross-industry","government","telecommunications",[20,283,308],"lending-and-credit",[269,23,310,22],"computer-vision","front-office",6,[314,315,316,317,318,319],"BCU","Close Brothers Motor Finance","CNG Holdings","Department for Work and Pensions","Payoneer","Telstra",{"kpi":321,"label":322,"unit":323,"n":324,"nUpTo":223,"kind":295,"value":325,"qualifier":256,"claimant":257,"organization":317,"vendorReported":177},"detection-rate-improvement","Detection improvement","multiplier",1,2.5,"2026-09-26",{"indexable":259,"reasons":328},[],[330,337,342,350,357,364,370,377,385,392,399,405,411,417,424,431,437,444,450,456,462,469,474,481,486,491,496,501,508,513,521,527,533,539,544,549],{"id":331,"label":332,"issuer":333,"region":138,"url":334,"description":335,"useCases":336,"indexable":259},"eu-ai-act","EU AI Act","European Union","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.",230,{"id":132,"label":338,"issuer":333,"region":138,"url":339,"description":340,"useCases":341,"indexable":259},"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.",207,{"id":343,"label":344,"issuer":345,"region":346,"url":347,"description":348,"useCases":349,"indexable":259},"iso-42001","ISO/IEC 42001","ISO and IEC","global","https://www.iso.org/standard/81230.html","The international management system standard for AI.",122,{"id":351,"label":352,"issuer":353,"region":179,"url":354,"description":355,"useCases":356,"indexable":259},"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.",92,{"id":358,"label":359,"issuer":360,"region":138,"url":361,"description":362,"useCases":363,"indexable":259},"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.",71,{"id":365,"label":366,"issuer":333,"region":138,"url":367,"description":368,"useCases":369,"indexable":259},"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":371,"label":372,"issuer":373,"region":138,"url":374,"description":375,"useCases":376,"indexable":259},"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.",50,{"id":378,"label":379,"issuer":380,"region":381,"url":382,"description":383,"useCases":384,"indexable":259},"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.",37,{"id":386,"label":387,"issuer":388,"region":381,"url":389,"description":390,"useCases":391,"indexable":259},"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":393,"label":394,"issuer":395,"region":179,"url":396,"description":397,"useCases":398,"indexable":259},"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.",22,{"id":133,"label":400,"issuer":401,"region":346,"url":402,"description":403,"useCases":404,"indexable":259},"PCI DSS","PCI Security Standards Council","https://www.pcisecuritystandards.org/","Security standard for any system that stores, processes or transmits cardholder data.",21,{"id":406,"label":407,"issuer":333,"region":138,"url":408,"description":409,"useCases":410,"indexable":259},"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.",17,{"id":412,"label":413,"issuer":414,"region":138,"url":415,"description":416,"useCases":410,"indexable":259},"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.",{"id":418,"label":419,"issuer":420,"region":179,"url":421,"description":422,"useCases":423,"indexable":259},"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.",16,{"id":425,"label":426,"issuer":427,"region":346,"url":428,"description":429,"useCases":430,"indexable":259},"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":432,"label":433,"issuer":333,"region":138,"url":434,"description":435,"useCases":436,"indexable":259},"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":438,"label":439,"issuer":440,"region":179,"url":441,"description":442,"useCases":443,"indexable":259},"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":445,"label":446,"issuer":447,"region":179,"url":448,"description":449,"useCases":443,"indexable":259},"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":451,"label":452,"issuer":333,"region":138,"url":453,"description":454,"useCases":455,"indexable":259},"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":457,"label":458,"issuer":459,"region":346,"url":460,"description":461,"useCases":455,"indexable":259},"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":463,"label":464,"issuer":465,"region":179,"url":466,"description":467,"useCases":468,"indexable":259},"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.",11,{"id":470,"label":471,"issuer":333,"region":138,"url":472,"description":473,"useCases":468,"indexable":259},"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.",{"id":475,"label":476,"issuer":477,"region":138,"url":478,"description":479,"useCases":480,"indexable":259},"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.",10,{"id":482,"label":483,"issuer":380,"region":381,"url":484,"description":485,"useCases":480,"indexable":259},"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.",{"id":487,"label":488,"issuer":333,"region":138,"url":489,"description":490,"useCases":480,"indexable":259},"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":492,"label":493,"issuer":333,"region":138,"url":494,"description":495,"useCases":480,"indexable":259},"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":497,"label":498,"issuer":333,"region":138,"url":499,"description":500,"useCases":239,"indexable":259},"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":502,"label":503,"issuer":504,"region":179,"url":505,"description":506,"useCases":507,"indexable":259},"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.",7,{"id":509,"label":510,"issuer":333,"region":138,"url":511,"description":512,"useCases":312,"indexable":259},"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":514,"label":515,"issuer":516,"region":517,"url":518,"description":519,"useCases":520,"indexable":259},"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.",5,{"id":522,"label":523,"issuer":524,"region":138,"url":525,"description":526,"useCases":287,"indexable":259},"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":528,"label":529,"issuer":530,"region":138,"url":531,"description":532,"useCases":287,"indexable":259},"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":534,"label":535,"issuer":536,"region":381,"url":537,"description":538,"useCases":253,"indexable":259},"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":540,"label":541,"issuer":333,"region":138,"url":542,"description":543,"useCases":253,"indexable":259},"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":545,"label":546,"issuer":333,"region":138,"url":547,"description":548,"useCases":253,"indexable":259},"eu-mortgage-credit-directive","EU Mortgage Credit Directive","https://eur-lex.europa.eu/eli/dir/2014/17/oj","Directive 2014/17/EU: creditworthiness assessment, disclosure and advice rules for residential mortgage lending.",{"id":550,"label":551,"issuer":552,"region":179,"url":553,"description":554,"useCases":253,"indexable":259},"nyc-local-law-144","NYC Local Law 144","New York City","https://www.nyc.gov/site/dca/about/automated-employment-decision-tools.page","Bias audits and notices for automated employment decision tools used in hiring and promotion in New York City.",1790683492446]