[{"data":1,"prerenderedAt":543},["ShallowReactive",2],{"uc-loan-restructuring-recommendations":3,"uc-regulations":336},{"useCase":4,"evidence":186,"blitsAiDeployments":221,"benchmarks":222,"indicative":223,"related":226,"indexability":334,"includeUnpublished":195},{"title":5,"shortTitle":6,"seoTitle":7,"metaDescription":8,"status":9,"definition":10,"aliases":11,"industries":15,"functions":17,"patterns":21,"channels":26,"audience":30,"autonomy":31,"adoptionStage":32,"segment":33,"problem":34,"problemStats":35,"howItWorks":36,"valueDrivers":37,"kpis":42,"indicativeValue":46,"macroEstimates":74,"feasibility":75,"implementation":88,"risk":128,"blitsAi":163,"faq":165,"related":175,"datePublished":181,"dateModified":181,"lastVerified":181,"changelog":182,"slug":185},"AI recommendations for loan restructuring and hardship arrangements","Restructuring recommendations","AI for loan restructuring and hardship plans","AI tests term extensions, payment holidays and rate relief against policy and affordability, then recommends the best fit for a hardship specialist to approve.","published","An assistant that assembles a stressed borrower's position, tests restructuring options such as a term extension, rate relief, payment holiday or due date change against policy and affordability, and recommends the best fit with a written rationale for a person to approve.",[12,13,14],"forbearance recommendation","hardship arrangement assistant","loan modification recommendation",[16],"banking",[18,19,20],"collections-and-recovery","lending-and-credit","risk-management",[22,23,24,25],"agentic-workflow","rag-knowledge-assistant","document-processing","recommendation-and-personalization",[27,28,29],"agent-desktop","internal-tools","mobile-app","employee-facing","copilot","emerging","lending","When a borrower gets into difficulty the right intervention early is cheaper for everyone than\nenforcement later. But finding it is slow. A hardship or workout specialist has to pull together\nbalances, arrears history, income and expense evidence, collateral and the borrower's own\nexplanation, then work through policy to see which options are allowed and affordable. Queues grow\nexactly when times are hard, decisions vary between specialists, and the reasons are not always\nwritten down.\n\nRegulators expect lenders to treat borrowers in financial difficulty fairly, to choose sustainable\nsolutions over short term fixes that fail, and to document why. Inconsistent or undocumented\nconcessions are a conduct risk and a credit risk at the same time.",[],"1. **Assemble the position.** The assistant gathers balances, arrears, payment history, other\n   exposures, collateral and any income or hardship evidence the customer has provided.\n2. **Read the evidence.** Document AI extracts figures from payslips, bank statements and letters,\n   and flags gaps.\n3. **Test the options.** For each option the policy allows (due date change, payment holiday, term\n   extension, temporary rate relief, capitalisation), it calculates the new payment, the effect on\n   arrears and whether it fits the stated budget.\n4. **Recommend with reasons.** It ranks the options, cites the policy clause behind each, and\n   writes a short rationale and the risks.\n5. **Decide and record.** A specialist accepts, changes or rejects the recommendation; the decision,\n   the reasons and the evidence are stored with the case.\n6. **Follow up.** Review dates are scheduled and the arrangement is monitored for early signs that\n   it is not working.",[38,39,40,41],"risk-reduction","customer-experience","employee-productivity","compliance",[43,44,45],"handling-time-reduction","processing-time-reduction","recovery-rate-uplift",{"referenceOrg":47,"inputs":48,"formula":69,"currency":70,"period":71,"resultLabel":72,"caveat":73},"A retail bank handling 10,000 hardship and restructuring requests a year",[49,55,62],{"key":50,"label":51,"low":52,"high":52,"unit":53,"note":54},"requests","Hardship and restructuring requests per year",10000,"requests per year","The reference bank.",{"key":56,"label":57,"low":58,"high":59,"unit":60,"note":61},"hoursSaved","Specialist hours saved per request on assembling the case and testing options",0.5,1.5,"hours per request","Editorial assumption. Replace with your own time study.",{"key":63,"label":64,"low":65,"high":66,"unit":67,"note":68},"hourlyCost","Fully loaded cost of a hardship specialist hour",45,70,"USD per hour","Editorial assumption.","requests * hoursSaved * hourlyCost","USD","per year","Specialist time released","Counts specialist time only. It leaves out the credit effect of earlier and more sustainable arrangements, fewer broken plans and lower complaint volumes, which can be larger but need a controlled measurement.",[],{"complexity":76,"complexityNote":77,"dataPrerequisites":78,"integrations":83},"medium","The option calculations are deterministic once policy is written down. The effort is in encoding policy, reading hardship evidence reliably and fitting the assistant into the specialist's case workflow.",[79,80,81,82],"Restructuring and hardship policy with eligibility rules per product","Account, arrears and payment history per borrower","Income and expense evidence, or a structured budget from the customer","Outcomes of past arrangements to learn which options last",[84,85,86,87],"Loan servicing and collections systems","Document intake for hardship evidence","Case management for hardship and workout teams","Customer channels for evidence requests and outcome letters",{"steps":89,"guardrails":105,"humanInTheLoop":111,"kpisToInstrument":112,"failureModes":118},[90,93,96,99,102],{"title":91,"detail":92},"Encode the policy","Turn the restructuring policy into explicit rules per product: which options, for how long, with which limits and approvals. The assistant can only recommend what the rules allow.",{"title":94,"detail":95},"Automate the case pack","Start by assembling the borrower's position and evidence automatically. Specialists gain time even before any recommendation is shown.",{"title":97,"detail":98},"Add option testing and ranking","Calculate each allowed option's payment and effect, rank them on affordability and sustainability, and show the calculation behind every number.",{"title":100,"detail":101},"Measure agreement and outcomes","Track how often specialists accept the recommendation and how arrangements perform after six and twelve months, by option and segment.",{"title":103,"detail":104},"Extend to proactive outreach","Once recommendations are trusted, combine them with early warning signals to offer support before customers fall behind.",[106,107,108,109,110],"Recommendations only; every restructure is approved by a person with authority","Options limited to what policy allows, with the policy clause cited","A written rationale stored with every decision","Consistency checks that flag similar cases receiving different outcomes","Vulnerability flags shown prominently and never used to reduce support","Hardship and workout specialists decide every case and can override any recommendation, with a reason. Credit risk approves the rules and reviews arrangement performance; conduct risk reviews consistency and outcomes for vulnerable customers.",[113,114,115,116,117],"Time from hardship request to decision","Share of recommendations accepted without change","Arrangements still performing after six and twelve months, by option","Complaints about hardship decisions","Outcome differences between comparable customers",[119,122,125],{"title":120,"detail":121},"Short term fixes that fail","The assistant optimises for the lowest payment now and the arrangement breaks later. Rank on sustainability and track long term outcomes.",{"title":123,"detail":124},"Rubber stamping","Specialists accept recommendations without reading them. Show the reasoning, sample decisions for review and measure override quality.",{"title":126,"detail":127},"Evidence misread","Wrong income or expense figures lead to an unaffordable plan. Show the source document next to each extracted figure.",{"euAiAct":129,"regulations":132,"guidance":139,"controls":157,"incidents":162},{"tier":130,"basis":131},"context-dependent","Recommending restructuring terms for individuals involves assessing their ability to pay, which can amount to evaluating the creditworthiness of natural persons under Annex III point 5(b). Human approval alone does not remove that: the Article 6(3) exception covers only systems that do not materially influence the decision, such as a narrow procedural or preparatory task, and never applies when the system profiles natural persons. A tool that only assembles the case file can fall under the exception; restructuring for companies is outside point 5(b).",[133,134,135,136,137,138],"eu-ai-act","gdpr","uk-consumer-duty","eba-loan-origination","us-sr-11-7","us-ecoa-reg-b",[140,146,151],{"title":141,"issuer":142,"region":143,"url":144,"note":145},"Guidance to banks on non-performing loans","European Central Bank","europe","https://www.bankingsupervision.europa.eu/ecb/pub/pdf/guidance_on_npl.en.pdf","Sets expectations for viable forbearance solutions, borrower affordability assessments and clearly defined, consistent decision making procedures.",{"title":147,"issuer":148,"region":143,"url":149,"note":150},"FG21/1: guidance for firms on the fair treatment of vulnerable customers","Financial Conduct Authority","https://www.fca.org.uk/publication/finalised-guidance/fg21-1.pdf","Fair treatment of customers in vulnerable circumstances, relevant to every hardship decision.",{"title":152,"issuer":153,"region":154,"url":155,"note":156},"REP 782 Hardship, hard to get help: Findings and actions to support customers in financial hardship","Australian Securities and Investments Commission","asia-pacific","https://asic.gov.au/regulatory-resources/find-a-document/reports/rep-782-hardship-hard-to-get-help-findings-and-actions-to-support-customers-in-financial-hardship/","Review of how 10 large lenders handled home loan customers in financial hardship (May 2024), with good and poor practices; ASIC says the insights are relevant to hardship involving all types of credit.",[158,159,160,161],"Policy rules under change control, approved by credit risk","Decision log with recommendation, final decision, override reason and evidence","Periodic consistency review across comparable cases","Outcome monitoring for vulnerable customers",[],{"howToBuild":164},"On Blits.ai this is an **agentic workflow** started from the hardship case. **Custom functions**\npull balances, arrears and payment history from the servicing system, and hardship evidence the\ncustomer sends as a **file upload** (payslips, statements) is attached to the case. The restructuring\npolicy sits in the **knowledge base**, so the **agent** cites the clause behind each option, while\ndeterministic **custom functions** (JavaScript sandbox) calculate the payments. **Structured\noutput** returns the ranked options and the rationale in a fixed format.\n\nThe recommendation waits for **human in the loop approval** by a specialist, and the **audit\ntrail** stores the run, the recommendation and the decision. Customer facing parts, such as\ncollecting evidence or confirming the arrangement, can run through an **AI agent** in the bank's\napp (through the **API channel**), on WhatsApp or by voice with **human handover** to the specialist. **PII masking** protects the evidence,\nand **test suites** check recommendations against reference cases on every policy change.",[166,169,172],{"question":167,"answer":168},"Can AI decide a loan restructure?","It should recommend, not decide. A person with authority approves every restructure, with the assistant's calculation and rationale in front of them, because hardship cases need judgment and fair treatment.",{"question":170,"answer":171},"Is anyone using AI to offer hardship support proactively?","In 2022 Commonwealth Bank said it used its Customer Engagement Engine and a weather data model to reach customers hit by natural disasters with same day support, such as deferring a loan or an emergency overdraft. That is proactive hardship outreach, not a restructuring recommender. A US auto lender's collections agent, as described by its vendor, spots borrowers' pay patterns on the call and triggers a change of due date in the lender's system, and routes hardship cases to its dealerships.",{"question":173,"answer":174},"What makes a good restructuring recommendation?","One that the borrower can sustain, that policy allows, and whose reasoning is written down. Track arrangements for six to twelve months to learn which options actually last.",[176,177,178,179,180],"collections-and-hardship-agent","credit-early-warning-monitoring","adverse-action-explanations","financial-wellbeing-coach","outbound-notice-drafting","2026-09-27",[183],{"date":181,"note":184},"First published","loan-restructuring-recommendations",[187],{"title":188,"useCases":189,"organization":193,"vendors":197,"summary":201,"stage":202,"year":203,"channels":204,"languages":205,"metrics":207,"outcomeDisclosed":195,"sources":208,"verification":216,"grade":218,"id":219,"organizationSlug":220},"Commonwealth Bank: Customer Engagement Engine for next best conversations",[190,179,185,191,192],"offers-and-rewards-agent","proactive-outbound-engagement-agent","personalized-marketing-at-scale",{"name":194,"anonymized":195,"country":196,"region":154,"industry":16},"Commonwealth Bank of Australia",false,"AU",[198],{"name":199,"role":200},"Pegasystems","platform","Commonwealth Bank's Customer Engagement Engine (CEE), built on Pega Customer Decision Hub, suggests in real time the next best conversation to have with each customer, whether in the branch, on the phone, online or on a mobile device. Beyond suggesting conversations, the bank uses it to match customers to government benefits and rebates they may be missing (Benefits finder) and to reach customers hit by natural disasters with same day support such as a loan deferral. The same decisions feed digital channels and prompts for branch and contact centre staff.","scaled",2022,[29,27],[206],"en",[],[209,213],{"url":210,"title":211,"publisher":194,"date":212},"https://www.commbank.com.au/articles/newsroom/2022/06/CBA-artificial-intelligence-usages.html","How artificial intelligence is changing the face of banking","2022-06-24",{"url":214,"title":215,"publisher":199},"https://www.pega.com/customers/cba-marketing","Delivering next best conversations with Pega",{"level":217,"checkedAt":181},"source-verified","B","commonwealth-bank-customer-engagement-engine","commonwealth-bank-of-australia",0,[],{"low":224,"high":225},225000,1050000,[227,262,277,289,307],{"slug":176,"title":228,"shortTitle":229,"definition":230,"status":9,"industries":231,"functions":238,"patterns":240,"audience":244,"autonomy":245,"adoptionStage":246,"segment":33,"evidenceCount":247,"publicEvidenceCount":248,"organizations":249,"bestGrade":252,"headline":253,"lastVerified":181,"indexable":261},"AI agent for early collections and hardship support","Collections and hardship agent","A voice and messaging agent that contacts customers in early arrears and answers their inbound calls, takes payments and sets up payment arrangements within preapproved rules, and recognises signs of hardship or vulnerability so those customers go straight to a trained person.",[232,16,233,234,235,236,237],"cross-industry","payments","telecommunications","energy-and-utilities","automotive","professional-services",[18,239],"customer-service",[241,242,22,243],"voice-agent","conversational-agent","classification-and-routing","customer-facing","supervised-agent","early-adopters",3,2,[250,251],"Day Knight & Associates","SameDay Auto Finance","C",{"kpi":254,"label":255,"unit":256,"n":248,"nUpTo":221,"kind":257,"value":258,"qualifier":259,"claimant":260,"organization":251,"vendorReported":261},"cost-reduction","Cost reduction","percent","reported",75,"exact","vendor",true,{"slug":177,"title":263,"shortTitle":264,"definition":265,"status":9,"industries":266,"functions":267,"patterns":268,"audience":30,"autonomy":271,"adoptionStage":246,"segment":33,"evidenceCount":247,"publicEvidenceCount":247,"organizations":272,"bestGrade":252,"headline":276,"lastVerified":181,"indexable":261},"AI early warning and covenant monitoring for loan portfolios","Credit early warning and covenants","A monitoring system that tracks covenant tests and borrower reporting across a loan book, reads financials, filings and news, and combines them with payment and sector signals to flag borrowers whose credit is deteriorating, with the evidence and a suggested next step for the relationship manager.",[16],[20,19],[269,24,22,270],"anomaly-detection","summarization","assist",[273,274,275],"OakNorth Bank","PNC Financial Services","Sumitomo Mitsui Banking Corporation",null,{"slug":178,"title":278,"shortTitle":279,"definition":280,"status":9,"industries":281,"functions":282,"patterns":284,"audience":30,"autonomy":31,"adoptionStage":32,"segment":33,"evidenceCount":248,"publicEvidenceCount":248,"organizations":286,"bestGrade":218,"headline":276,"lastVerified":181,"indexable":261},"AI drafted explanations for credit declines and adverse actions","Adverse action explanations","An assistant that turns the reason codes of a credit model into an accurate, specific and readable explanation of a decline, reduced limit or repricing for the customer, and a matching internal rationale for the file, without adding any reason the model did not produce.",[16,233],[19,283,239],"regulatory-compliance",[285,23,242],"content-generation",[287,288],"Discover Financial Services","Wells Fargo",{"slug":179,"title":290,"shortTitle":291,"definition":292,"status":9,"industries":293,"functions":294,"patterns":296,"audience":244,"autonomy":245,"adoptionStage":246,"segment":298,"evidenceCount":299,"publicEvidenceCount":300,"organizations":301,"bestGrade":218,"headline":276,"lastVerified":181,"indexable":261},"AI financial wellbeing coach in the banking app","Financial wellbeing coach","An in app AI assistant that the customer opens to understand their own money: it uses the customer's transaction data to explain their spending, forecast upcoming bills and cash flow, set and track savings goals and answer money questions in plain language, staying on the guidance side of the line between guidance and regulated financial advice.",[16],[239,295],"marketing",[242,25,297,22],"prediction-and-scoring","front-office",8,6,[302,194,303,304,305,306],"Bank of America","Hyundai Card","Royal Bank of Canada","Starling Bank","Westpac",{"slug":180,"title":308,"shortTitle":309,"definition":310,"status":9,"industries":311,"functions":316,"patterns":319,"audience":30,"autonomy":31,"adoptionStage":246,"segment":321,"evidenceCount":322,"publicEvidenceCount":322,"organizations":323,"bestGrade":218,"headline":328,"lastVerified":333,"indexable":261},"AI for drafting customer letters and outbound notices","Outbound notice drafting","AI that drafts the letters and notices operations must send at scale, such as arrears notices, decline letters, complaint responses, servicing confirmations and product change notices, from case data and approved templates and clauses, in the customer's language, for a person to approve where the notice is regulated.",[232,16,312,313,314,315],"insurance","government","healthcare","wealth-and-asset-management",[317,239,18,283,318],"operations","claims",[285,23,320],"translation","back-office",5,[324,325,326,327],"Acentra Health","Hiscox","Health Resources and Services Administration","SS&C Technologies",{"kpi":44,"label":329,"unit":256,"n":330,"nUpTo":221,"kind":257,"value":331,"qualifier":259,"claimant":260,"organization":332,"vendorReported":261},"Cycle time reduction",1,25,"SS&C GIDS and RS","2026-09-26",{"indexable":261,"reasons":335},[],[337,343,348,356,364,370,377,382,389,395,402,407,414,421,427,432,439,445,451,457,463,469,475,480,485,491,496,501,506,513,520,526,532,537],{"id":133,"label":338,"issuer":339,"region":143,"url":340,"description":341,"useCases":342,"indexable":261},"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.",197,{"id":134,"label":344,"issuer":339,"region":143,"url":345,"description":346,"useCases":347,"indexable":261},"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":349,"label":350,"issuer":351,"region":352,"url":353,"description":354,"useCases":355,"indexable":261},"iso-42001","ISO/IEC 42001","ISO and IEC","global","https://www.iso.org/standard/81230.html","The international management system standard for AI.",110,{"id":357,"label":358,"issuer":359,"region":360,"url":361,"description":362,"useCases":363,"indexable":261},"nist-ai-rmf","NIST AI Risk Management Framework","NIST","north-america","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":365,"label":366,"issuer":339,"region":143,"url":367,"description":368,"useCases":369,"indexable":261},"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":143,"url":374,"description":375,"useCases":376,"indexable":261},"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":135,"label":378,"issuer":148,"region":143,"url":379,"description":380,"useCases":381,"indexable":261},"FCA Consumer Duty","https://www.fca.org.uk/firms/consumer-duty","UK rules that require firms to deliver good outcomes for retail customers, including through automated channels.",47,{"id":383,"label":384,"issuer":385,"region":154,"url":386,"description":387,"useCases":388,"indexable":261},"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":390,"label":391,"issuer":392,"region":154,"url":393,"description":394,"useCases":331,"indexable":261},"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.",{"id":396,"label":397,"issuer":398,"region":352,"url":399,"description":400,"useCases":401,"indexable":261},"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":137,"label":403,"issuer":404,"region":360,"url":405,"description":406,"useCases":401,"indexable":261},"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":408,"label":409,"issuer":410,"region":143,"url":411,"description":412,"useCases":413,"indexable":261},"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":415,"label":416,"issuer":417,"region":352,"url":418,"description":419,"useCases":420,"indexable":261},"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":422,"label":423,"issuer":339,"region":143,"url":424,"description":425,"useCases":426,"indexable":261},"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":428,"label":429,"issuer":339,"region":143,"url":430,"description":431,"useCases":426,"indexable":261},"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":433,"label":434,"issuer":435,"region":360,"url":436,"description":437,"useCases":438,"indexable":261},"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":440,"label":441,"issuer":339,"region":143,"url":442,"description":443,"useCases":444,"indexable":261},"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":446,"label":447,"issuer":448,"region":360,"url":449,"description":450,"useCases":444,"indexable":261},"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":452,"label":453,"issuer":454,"region":352,"url":455,"description":456,"useCases":444,"indexable":261},"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":458,"label":459,"issuer":339,"region":143,"url":460,"description":461,"useCases":462,"indexable":261},"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":464,"label":465,"issuer":466,"region":360,"url":467,"description":468,"useCases":462,"indexable":261},"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":470,"label":471,"issuer":385,"region":154,"url":472,"description":473,"useCases":474,"indexable":261},"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":476,"label":477,"issuer":339,"region":143,"url":478,"description":479,"useCases":474,"indexable":261},"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":481,"label":482,"issuer":339,"region":143,"url":483,"description":484,"useCases":474,"indexable":261},"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":136,"label":486,"issuer":487,"region":143,"url":488,"description":489,"useCases":490,"indexable":261},"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":138,"label":492,"issuer":493,"region":360,"url":494,"description":495,"useCases":299,"indexable":261},"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.",{"id":497,"label":498,"issuer":339,"region":143,"url":499,"description":500,"useCases":299,"indexable":261},"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":339,"region":143,"url":504,"description":505,"useCases":300,"indexable":261},"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":507,"label":508,"issuer":509,"region":510,"url":511,"description":512,"useCases":322,"indexable":261},"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":514,"label":515,"issuer":516,"region":143,"url":517,"description":518,"useCases":519,"indexable":261},"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":521,"label":522,"issuer":523,"region":143,"url":524,"description":525,"useCases":519,"indexable":261},"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":527,"label":528,"issuer":529,"region":154,"url":530,"description":531,"useCases":247,"indexable":261},"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":533,"label":534,"issuer":339,"region":143,"url":535,"description":536,"useCases":247,"indexable":261},"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":538,"label":539,"issuer":540,"region":360,"url":541,"description":542,"useCases":247,"indexable":261},"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.",1790598303479]