[{"data":1,"prerenderedAt":722},["ShallowReactive",2],{"uc-application-and-identity-fraud-detection":3,"uc-regulations":521},{"useCase":4,"evidence":220,"blitsAiDeployments":392,"benchmarks":393,"indicative":400,"related":403,"indexability":519,"includeUnpublished":227},{"title":5,"shortTitle":6,"seoTitle":7,"metaDescription":8,"status":9,"definition":10,"aliases":11,"industries":17,"functions":23,"patterns":27,"channels":32,"audience":35,"autonomy":36,"adoptionStage":37,"segment":38,"problem":39,"problemStats":40,"howItWorks":51,"valueDrivers":52,"kpis":57,"indicativeValue":63,"macroEstimates":98,"feasibility":99,"implementation":113,"risk":153,"blitsAi":195,"faq":197,"related":207,"datePublished":214,"dateModified":214,"lastVerified":215,"changelog":216,"slug":219},"AI for application and identity fraud detection","Application and identity fraud","Application and identity fraud detection with AI","AI checks loan and account applications for forged documents and synthetic identities before approval. Close Brothers Motor Finance, BCU and CNG Holdings use it.","published","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.",[12,13,14,15,16],"application fraud detection","synthetic identity detection","document fraud detection","fake pay slip and bank statement detection","first party fraud detection at onboarding",[18,19,20,21,22],"banking","payments","cross-industry","government","telecommunications",[24,25,26],"fraud-prevention","onboarding-and-kyc","lending-and-credit",[28,29,30,31],"document-processing","anomaly-detection","computer-vision","prediction-and-scoring",[33,34],"api","internal-tools","back-office","supervised-agent","early-adopters","front-office","Lenders and banks decide on applications from documents and data the applicant provides: pay\nslips, bank statements, tax forms, identity documents and a selfie. Editing tools and now\ngenerative AI make convincing forgeries cheap, and synthetic identities built from real and\ninvented data can pass individual checks and build a credit history before they default.\n\nManual document review is slow and inconsistent, and reviewers cannot see metadata manipulation\nor the pattern across many applications, such as the same device, the same template or similar\nemail addresses. Blunt rules, on the other hand, turn away genuine applicants and slow down the\ndigital onboarding that customers expect. The same problem exists outside banking wherever\ndocuments prove eligibility: insurance, telecom contracts, rentals and public benefits.",[41,46],{"statement":42,"sourceTitle":43,"sourceUrl":44,"year":45},"FinCEN reported an increase in suspicious activity reports describing suspected deepfake media, particularly fraudulent identity documents used to get past identity verification, and issued an alert with red flag indicators.","FinCEN Issues Alert on Fraud Schemes Involving Deepfake Media Targeting Financial Institutions","https://www.fincen.gov/news/news-releases/fincen-issues-alert-fraud-schemes-involving-deepfake-media-targeting-financial",2024,{"statement":47,"sourceTitle":48,"sourceUrl":49,"year":50},"In a Feedzai survey of 562 fraud and financial crime professionals at financial institutions, 92% of the institutions said fraudsters use generative AI.","AI Fraud Trends 2025: Banks Fight Back","https://www.feedzai.com/pressrelease/ai-fraud-trends-2025/",2025,"1. **Read the documents.** Document AI extracts the data from pay slips, statements and identity\n   documents and checks each file for tampering: metadata, fonts, layout against known templates,\n   arithmetic that does not add up, and signs of generation.\n2. **Check the identity.** Identity data is verified against bureau and official sources, the\n   identity document is checked for authenticity, and a liveness check confirms the applicant is\n   present and matches the document.\n3. **Look across the queue.** Anomaly and graph models link applications that share devices, IP\n   addresses, contact details, employers or document templates, exposing rings and synthetic\n   identity farms.\n4. **Score and explain.** Signals combine into a fraud risk score with the reasons behind it,\n   separate from the credit decision.\n5. **Route, do not reject.** Clean applications flow straight through; flagged ones go to a\n   fraud analyst with the evidence, who decides whether to request more information, verify\n   directly with the employer or bank, or decline.",[53,54,55,56],"risk-reduction","speed","customer-experience","cost-to-serve",[58,59,60,61,62],"fraud-loss-reduction","detection-rate-improvement","automation-rate","processing-time-reduction","false-positive-reduction",{"referenceOrg":64,"inputs":65,"formula":93,"currency":94,"period":95,"resultLabel":96,"caveat":97},"A consumer lender processing 100,000 applications a year",[66,72,79,86],{"key":67,"label":68,"low":69,"high":69,"unit":70,"note":71},"applications","Applications per year",100000,"applications per year","The reference lender.",{"key":73,"label":74,"low":75,"high":76,"unit":77,"note":78},"fraudRate","Share of applications that are fraudulent and would be approved today",0.002,0.005,"fraction of applications","Editorial assumption. Replace with your own confirmed application fraud rate.",{"key":80,"label":81,"low":82,"high":83,"unit":84,"note":85},"lossPerFraud","Average loss per approved fraudulent application",3000,8000,"USD per case","Editorial assumption. Replace with your own charge off data.",{"key":87,"label":88,"low":89,"high":90,"unit":91,"note":92},"extraCaught","Additional share of that fraud caught before approval",0.2,0.4,"fraction of fraudulent applications","Editorial assumption, far below the vendor reported result on this page (CNG Holdings saw a greater than 80% drop in third party fraud within 90 days). Replace with results from a back test on your own confirmed fraud.","applications * fraudRate * lossPerFraud * extraCaught","USD","per year","Application fraud losses avoided","Leaves out manual review time saved, faster approval for genuine applicants, lost revenue from wrongly declined applicants, and the cost of data sources and the platform.",[],{"complexity":100,"complexityNote":101,"dataPrerequisites":102,"integrations":107},"medium","Document and identity checks are available as services and integrate through APIs. The work is in combining signals across the application queue, feeding back confirmed fraud, and keeping fraud flags separate from credit decisions.",[103,104,105,106],"Confirmed application fraud and synthetic identity cases linked to the original applications","Application, device and session data for every application","Document images or files in their original format, not only extracted fields","Access to bureau, identity verification and, where available, official data sources",[108,109,110,111,112],"Loan origination or account opening system","Identity verification and liveness service","Credit bureau and fraud data sharing schemes","Device intelligence","Fraud case management",{"steps":114,"guardrails":130,"humanInTheLoop":136,"kpisToInstrument":137,"failureModes":143},[115,118,121,124,127],{"title":116,"detail":117},"Collect the evidence you already have","Link past confirmed fraud and early defaults with no payments to their applications, and keep original document files. This is your test set.",{"title":119,"detail":120},"Add document forensics to the existing flow","Run document checks on every uploaded file in shadow mode, measure what they catch against the test set, and tune thresholds before they affect any applicant.",{"title":122,"detail":123},"Look across applications","Link applications by device, contact details, employer and document template to find rings that single application checks miss.",{"title":125,"detail":126},"Route flagged applications to people","Send flags to fraud analysts with the evidence, and give them fast ways to verify, such as open banking data or direct employer confirmation.",{"title":128,"detail":129},"Keep fraud and credit separate","Document that a fraud flag leads to investigation, not an automatic credit decline, and report false positive rates to the risk committee.",[131,132,133,134,135],"A fraud flag triggers investigation or verification, never an automatic decline on its own","Reasons for every flag stored and available to the analyst","False positive rates monitored across customer groups to avoid unfair outcomes","Liveness and biometric checks used only for one to one verification with consent","Confirmed outcomes fed back to keep models current as generation tools improve","Fraud analysts decide every flagged application. Credit decisions stay in the credit process. The fraud strategy owner approves thresholds, and the risk committee receives false positive and performance reports.",[138,139,140,141,142],"Application fraud losses and first payment defaults, normalised for volume","Share of confirmed fraud flagged before approval","Share of genuine applicants flagged, and time to clear them","Straight through approval rate for clean applications","Rings detected and applications linked per ring",[144,147,150],{"title":145,"detail":146},"The arms race","Generation tools improve faster than template checks. Combine document forensics with data verification at the source and network signals.",{"title":148,"detail":149},"Fraud flag as a hidden decline","Flags quietly turn into declines, creating fair lending and adverse action exposure. Separate the processes and audit outcomes.",{"title":151,"detail":152},"Punishing thin files","Young people and newcomers look like synthetic identities. Test false positive rates on these groups and provide alternative verification.",{"euAiAct":154,"regulations":157,"guidance":168,"controls":184,"incidents":190},{"tier":155,"basis":156},"context-dependent","Annex III point 5(b) excludes AI used to detect financial fraud from the high risk credit scoring category, but a system that in effect decides on creditworthiness is high risk, and remote biometric identification is high risk under point 1(a), which excludes one to one biometric verification. When a public authority uses the model on claims for public benefits, point 5(a) can apply, because it covers AI used to grant, reduce, revoke or reclaim benefits and has no fraud exception. Keep fraud detection separate from the credit or eligibility decision and use biometrics only for one to one verification.",[158,159,160,161,162,163,164,165,166,167],"eu-ai-act","gdpr","eba-loan-origination","fatf-recommendations","us-sr-11-7","uk-consumer-duty","nist-ai-rmf","eu-amlr","us-bsa","us-fcra",[169,175,180],{"title":170,"issuer":171,"region":172,"url":173,"note":174},"Annex III: High-Risk AI Systems Referred to in Article 6(2)","European Union","europe","https://artificialintelligenceact.eu/annex/3/","Point 5(b) excludes fraud detection from high risk credit scoring; point 5(a) covers public benefits decisions; point 1(a) covers remote biometric identification, excluding one to one verification.",{"title":176,"issuer":177,"region":172,"url":178,"note":179},"Guidelines on the use of remote customer onboarding solutions","European Banking Authority","https://www.eba.europa.eu/regulation-and-policy/anti-money-laundering-and-countering-financing-terrorism/guidelines-use-remote-customer-onboarding-solutions","Sets expectations for the reliability of remote onboarding solutions, including checks that identity documents are genuine and not tampered with, and strong and reliable algorithms for biometric matching.",{"title":43,"issuer":181,"region":182,"url":44,"note":183},"Financial Crimes Enforcement Network (FinCEN)","north-america","Describes typologies and red flag indicators for deepfake media, particularly fraudulent identity documents used to get past identity verification, and reminds institutions of their reporting duties under the Bank Secrecy Act.",[185,186,187,188,189],"Documented separation between fraud flags and credit decisions","Stored reasons for every flag and analyst decision","Fairness monitoring of flag rates across customer groups","Consent and data protection impact assessment for biometric checks","Model inventory entry, validation and drift monitoring",[191],{"title":192,"url":193,"note":194},"Revealed: bias found in AI system used to detect UK benefits fraud","https://www.theguardian.com/society/2024/dec/06/revealed-bias-found-in-ai-system-used-to-detect-uk-benefits","The Guardian reported in December 2024 that an internal fairness assessment of the UK Department for Work and Pensions' machine learning model for Universal Credit advance claims found it selected people for fraud investigation at different rates by age, disability, marital status and nationality. Staff make the final decision, but the case shows why flag rates need fairness monitoring.",{"howToBuild":196},"Document forensics and identity verification usually come from specialist services, called\nthrough **custom functions**. Blits.ai adds the orchestration and the human side: an **agentic\nworkflow**, triggered through the API for each application, collects the verification results,\ncompares the documents' extracted data with the application and bureau data, checks the\n**SQL knowledge base** of previous applications for shared details, and returns **structured\noutput** with a risk summary and reasons.\n\nFlagged applications go to an analyst through **human in the loop approval**. When the\napplicant needs to provide more, a **conversational agent** on **web chat**, **WhatsApp** or the\nlender's own app through the **API channel** asks for the specific document and accepts **file uploads**, with **PII masking**,\n**guardrails** and a **human handover** for anything sensitive. Every run keeps an audit trail,\nand the platform is model agnostic with EU and UAE data residency.",[198,201,204],{"question":199,"answer":200},"Can AI detect AI generated pay slips and bank statements?","Often, by combining signals: file metadata, template and font analysis, arithmetic consistency and, most reliably, checking the data against the source through open banking or the employer. No single check is enough as generation tools improve.",{"question":202,"answer":203},"Should a fraud flag decline the application?","No. A flag should lead to verification or investigation. Automatic declines based on fraud scores create fair lending and adverse action exposure and turn away genuine applicants.",{"question":205,"answer":206},"Is application fraud detection high risk under the EU AI Act?","Fraud detection is excluded from the high risk credit scoring category, but the design matters. If the fraud score in effect decides credit, if you use remote biometric identification rather than one to one verification, or if a public authority uses it to decide on benefits, it can become high risk.",[208,209,210,211,212,213],"digital-onboarding-assistant","real-time-fraud-scoring","mule-network-detection","conversational-loan-application-intake","intelligent-document-processing","alternative-data-credit-scoring","2026-09-27","2026-09-26",[217],{"date":214,"note":218},"First published","application-and-identity-fraud-detection",[221,263,291,319,342,374],{"title":222,"useCases":223,"organization":225,"vendors":229,"summary":233,"stage":234,"year":235,"channels":236,"languages":237,"metrics":239,"outcomeDisclosed":249,"sources":250,"verification":258,"grade":260,"id":261,"organizationSlug":262},"Department for Work and Pensions: machine learning risk model for Universal Credit advances",[224,219],"benefit-fraud-and-error-detection",{"name":226,"anonymized":227,"country":228,"region":172,"industry":21},"Department for Work and Pensions",false,"GB",[230],{"name":231,"role":232},"In house (Integrated Risk and Intelligence Service)","in-house","DWP scores requests for Universal Credit advances in real time with a supervised machine learning classifier and refers the highest risk requests to a caseworker before payment. The caseworker is not told the referral came from the model, a random control group is referred alongside, and every decision to decline is made by a person and can be appealed. DWP's published effectiveness assessment for April 2025 to March 2026 finds the model 2.5 times more effective than random selection with a median payment delay of one day for approved referrals. It also finds that non UK nationals and several age bands were referred more often without a matching increase in confirmed fraud, and that referrals of couples were less often confirmed than those of single claimants; a retrained model was being tested.","scaled",2026,[33],[238],"en",[240],{"kpi":59,"value":241,"unit":242,"qualifier":243,"period":244,"baseline":245,"claimant":246,"quote":247,"sourceUrl":248},2.5,"multiplier","exact","1 April 2025 to 31 March 2026","Randomised control group sample of advances","organization","The performance information for 2025 to 2026 demonstrates the model is 2.5 times more effective at identifying fraud risk than a randomised control group sample.","https://www.gov.uk/government/publications/effectiveness-assessment-including-statistical-analysis-of-the-universal-credit-advances-machine-learning-model-1-april-2025-to-31-march-2026/effectiveness-assessment-of-universal-credit-advances-model",true,[251,254],{"url":248,"title":252,"publisher":226,"date":253},"Effectiveness Assessment of Universal Credit Advances Model","2026-07-09",{"url":255,"title":256,"publisher":257},"https://www.gov.uk/algorithmic-transparency-records/dwp-universal-credit-advances-model","DWP: Universal Credit Advances Model (algorithmic transparency record)","GOV.UK",{"level":259,"checkedAt":215},"source-verified","B","dwp-universal-credit-advances-fraud-model",null,{"title":264,"useCases":265,"organization":267,"vendors":271,"summary":274,"stage":275,"year":50,"channels":276,"languages":277,"metrics":278,"outcomeDisclosed":249,"sources":279,"verification":288,"grade":260,"id":289,"organizationSlug":290},"Telstra and CommBank: Scam Indicator and Fraud Indicator built on mobile network intelligence",[266,219],"telecom-fraud-detection",{"name":268,"anonymized":227,"country":269,"region":270,"industry":22},"Telstra","AU","asia-pacific",[272],{"name":273,"role":232},"Quantium Telstra","Quantium Telstra built two services in collaboration with Commonwealth Bank. Scam Indicator detects and intercepts suspected scam calls to bank customers in real time and was later extended to landlines. Fraud Indicator, live from early 2025, securely shares intelligence about unusual mobile service usage so the bank can spot fraudsters opening accounts with a phone number they control. Telstra describes the Scam and Fraud Indicator as using AI and says it has safeguarded thousands of customers and prevented millions of dollars in fraud since 2023; the expected gain in detection of fraudulent accounts was published as a forecast.","production",[33],[238],[],[280,284],{"url":281,"title":282,"publisher":268,"date":283},"https://www.telstra.com.au/exchange/telstra-and-commbank-expand-collaboration-to-increase-fraud-dete","Telstra and CommBank expand collaboration to increase fraud detection rates","2025-02-10",{"url":285,"title":286,"publisher":268,"date":287},"https://www.telstra.com.au/exchange/telstra-s-ai-transformation--strategy--partnerships-and-real-wor","Telstra's AI transformation: strategy, partnerships and real-world results","2026-04-20",{"level":259,"checkedAt":214},"telstra-quantium-fraud-indicator","telstra",{"title":292,"useCases":293,"organization":294,"vendors":297,"summary":301,"stage":275,"year":50,"channels":302,"languages":303,"metrics":304,"outcomeDisclosed":249,"sources":313,"verification":316,"grade":317,"id":318,"organizationSlug":262},"BCU: AI document fraud detection in lending and account opening with Inscribe",[219],{"name":295,"anonymized":227,"country":296,"region":182,"industry":18},"BCU","US",[298],{"name":299,"role":300},"Inscribe","platform","BCU, a US credit union, uses Inscribe's document fraud detection on loan documents and member account applications to find altered bank statements, pay stubs and other documents. Its investigators used the signals to uncover fraud rings, synthetic identities and reused document templates. The vendor reports USD 5.6 million in losses from altered documents prevented in the first nine months of 2025.",[33,34],[238],[305],{"kpi":306,"value":307,"unit":308,"currency":94,"qualifier":243,"period":309,"claimant":310,"quote":311,"sourceUrl":312},"fraud-losses-prevented",5600000,"currency","first nine months of 2025","vendor","In the first nine months of 2025, BCU prevented $5.6 million in losses from altered documents and has now saved $80 million total!","https://www.inscribe.ai/customers/bcu",[314],{"url":312,"title":315,"publisher":299},"BCU Success Story",{"level":259,"checkedAt":215},"C","bcu-inscribe-document-fraud-detection",{"title":320,"useCases":321,"organization":322,"vendors":324,"summary":327,"stage":275,"year":45,"channels":328,"languages":329,"metrics":330,"outcomeDisclosed":249,"sources":337,"verification":340,"grade":317,"id":341,"organizationSlug":262},"Close Brothers Motor Finance: AI document fraud detection on finance applications with Resistant AI",[219],{"name":323,"anonymized":227,"country":228,"region":172,"industry":18},"Close Brothers Motor Finance",[325],{"name":326,"role":300},"Resistant AI","Close Brothers Motor Finance added Resistant AI's document forensics to its motor finance application process in August 2024, after a trial that surfaced 18 further fraud cases. Underwriters get a document fraud check, for example on company bank statements, in 12 seconds instead of a 15 minute manual assessment. The vendor reports fraud losses prevented, a high return on investment and faster application reviews.",[34],[238],[331],{"kpi":306,"value":332,"unit":308,"currency":333,"qualifier":243,"period":334,"claimant":310,"quote":335,"sourceUrl":336},800000,"GBP","first 8 months after implementation","£800,000 in fraud losses prevented in 8 months.","https://resistant.ai/case-studies/close-brothers",[338],{"url":336,"title":339,"publisher":326},"Close Brothers",{"level":259,"checkedAt":215},"close-brothers-resistant-ai-document-fraud",{"title":343,"useCases":344,"organization":345,"vendors":347,"summary":352,"stage":234,"year":353,"channels":354,"languages":355,"metrics":356,"outcomeDisclosed":249,"sources":365,"verification":372,"grade":317,"id":373,"organizationSlug":262},"CNG Holdings: identity first application fraud prevention with SAS",[219],{"name":346,"anonymized":227,"country":296,"region":182,"industry":18},"CNG Holdings",[348,350],{"name":349,"role":300},"SAS",{"name":351,"role":300},"Microsoft Azure","CNG Holdings, a US consumer lender with online lending and around 1,000 retail stores, runs SAS fraud decisioning with machine learning on Microsoft Azure to verify applicants' identities in real time and stop third party and synthetic identity fraud at application. SAS reports a steep drop in third party fraud within 90 days, a very low fraud false positive rate and lower fraud programme costs after CNG retired several older tools.",2023,[33],[238],[357],{"kpi":358,"value":359,"unit":360,"qualifier":361,"period":362,"claimant":310,"quote":363,"sourceUrl":364},"cost-reduction",30,"percent","at-least","fraud programme costs","By replacing a disjointed patchwork of expensive and ineffective fraud tools with SAS’ integrated defenses, Cooney estimates that CNG cut its fraud program costs more than 30%.","https://www.sas.com/en_us/news/press-releases/2023/june/cng-holdings-zaps-fraud.html",[366,369],{"url":364,"title":367,"publisher":349,"date":368},"CNG Holdings zaps third-party and synthetic fraud with 'identity-first' fraud prevention","2023-06-13",{"url":370,"title":371,"publisher":349},"https://www.sas.com/en_us/customers/cng-holdings.html","Identity management is key to preventing credit fraud",{"level":259,"checkedAt":215},"cng-holdings-sas-identity-fraud",{"title":375,"useCases":376,"organization":377,"vendors":380,"summary":382,"stage":234,"year":353,"channels":383,"languages":384,"metrics":385,"outcomeDisclosed":249,"sources":386,"verification":390,"grade":317,"id":391,"organizationSlug":262},"Payoneer: AI document forensics in customer onboarding with Resistant AI",[219],{"name":378,"anonymized":227,"country":296,"region":379,"industry":19},"Payoneer","global",[381],{"name":326,"role":300},"Cross border payments platform Payoneer added Resistant AI's document forensics to its intelligent document processing in onboarding, to detect fake documents and serial fraud attempts while keeping onboarding fast. Resistant AI says it now supports more than 82% of Payoneer's document fraud decision making, with only edge cases escalated for manual review.",[33],[238],[],[387],{"url":388,"title":378,"publisher":326,"archivedUrl":389},"https://resistant.ai/case-studies/payoneer","https://web.archive.org/web/20230205042853/https://resistant.ai/case-studies/payoneer/",{"level":259,"checkedAt":215},"payoneer-resistant-ai-document-forensics",0,[394],{"kpi":59,"label":395,"unit":242,"aggregate":249,"higherIsBetter":249,"n":396,"nUpTo":392,"median":241,"min":241,"max":241,"byClaimant":397,"vendorOnly":227,"points":398},"Detection improvement",1,{"organization":396,"vendor":392,"regulator":392,"independent":392},[399],{"evidenceId":261,"organization":226,"value":241,"qualifier":243,"claimant":246,"grade":260,"pooled":249},{"low":401,"high":402},120000,1600000,[404,427,450,463,479,503],{"slug":208,"title":405,"shortTitle":406,"definition":407,"status":9,"industries":408,"functions":410,"patterns":413,"audience":416,"autonomy":36,"adoptionStage":37,"segment":38,"evidenceCount":417,"publicEvidenceCount":418,"organizations":419,"bestGrade":317,"headline":423,"lastVerified":214,"indexable":249},"AI assistant for digital account onboarding and KYC","Digital onboarding","A customer facing AI assistant that guides a new applicant, a person or a small merchant, through a digital account, card or relationship application: it collects and checks identity and supporting documents, orchestrates the know your customer and anti money laundering checks, prefills what it can and sends only the unclear cases to a human reviewer with a summary. The ownership research for complex corporate clients is a separate back office job.",[18,19,409],"wealth-and-asset-management",[25,411,412],"sales","customer-service",[414,28,30,415],"conversational-agent","agentic-workflow","customer-facing",6,3,[420,421,422],"Albo","Deutsche Bank","M-DAQ Global",{"kpi":424,"label":425,"unit":242,"n":396,"nUpTo":392,"kind":426,"value":359,"qualifier":243,"claimant":310,"organization":422,"vendorReported":249},"productivity-gain","Productivity gain","reported",{"slug":209,"title":428,"shortTitle":429,"definition":430,"status":9,"industries":431,"functions":432,"patterns":433,"audience":35,"autonomy":434,"adoptionStage":435,"segment":436,"evidenceCount":437,"publicEvidenceCount":437,"organizations":438,"bestGrade":260,"headline":447,"lastVerified":214,"indexable":249},"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.",[18,19],[24],[31,29],"autonomous","mainstream","middle-office",9,[439,440,441,442,443,444,445,446],"ANZ, Commonwealth Bank, NAB, Suncorp Bank and Westpac (BioCatch Trust Australia)","Commonwealth Bank of Australia","Mastercard","NatWest Group","Pay.UK","Revolut","Stripe","Visa",{"kpi":58,"label":448,"unit":360,"n":418,"nUpTo":392,"kind":449,"value":359,"qualifier":243,"claimant":246,"organization":262,"vendorReported":227},"Fraud loss reduction","median",{"slug":210,"title":451,"shortTitle":452,"definition":453,"status":9,"industries":454,"functions":455,"patterns":457,"audience":35,"autonomy":459,"adoptionStage":37,"segment":436,"evidenceCount":418,"publicEvidenceCount":418,"organizations":460,"bestGrade":260,"headline":262,"lastVerified":214,"indexable":249},"AI for money mule account and network detection","Mule network detection","Graph and behavioural machine learning that finds money mule accounts and the networks around them, such as circular flows, layering chains and clusters of newly linked accounts, and supports investigators in tracing scam proceeds and restricting accounts before the money is gone.",[18,19],[24,456],"financial-crime-compliance",[29,31,415,458],"summarization","copilot",[461,439,462],"BigPay","Reserve Bank Innovation Hub (Reserve Bank of India)",{"slug":211,"title":464,"shortTitle":465,"definition":466,"status":9,"industries":467,"functions":468,"patterns":469,"audience":416,"autonomy":36,"adoptionStage":37,"segment":38,"evidenceCount":417,"publicEvidenceCount":472,"organizations":473,"bestGrade":317,"headline":262,"lastVerified":214,"indexable":249},"Conversational AI for loan application intake","Loan application intake","A conversational assistant on web, app, messaging or voice that explains loan products, captures the application through dialogue in the customer's language, checks documents and basic eligibility rules, and hands a complete, structured application to origination, without making the credit decision.",[18],[26,411,412],[414,28,470,471],"rag-knowledge-assistant","voice-agent",5,[474,475,476,477,478],"Absa Bank","Figure","Lloyds Banking Group","Oper Credits","Rocket Mortgage",{"slug":212,"title":480,"shortTitle":481,"definition":482,"status":9,"industries":483,"functions":486,"patterns":490,"audience":35,"autonomy":36,"adoptionStage":435,"evidenceCount":492,"publicEvidenceCount":472,"organizations":493,"bestGrade":260,"headline":499,"lastVerified":214,"indexable":249},"AI document intelligence for unstructured forms and documents","Intelligent document processing","AI that takes documents in any format, such as scanned forms, PDFs, photos, emails and handwritten notes, splits and classifies them, extracts the required fields with a confidence score, validates them against business rules and source systems, and sends only the uncertain cases to a person before the data enters the downstream process.",[20,21,484,485],"automotive","manufacturing",[487,488,489],"operations","case-management","finance-and-accounting",[28,30,491],"classification-and-routing",7,[494,495,496,497,498],"Ancine","Pupuk Indonesia","U.S. Immigration and Customs Enforcement","U.S. Citizenship and Immigration Services","Volvo Group",{"kpi":500,"label":501,"unit":360,"n":396,"nUpTo":392,"kind":426,"value":502,"qualifier":361,"claimant":310,"organization":494,"vendorReported":249},"accuracy","Accuracy",90,{"slug":213,"title":504,"shortTitle":505,"definition":506,"status":9,"industries":507,"functions":508,"patterns":511,"audience":35,"autonomy":36,"adoptionStage":37,"segment":512,"evidenceCount":472,"publicEvidenceCount":472,"organizations":513,"bestGrade":260,"headline":262,"lastVerified":215,"indexable":249},"AI credit scoring with alternative data for thin file applicants","Alternative data credit scoring","A machine learning credit model that adds consumer permissioned alternative data, such as bank account cash flow, rent, utility and telco payments or ecosystem data, to credit bureau data, so a lender can assess applicants with thin or no credit files and return a decision with specific reasons.",[18,19],[26,509,510],"underwriting","risk-management",[31,28,414],"lending",[514,515,516,517,518],"Atlanticus","Golden 1 Credit Union","GXS Bank","Patelco Credit Union","Upstart Network",{"indexable":249,"reasons":520},[],[522,527,532,539,545,551,558,564,571,578,585,590,597,603,608,613,619,625,631,637,643,649,655,660,665,669,676,681,686,693,700,706,712,717],{"id":158,"label":523,"issuer":171,"region":172,"url":524,"description":525,"useCases":526,"indexable":249},"EU AI Act","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":159,"label":528,"issuer":171,"region":172,"url":529,"description":530,"useCases":531,"indexable":249},"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":533,"label":534,"issuer":535,"region":379,"url":536,"description":537,"useCases":538,"indexable":249},"iso-42001","ISO/IEC 42001","ISO and IEC","https://www.iso.org/standard/81230.html","The international management system standard for AI.",110,{"id":164,"label":540,"issuer":541,"region":182,"url":542,"description":543,"useCases":544,"indexable":249},"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":546,"label":547,"issuer":171,"region":172,"url":548,"description":549,"useCases":550,"indexable":249},"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":552,"label":553,"issuer":554,"region":172,"url":555,"description":556,"useCases":557,"indexable":249},"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":163,"label":559,"issuer":560,"region":172,"url":561,"description":562,"useCases":563,"indexable":249},"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":565,"label":566,"issuer":567,"region":270,"url":568,"description":569,"useCases":570,"indexable":249},"mas-ai-risk-management","MAS AI risk management guidelines","Monetary Authority of Singapore","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":572,"label":573,"issuer":574,"region":270,"url":575,"description":576,"useCases":577,"indexable":249},"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":579,"label":580,"issuer":581,"region":379,"url":582,"description":583,"useCases":584,"indexable":249},"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":162,"label":586,"issuer":587,"region":182,"url":588,"description":589,"useCases":584,"indexable":249},"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":591,"label":592,"issuer":593,"region":172,"url":594,"description":595,"useCases":596,"indexable":249},"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":161,"label":598,"issuer":599,"region":379,"url":600,"description":601,"useCases":602,"indexable":249},"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":165,"label":604,"issuer":171,"region":172,"url":605,"description":606,"useCases":607,"indexable":249},"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":609,"label":610,"issuer":171,"region":172,"url":611,"description":612,"useCases":607,"indexable":249},"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":166,"label":614,"issuer":615,"region":182,"url":616,"description":617,"useCases":618,"indexable":249},"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":620,"label":621,"issuer":171,"region":172,"url":622,"description":623,"useCases":624,"indexable":249},"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":626,"label":627,"issuer":628,"region":182,"url":629,"description":630,"useCases":624,"indexable":249},"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":632,"label":633,"issuer":634,"region":379,"url":635,"description":636,"useCases":624,"indexable":249},"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":638,"label":639,"issuer":171,"region":172,"url":640,"description":641,"useCases":642,"indexable":249},"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":644,"label":645,"issuer":646,"region":182,"url":647,"description":648,"useCases":642,"indexable":249},"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":650,"label":651,"issuer":567,"region":270,"url":652,"description":653,"useCases":654,"indexable":249},"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":656,"label":657,"issuer":171,"region":172,"url":658,"description":659,"useCases":654,"indexable":249},"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":661,"label":662,"issuer":171,"region":172,"url":663,"description":664,"useCases":654,"indexable":249},"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":160,"label":666,"issuer":177,"region":172,"url":667,"description":668,"useCases":437,"indexable":249},"EBA Guidelines on loan origination and monitoring","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.",{"id":670,"label":671,"issuer":672,"region":182,"url":673,"description":674,"useCases":675,"indexable":249},"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":677,"label":678,"issuer":171,"region":172,"url":679,"description":680,"useCases":675,"indexable":249},"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":682,"label":683,"issuer":171,"region":172,"url":684,"description":685,"useCases":417,"indexable":249},"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":687,"label":688,"issuer":689,"region":690,"url":691,"description":692,"useCases":472,"indexable":249},"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":694,"label":695,"issuer":696,"region":172,"url":697,"description":698,"useCases":699,"indexable":249},"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.",4,{"id":701,"label":702,"issuer":703,"region":172,"url":704,"description":705,"useCases":699,"indexable":249},"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":707,"label":708,"issuer":709,"region":270,"url":710,"description":711,"useCases":418,"indexable":249},"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":713,"label":714,"issuer":171,"region":172,"url":715,"description":716,"useCases":418,"indexable":249},"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":167,"label":718,"issuer":719,"region":182,"url":720,"description":721,"useCases":418,"indexable":249},"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.",1790598299097]