[{"data":1,"prerenderedAt":661},["ShallowReactive",2],{"uc-call-quality-and-compliance-monitoring":3,"uc-regulations":457},{"useCase":4,"evidence":202,"blitsAiDeployments":332,"benchmarks":333,"indicative":350,"related":353,"indexability":455,"includeUnpublished":208},{"title":5,"shortTitle":6,"seoTitle":7,"metaDescription":8,"status":9,"definition":10,"aliases":11,"industries":17,"functions":24,"patterns":28,"channels":32,"audience":36,"autonomy":37,"adoptionStage":38,"problem":39,"problemStats":40,"howItWorks":41,"valueDrivers":42,"kpis":47,"indicativeValue":53,"macroEstimates":82,"feasibility":83,"implementation":95,"risk":141,"blitsAi":179,"faq":181,"related":191,"datePublished":197,"dateModified":197,"lastVerified":197,"changelog":198,"slug":201},"AI quality and compliance monitoring of every customer interaction","Call quality and compliance","AI call quality and compliance monitoring","AI scores every call and chat against your QA rubric and required disclosures. US bank group Central Bank went from 24 to 167,000 calls checked a quarter.","published","Automated quality assurance that transcribes and scores every customer interaction, voice and chat, against the organization's own rubric, checking required disclosures and script adherence, flagging conduct and mis selling risk, and surfacing coaching opportunities, instead of the small sample a human QA team can review.",[12,13,14,15,16],"automated QA","AI quality management","conversation intelligence","speech analytics for compliance","interaction analytics",[18,19,20,21,22,23],"cross-industry","banking","insurance","energy-and-utilities","telecommunications","retail-and-ecommerce",[25,26,27],"customer-service","regulatory-compliance","operations",[29,30,31],"speech-analytics","classification-and-routing","summarization",[33,34,35],"voice","web-chat","agent-desktop","back-office","supervised-agent","early-adopters","Traditional contact centre QA reviews a tiny sample: a few calls per agent per month, scored by\nhand. That sample is too small to find systematic problems, too late to coach while the call is\nremembered, and inconsistent between reviewers. For regulated firms it is also weak evidence:\nwhen a supervisor asks whether required disclosures were given, or whether vulnerable customers\nwere treated fairly, a small sample is not much of an answer.\n\nSpeech analytics and language models make it possible to evaluate every interaction against the\nsame rubric within a day. The value is coverage and consistency: finding the missed disclosure,\nthe mis sold product or the recurring complaint driver, and coaching on patterns rather than\nanecdotes. The risk is treating a model score as a verdict on a person, which is both unfair and,\nin the EU, a high risk use of AI.",[],"1. **Capture and transcribe.** Calls are transcribed after the fact (or in near real time) with\n   speaker separation; chat and email are ingested as text. Card data is redacted.\n2. **Score against the rubric.** Each interaction is checked against configurable criteria:\n   required disclosures, identity checks, script steps, prohibited statements, complaint and\n   vulnerability indicators, and service behaviours.\n3. **Flag for review.** Interactions with likely breaches, complaints or vulnerability signals go\n   to a QA or compliance reviewer with the relevant excerpt, not a bare score.\n4. **Calibrate against humans.** Reviewers regularly score the same interactions as the model;\n   disagreements tune the rubric and prompts.\n5. **Coach on patterns.** Team leaders see recurring gaps per team and topic and coach from real\n   examples; agents can see and dispute their own results.\n6. **Report oversight evidence.** Compliance gets coverage statistics, breach rates and trends for\n   conduct reporting and root cause analysis.",[43,44,45,46],"compliance","risk-reduction","customer-experience","employee-productivity",[48,49,50,51,52],"quality-score-uplift","interactions-handled","handling-time-reduction","accuracy","customer-satisfaction",{"referenceOrg":54,"inputs":55,"formula":77,"currency":78,"period":79,"resultLabel":80,"caveat":81},"A contact centre with 500 agents and a QA team of 10 to 20 analysts",[56,63,70],{"key":57,"label":58,"low":59,"high":60,"unit":61,"note":62},"qaAnalysts","QA analysts",10,20,"analysts","Editorial assumption of one analyst per 25 to 50 agents. Replace with your own team size.",{"key":64,"label":65,"low":66,"high":67,"unit":68,"note":69},"analystCost","Fully loaded cost per QA analyst",50000,70000,"USD per analyst per year","Editorial assumption. Replace with your own cost.",{"key":71,"label":72,"low":73,"high":74,"unit":75,"note":76},"timeFreed","Share of analyst time moved from listening and scoring to coaching and root cause work",0.3,0.5,"fraction of analyst time","Editorial assumption. Automated scoring replaces most manual listening, but calibration and review of flagged interactions remain.","qaAnalysts * analystCost * timeFreed","USD","per year","QA analyst capacity redirected","Covers QA effort only. It leaves out the main value, which is finding compliance breaches, mis selling and complaint drivers that a sample misses, and the platform and transcription costs.",[],{"complexity":84,"complexityNote":85,"dataPrerequisites":86,"integrations":90},"medium","Transcription and scoring are mature. The effort is in writing a rubric specific enough for a machine to apply, calibrating it against human reviewers, integrating recordings from the telephony platform, and agreeing with HR and employee representatives how results may be used.",[87,88,89],"The QA rubric and regulatory disclosure requirements per product and journey","A calibration set of interactions scored by experienced reviewers","Access to recordings and chat logs with metadata (agent, team, product, outcome)",[91,92,93,94],"Call recording and telephony platform","Chat and messaging platforms","QA, coaching and workforce management tools","Complaints and conduct risk reporting",{"steps":96,"guardrails":115,"humanInTheLoop":121,"kpisToInstrument":122,"failureModes":128},[97,100,103,106,109,112],{"title":98,"detail":99},"Rewrite the rubric for machines","Turn vague criteria (\"showed empathy\") into observable checks (\"acknowledged the problem before offering a solution\"), and list every mandatory disclosure per journey.",{"title":101,"detail":102},"Calibrate before you publish scores","Have experienced reviewers score a few hundred interactions and compare with the model. Publish only criteria where agreement is at least as good as between two humans. British Gas tested until its automated scores reached at least 80% agreement with human reviewers before going live.",{"title":104,"detail":105},"Start with compliance checks","Mandatory disclosures and prohibited statements are the clearest criteria and the strongest oversight evidence. Add softer service behaviours later.",{"title":107,"detail":108},"Agree the rules of use","Decide with HR, legal and employee representatives how results feed coaching and whether they may affect evaluation, pay or discipline, and document the assessment.",{"title":110,"detail":111},"Give agents visibility and a dispute route","Let agents see their scored interactions and challenge them. Disputes are also a calibration signal.",{"title":113,"detail":114},"Close the loop to root causes","Feed recurring breaches and complaint drivers to product, process and training owners, not only to individual coaching.",[116,117,118,119,120],"Scores are inputs for human review, never automatic sanctions","Rubric criteria published only after calibration against human reviewers","No inference of agents' emotions","Card data and special category data redacted before scoring and storage","Agents can see and dispute their results","QA and compliance reviewers confirm every flagged breach before it is recorded or acted on. Team leaders decide on coaching, and any consequence for an individual follows the normal HR process with human judgment. Reviewers recalibrate the rubric at least quarterly.",[123,124,125,126,127],"Share of interactions evaluated automatically","Agreement between model and human reviewers per criterion","Confirmed breach rate for mandatory disclosures, per journey","Disputed scores and their outcome","Complaint and repeat contact trends after coaching",[129,132,135,138],{"title":130,"detail":131},"Scores treated as facts","An uncalibrated score drives performance ratings. Calibrate per criterion and keep humans in every consequential decision.",{"title":133,"detail":134},"Rubric too vague for a model","Criteria such as \"professional tone\" give noisy scores. Rewrite into observable behaviours.",{"title":136,"detail":137},"Surveillance backlash","Agents experience total monitoring without transparency, and trust and retention fall. Be open about what is measured and let agents dispute.",{"title":139,"detail":140},"Finding problems nobody fixes","Breaches are counted but root causes in products or processes remain. Route themes to owners with deadlines.",{"euAiAct":142,"regulations":145,"guidance":152,"controls":172,"incidents":178},{"tier":143,"basis":144},"high","Scoring individual agents' interactions to monitor and evaluate their performance and behaviour falls under Annex III point 4(b), employment and worker management. The Article 6(3) exception does not apply where the system profiles natural persons. Inferring agents' emotions is prohibited under Article 5(1)(f), except for medical or safety reasons. Inferring customers' emotions from their voice is emotion recognition on biometric data: high risk under Annex III point 1(c), and Article 50(3) requires informing the people exposed to it. Analytics that only aggregate interaction themes without evaluating individuals can fall outside the high risk category.",[146,147,148,149,150,151],"eu-ai-act","gdpr","uk-gdpr","uk-consumer-duty","pci-dss","iso-42001",[153,159,163,167],{"title":154,"issuer":155,"region":156,"url":157,"note":158},"Annex III, high risk AI systems referred to in Article 6(2)","European Union","europe","https://artificialintelligenceact.eu/annex/3/","Point 4(b) covers AI systems intended to monitor and evaluate the performance and behaviour of workers.",{"title":160,"issuer":155,"region":156,"url":161,"note":162},"Article 5, prohibited AI practices","https://artificialintelligenceact.eu/article/5/","Point 1(f) prohibits inferring emotions of natural persons in the workplace, except for medical or safety reasons.",{"title":164,"issuer":155,"region":156,"url":165,"note":166},"Article 50, transparency obligations for providers and deployers of certain AI systems","https://artificialintelligenceact.eu/article/50/","Point 3 requires deployers of an emotion recognition system to inform the people exposed to it, which matters when voice analytics infers customer sentiment from speech.",{"title":168,"issuer":169,"region":156,"url":170,"note":171},"Employment practices and data protection: monitoring workers","UK Information Commissioner's Office","https://ico.org.uk/for-organisations/uk-gdpr-guidance-and-resources/employment/monitoring-workers/","Guidance on transparency, necessity and impact assessments when monitoring workers, including through automated tools. The ICO marks it as under review after the Data (Use and Access) Act.",[173,174,175,176,177],"Data protection impact assessment and, in the EU, the high risk obligations for the deployer","Documented calibration results per criterion and per model version","Written rules on how scores may and may not be used for individual decisions","Agent access to their results and a dispute process","Retention limits and access controls on recordings and transcripts",[],{"howToBuild":180},"On Blits.ai the scoring runs as an **agentic workflow**: interactions from the platform's own\n**voice** and chat channels, or recordings fetched through **custom functions**, are transcribed\nwith streaming speech recognition or the **self hosted transcription** option with speaker\ndiarization, then an **AI agent** with **structured output** scores each one against the\nrubric held in a **knowledge base**, citing the excerpt behind each finding.\n\nFindings above a threshold go to a reviewer through **human in the loop approval** before\nanything is recorded, and every run keeps an audit trail. Chat traffic on the platform's own\nchannels passes the gateway, where **PII masking** and card number tokenization apply before it\nreaches the chat backend. Recordings fetched into the workflow do not pass the gateway, so add a\nredaction step in the workflow before scoring. **Test suites** with LLM grading hold the\nhuman calibration set and are rerun after every rubric or model change, so agreement with\nreviewers is tracked over time. The platform is model agnostic and can run in the EU or UAE region.",[182,185,188],{"question":183,"answer":184},"Can AI really review every call?","Yes, coverage is the main change. Observe.AI reports that Central Bank, a US community bank group, evaluated 167,000 calls in the third quarter of 2024, up from 24 per quarter before automated QA, and that DoorDash reached nearly 100% automated quality coverage across 19,000 agents.",{"question":186,"answer":187},"Is automated agent scoring high risk under the EU AI Act?","When it evaluates individual workers, yes: Annex III point 4(b) covers monitoring and evaluating the performance and behaviour of workers. Inferring agents' emotions from biometric data such as voice is prohibited under Article 5(1)(f), except for medical or safety reasons. Aggregate analytics on themes, without scoring individuals, carry less risk.",{"question":189,"answer":190},"How do we know the scores are right?","Calibrate. Have experienced reviewers score the same interactions as the model, publish only criteria where agreement is good enough, and repeat after every rubric or model change. British Gas, for example, went live only after its automated scores reached at least 80% agreement with human reviewers, and lets agents and team leaders challenge scores.",[192,193,194,195,196],"live-agent-assist","conversation-roleplay-training","complaints-root-cause-analysis","sales-call-coaching-and-crm-update","customer-feedback-analysis","2026-09-27",[199],{"date":197,"note":200},"First published","call-quality-and-compliance-monitoring",[203,237,259,290,311],{"title":204,"useCases":205,"organization":206,"vendors":210,"summary":214,"stage":215,"year":216,"channels":217,"languages":218,"metrics":220,"outcomeDisclosed":228,"sources":229,"verification":232,"grade":234,"id":235,"organizationSlug":236},"British Gas: automated quality and regulatory assurance of voice and webchat",[201],{"name":207,"anonymized":208,"country":209,"region":156,"industry":21},"British Gas",false,"GB",[211],{"name":212,"role":213},"CallMiner","platform","British Gas, which runs a 20 million contact operation across voice and webchat, used to assess quality and regulatory compliance manually, which limited volume and consistency. With CallMiner it now runs millions of automated assessments across voice and webchat against its Five Steps to Customer Excellence framework and its Ofgem regulatory checks, and feeds the results into agent coaching. The automated scores had to reach at least 80% agreement with human reviewers before going live, and agents and team leaders can challenge scores. The vendor reports that quality scores improved by about 10% and that several regulatory scores now meet or exceed target.","scaled",2026,[33,34],[219],"en",[221],{"kpi":48,"value":59,"unit":222,"qualifier":223,"period":224,"claimant":225,"quote":226,"sourceUrl":227},"percent","approximately","quality scores against the Five Steps to Customer Excellence framework","vendor","Quality scores against the Five Steps to Customer Excellence framework have improved by approximately 10%, with a general upward trend.","https://callminer.com/customers/stories/british-gas-scales-quality-and-regulatory-assurance",true,[230],{"url":227,"title":231,"publisher":212},"British Gas Scales Quality & Regulatory Assurance from Thousands to Millions with CallMiner",{"level":233,"checkedAt":197},"source-verified","C","british-gas-quality-and-regulatory-assurance",null,{"title":238,"useCases":239,"organization":240,"vendors":244,"summary":249,"stage":215,"year":216,"channels":250,"languages":251,"metrics":252,"outcomeDisclosed":228,"sources":253,"verification":257,"grade":234,"id":258,"organizationSlug":236},"DoorDash: automated quality evaluation across 19,000 support agents",[201],{"name":241,"anonymized":208,"country":242,"region":243,"industry":23},"DoorDash","US","global",[245,247],{"name":246,"role":213},"Observe.AI",{"name":248,"role":213},"AWS","DoorDash moved from manually reviewing a small sample of support interactions to automated evaluation of nearly all of them, reaching nearly 100% automated quality coverage across 19,000 frontline teammates in its own teams and BPO partners. It uses sentiment, comprehension and behavioural signals rather than only binary compliance checklists, and coaches from AI generated insights. Emerging problems that took days or weeks to surface are now seen in near real time.",[33],[219],[],[254],{"url":255,"title":256,"publisher":246},"https://www.observe.ai/customers/doordash-scales-customer-centric-ai-across-19-000-agents-with-nearly-100-automated-quality-coverage","DoorDash Scales Customer-Centric AI Across 19,000 Agents With Nearly 100% Automated Quality Coverage",{"level":233,"checkedAt":197},"doordash-automated-quality-coverage",{"title":260,"useCases":261,"organization":262,"vendors":265,"summary":267,"stage":215,"year":268,"channels":269,"languages":270,"metrics":271,"outcomeDisclosed":228,"sources":284,"verification":287,"grade":234,"id":289,"organizationSlug":236},"Central Bank: automated QA across all contact centre calls",[201],{"name":263,"anonymized":208,"country":242,"region":264,"industry":19},"Central Bank","north-america",[266],{"name":246,"role":213},"Central Bank, a group of community banks serving several states, mostly in the Midwest, runs a customer service centre with more than 3,000 interactions a day. It replaced manual QA sampling with Observe.AI's automated QA, searchable transcripts and AI tagging of call reasons, which also replaced manual disposition codes. The team went from evaluating a handful of calls a month to evaluating every call, and used the insight on agent behaviours to lower handling time.",2024,[33],[219],[272,280],{"kpi":49,"value":273,"unit":274,"qualifier":275,"period":276,"baseline":277,"claimant":225,"quote":278,"sourceUrl":279},167000,"count","exact","third quarter of 2024","24 calls evaluated per quarter before automated QA","In the third quarter of 2024, the team evaluated 167,000 calls, a jump from just eight per month or 24 per quarter before adopting Auto QA.","https://www.observe.ai/customers/central-bank",{"kpi":50,"value":281,"unit":222,"qualifier":282,"claimant":225,"quote":283,"sourceUrl":279},5,"up-to","Identifying key agent behaviors has helped the CSC team develop data-driven plans and reduce average handle time by up to 5%, resulting in significant efficiency gains.",[285],{"url":279,"title":286,"publisher":246},"Central Bank streamlines call handling and boosts efficiency with Post-Interaction AI",{"level":233,"checkedAt":288},"2026-09-26","central-bank-automated-call-quality",{"title":291,"useCases":292,"organization":293,"vendors":295,"summary":298,"stage":215,"year":268,"channels":299,"languages":300,"metrics":301,"outcomeDisclosed":208,"sources":302,"verification":309,"grade":234,"id":310,"organizationSlug":236},"Oportun: from sample based QA to monitoring every call",[201,192],{"name":294,"anonymized":208,"country":242,"region":264,"industry":19},"Oportun",[296],{"name":297,"role":213},"Cresta","Oportun, a US consumer lender, replaced manual, sample based QA with Cresta's AI quality management across all calls, combined with real time guidance for agents. Coaching now focuses on the behaviours that drive performance, visible across every call, instead of a small sample reviewed weeks later. No quantified outcome is published.",[33],[219],[],[303,306],{"url":304,"title":305,"publisher":297},"https://www.cresta.com/customers/oportun","How Oportun moved from sample-based QA to 100% interaction monitoring with Cresta",{"url":307,"title":308,"publisher":297},"https://web.archive.org/web/20240423210739/https://cresta.com/customers/oportun/","How Oportun transformed QM and reduced workload by 50% with Cresta",{"level":233,"checkedAt":197},"oportun-ai-quality-management",{"title":312,"useCases":313,"organization":314,"vendors":316,"summary":321,"stage":215,"year":322,"channels":323,"languages":324,"metrics":325,"outcomeDisclosed":208,"sources":326,"verification":330,"grade":234,"id":331,"organizationSlug":236},"VitalityHealth: automated QA of every advisor call with speech analytics",[201],{"name":315,"anonymized":208,"country":209,"region":156,"industry":20},"VitalityHealth",[317,318],{"name":212,"role":213},{"name":319,"role":320},"Davies Consulting","integrator","VitalityHealth, a UK health insurer with more than 550 customer service advisors and over 1 million calls a year, moved from manual to automated quality assurance with CallMiner, run as a managed service by Davies Consulting. Every call is analysed and assessed in three areas: regulatory, service excellence (tone, empathy, how the call opened and closed) and process assurance, and results reach the coaching system within 24 hours. No quantified outcome is published.",2020,[33],[219],[],[327],{"url":328,"title":329,"publisher":212},"https://callminer.com/customers/stories/davies-consulting-callminer-partner-to-automate-quality-assurance-for-vitalityhealth","Davies Consulting and CallMiner partner to automate quality assurance for VitalityHealth",{"level":233,"checkedAt":288},"vitalityhealth-automated-quality-assurance",0,[334,340,345],{"kpi":49,"label":335,"unit":274,"aggregate":208,"higherIsBetter":228,"n":336,"nUpTo":332,"median":273,"min":273,"max":273,"byClaimant":337,"vendorOnly":228,"points":338},"Interactions handled",1,{"organization":332,"vendor":336,"regulator":332,"independent":332},[339],{"evidenceId":289,"organization":263,"value":273,"qualifier":275,"claimant":225,"grade":234,"pooled":228},{"kpi":48,"label":341,"unit":222,"aggregate":228,"higherIsBetter":228,"n":336,"nUpTo":332,"median":59,"min":59,"max":59,"byClaimant":342,"vendorOnly":228,"points":343},"Quality score uplift",{"organization":332,"vendor":336,"regulator":332,"independent":332},[344],{"evidenceId":235,"organization":207,"value":59,"qualifier":223,"claimant":225,"grade":234,"pooled":228},{"kpi":50,"label":346,"unit":222,"aggregate":228,"higherIsBetter":228,"n":332,"nUpTo":336,"median":236,"min":236,"max":236,"byClaimant":347,"vendorOnly":208,"points":348},"Handling time reduction",{"organization":332,"vendor":332,"regulator":332,"independent":332},[349],{"evidenceId":289,"organization":263,"value":281,"qualifier":282,"claimant":225,"grade":234,"pooled":208},{"low":351,"high":352},150000,700000,[354,381,402,419,438],{"slug":192,"title":355,"shortTitle":356,"definition":357,"status":9,"industries":358,"functions":361,"patterns":362,"audience":365,"autonomy":366,"adoptionStage":367,"evidenceCount":368,"publicEvidenceCount":281,"organizations":369,"bestGrade":374,"headline":375,"lastVerified":197,"indexable":228},"Real time AI assist for contact centre agents","Live agent assist","A real time copilot for human contact centre agents during a live call or chat: it transcribes the conversation as it happens, surfaces the relevant knowledge and next step, drafts responses, and writes the after call summary and CRM notes, while the agent stays in control of what is said and done.",[18,19,20,22,359,23,360],"healthcare","technology",[25,27],[29,363,31,364],"rag-knowledge-assistant","content-generation","employee-facing","assist","mainstream",7,[370,371,294,372,373],"DBS Bank","Definity","SEB","SIGNAL IDUNA","B",{"kpi":376,"label":377,"unit":222,"n":378,"nUpTo":332,"kind":379,"value":380,"qualifier":275,"claimant":225,"organization":371,"vendorReported":228},"productivity-gain","Productivity gain",2,"reported",15,{"slug":193,"title":382,"shortTitle":383,"definition":384,"status":9,"industries":385,"functions":387,"patterns":390,"audience":365,"autonomy":366,"adoptionStage":38,"evidenceCount":393,"publicEvidenceCount":393,"organizations":394,"bestGrade":374,"headline":398,"lastVerified":288,"indexable":228},"AI roleplay training for customer conversations","Conversation roleplay training","A training simulator in which generative AI plays a realistic customer, by voice or text, so service, sales and crisis staff can rehearse difficult conversations as often as they need before they handle live ones, and receive structured feedback against the organization's own standards.",[18,19,20,22,386,359],"government",[388,25,389],"human-resources","sales",[391,392,364],"conversational-agent","voice-agent",3,[395,396,397],"Bank of America","GoHealth","U.S. Department of Veterans Affairs",{"kpi":399,"label":400,"unit":222,"n":336,"nUpTo":332,"kind":379,"value":401,"qualifier":275,"claimant":225,"organization":396,"vendorReported":228},"conversion-rate-uplift","Conversion uplift",21,{"slug":194,"title":403,"shortTitle":404,"definition":405,"status":9,"industries":406,"functions":408,"patterns":410,"audience":36,"autonomy":412,"adoptionStage":413,"segment":414,"evidenceCount":393,"publicEvidenceCount":393,"organizations":415,"bestGrade":374,"headline":236,"lastVerified":197,"indexable":228},"AI for complaints root cause and systemic issue analysis","Complaints root cause analysis","AI that reads the free text of complaints across all channels, clusters them into themes, separates systemic causes from one off events, links each theme to the product, process or control behind it and routes the insight to the owner who can fix it, with a human validating every root cause and every remediation.",[18,19,20,407,22,386],"payments",[26,25,409],"analytics-and-reporting",[30,31,411,363],"agentic-workflow","copilot","emerging","second-line",[416,417,418],"Centers for Medicare and Medicaid Services","Board of Governors of the Federal Reserve System","Federal Trade Commission",{"slug":195,"title":420,"shortTitle":421,"definition":422,"status":9,"industries":423,"functions":425,"patterns":426,"audience":365,"autonomy":412,"adoptionStage":38,"evidenceCount":427,"publicEvidenceCount":427,"organizations":428,"bestGrade":234,"headline":433,"lastVerified":197,"indexable":228},"AI sales call coaching and CRM update","Sales call coaching and CRM update","AI for sales teams that analyses sales calls and meetings against the team's own sales method to coach sellers and their managers, and writes the call summary, next steps and opportunity updates into the CRM for the seller to confirm. Its purpose is winning deals and building selling skill, not the regulated advice record or general meeting notes.",[18,22,424,20],"manufacturing",[389],[29,31,364],4,[429,430,431,432],"Hughes Network Systems","Lumen Technologies","Sandvik Coromant","Zurich Insurance Group",{"kpi":434,"label":435,"unit":436,"n":336,"nUpTo":336,"kind":379,"value":393,"qualifier":275,"claimant":437,"organization":431,"vendorReported":208},"time-saved-per-task","Time saved per task","minutes","organization",{"slug":196,"title":439,"shortTitle":440,"definition":441,"status":9,"industries":442,"functions":443,"patterns":445,"audience":36,"autonomy":412,"adoptionStage":367,"evidenceCount":281,"publicEvidenceCount":281,"organizations":446,"bestGrade":374,"headline":452,"lastVerified":197,"indexable":228},"AI for voice of the customer and feedback analysis","Customer feedback analysis","AI that reads every piece of free text customer feedback, such as survey verbatims, NPS comments, reviews, social posts, chat and call transcripts, and turns it into themes, sentiment, drivers and suggested actions that a named owner can act on, so the organization hears all of its customers instead of a sample.",[18,23,386,424],[25,444,409],"marketing",[30,31,29],[447,448,449,450,451],"U.S. Department of Housing and Urban Development","Majid Al Futtaim Retail","Mattel","SBF Group","U.S. Social Security Administration",{"kpi":51,"label":453,"unit":222,"n":336,"nUpTo":332,"kind":379,"value":454,"qualifier":275,"claimant":225,"organization":450,"vendorReported":228},"Accuracy",84,{"indexable":228,"reasons":456},[],[458,463,468,474,481,487,493,499,507,514,519,525,532,538,544,549,556,562,568,574,580,586,591,596,601,608,615,620,626,633,639,645,651,656],{"id":146,"label":459,"issuer":155,"region":156,"url":460,"description":461,"useCases":462,"indexable":228},"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":147,"label":464,"issuer":155,"region":156,"url":465,"description":466,"useCases":467,"indexable":228},"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":151,"label":469,"issuer":470,"region":243,"url":471,"description":472,"useCases":473,"indexable":228},"ISO/IEC 42001","ISO and IEC","https://www.iso.org/standard/81230.html","The international management system standard for AI.",110,{"id":475,"label":476,"issuer":477,"region":264,"url":478,"description":479,"useCases":480,"indexable":228},"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":482,"label":483,"issuer":155,"region":156,"url":484,"description":485,"useCases":486,"indexable":228},"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":148,"label":488,"issuer":489,"region":156,"url":490,"description":491,"useCases":492,"indexable":228},"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":149,"label":494,"issuer":495,"region":156,"url":496,"description":497,"useCases":498,"indexable":228},"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":500,"label":501,"issuer":502,"region":503,"url":504,"description":505,"useCases":506,"indexable":228},"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":508,"label":509,"issuer":510,"region":503,"url":511,"description":512,"useCases":513,"indexable":228},"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":150,"label":515,"issuer":516,"region":243,"url":517,"description":518,"useCases":60,"indexable":228},"PCI DSS","PCI Security Standards Council","https://www.pcisecuritystandards.org/","Security standard for any system that stores, processes or transmits cardholder data.",{"id":520,"label":521,"issuer":522,"region":264,"url":523,"description":524,"useCases":60,"indexable":228},"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":526,"label":527,"issuer":528,"region":156,"url":529,"description":530,"useCases":531,"indexable":228},"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":533,"label":534,"issuer":535,"region":243,"url":536,"description":537,"useCases":380,"indexable":228},"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.",{"id":539,"label":540,"issuer":155,"region":156,"url":541,"description":542,"useCases":543,"indexable":228},"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":545,"label":546,"issuer":155,"region":156,"url":547,"description":548,"useCases":543,"indexable":228},"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":550,"label":551,"issuer":552,"region":264,"url":553,"description":554,"useCases":555,"indexable":228},"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":557,"label":558,"issuer":155,"region":156,"url":559,"description":560,"useCases":561,"indexable":228},"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":563,"label":564,"issuer":565,"region":264,"url":566,"description":567,"useCases":561,"indexable":228},"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":569,"label":570,"issuer":571,"region":243,"url":572,"description":573,"useCases":561,"indexable":228},"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":575,"label":576,"issuer":155,"region":156,"url":577,"description":578,"useCases":579,"indexable":228},"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":581,"label":582,"issuer":583,"region":264,"url":584,"description":585,"useCases":579,"indexable":228},"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":587,"label":588,"issuer":502,"region":503,"url":589,"description":590,"useCases":59,"indexable":228},"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":592,"label":593,"issuer":155,"region":156,"url":594,"description":595,"useCases":59,"indexable":228},"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":597,"label":598,"issuer":155,"region":156,"url":599,"description":600,"useCases":59,"indexable":228},"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":602,"label":603,"issuer":604,"region":156,"url":605,"description":606,"useCases":607,"indexable":228},"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":609,"label":610,"issuer":611,"region":264,"url":612,"description":613,"useCases":614,"indexable":228},"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":616,"label":617,"issuer":155,"region":156,"url":618,"description":619,"useCases":614,"indexable":228},"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":621,"label":622,"issuer":155,"region":156,"url":623,"description":624,"useCases":625,"indexable":228},"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.",6,{"id":627,"label":628,"issuer":629,"region":630,"url":631,"description":632,"useCases":281,"indexable":228},"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":634,"label":635,"issuer":636,"region":156,"url":637,"description":638,"useCases":427,"indexable":228},"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":640,"label":641,"issuer":642,"region":156,"url":643,"description":644,"useCases":427,"indexable":228},"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":646,"label":647,"issuer":648,"region":503,"url":649,"description":650,"useCases":393,"indexable":228},"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":652,"label":653,"issuer":155,"region":156,"url":654,"description":655,"useCases":393,"indexable":228},"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":657,"label":658,"issuer":418,"region":264,"url":659,"description":660,"useCases":393,"indexable":228},"us-fcra","Fair Credit Reporting Act","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.",1790598303311]