report.object
AI models for banking, capital markets, and insurance
Expert-reviewed from public records, platform data, primary sources, and private research beyond AI search. Use /api/v1/reports/RPT_01KSHN11BSWF9E8EAJ1WVS9FG4/sample before purchase, then download PDF, JSON, or Markdown after checkout.
page.markdown
AI models for banking, capital markets, and insurance.md
# AI models for banking, capital markets, and insurance
Compares AI models across banking, capital markets and insurance by source authority, customer impact, market proximity, model risk, retrieval quality, benchmark evidence, deployment channel, data rights and cost per accepted financial output.
## Object
| field | value |
| ---------------- | ------------------------------ |
| report_id | RPT_01KSHN11BSWF9E8EAJ1WVS9FG4 |
| schema_version | 2026-05-28 |
| last_updated_at | 2026-06-16 |
| category | MODELS |
| sector | Financial services |
| region | GLOBAL |
| confidence_score | 0.87 |
| source_count | 90 |
| evidence_count | 108 |
## Endpoints
| action | endpoint |
| ------------ | --------------------------------------------------------- |
| summary | /api/v1/reports/RPT_01KSHN11BSWF9E8EAJ1WVS9FG4 |
| sample | /api/v1/reports/RPT_01KSHN11BSWF9E8EAJ1WVS9FG4/sample |
| schema | /api/v1/reports/RPT_01KSHN11BSWF9E8EAJ1WVS9FG4/schema |
| quote | /api/v1/reports/RPT_01KSHN11BSWF9E8EAJ1WVS9FG4/quote |
| purchase | /checkout/RPT_01KSHN11BSWF9E8EAJ1WVS9FG4?view=agent |
| versions | /api/v1/reports/RPT_01KSHN11BSWF9E8EAJ1WVS9FG4/versions |
| changelog | /api/v1/reports/RPT_01KSHN11BSWF9E8EAJ1WVS9FG4/changelog |
| monitor | /api/v1/monitors?report_id=RPT_01KSHN11BSWF9E8EAJ1WVS9FG4 |
| mcp_resource | colophon://reports/RPT_01KSHN11BSWF9E8EAJ1WVS9FG4/sample |
## Coverage
| field | value |
| -------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| category | MODELS |
| sector | Financial services |
| region | GLOBAL |
| topics | banking AI, capital markets AI, insurance AI, financial models, model risk management, financial RAG, credit scoring, AML and fraud, market surveillance, investment research, insurance underwriting, claims AI, financial benchmarks, AI governance, DORA, EU AI Act |
| source_classes | regulator, regulator_survey, regulation, vendor_docs, case_study, privacy_terms, cloud_docs, financial_benchmark, public_data_api, standard, financial_data, financial_crime, payments_ai, insurance_ai, model_benchmark, market_analysis, status_page, official_model_docs, pricing_page, agent_benchmark, market_survey |
| update_cadence | Weekly |
## Sample JSON
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"schema_version": "2026-05-28",
"title": "AI models for banking, capital markets, and insurance",
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"known_gaps": [
"Public evidence is thin for regulator-ready credit adverse-action outputs and customer-outcome testing.",
"Insurance AI evidence varies by state, line, claim type and policy form; public examples rarely include fairness slices.",
"Capital-markets model evidence rarely exposes live entitlements, market-data timestamps, internal restrictions or client-supervision decisions.",
"AML, fraud, market-surveillance and claims-fraud routes rarely publish false-negative rates or severity-weighted outcomes.",
"Provider case studies rarely include denominator data, rejected outputs, reviewer minutes or audit-export quality.",
"Open-weight and self-hosted routes depend on private infrastructure utilization, validation and security evidence.",
"EU AI Act, DORA, NAIC and securities-regulator updates can move route classification before annual review.",
"The demand signal for narrower banking, market and insurance tracks is not yet source-complete enough to split this control map further."
]
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"title": "AI models for banking, capital markets, and insurance",
"subtitle": "Approve the regulated output, not the clever model.",
"abstract": "Compares AI models across banking, capital markets and insurance by source authority, customer impact, market proximity, model risk, retrieval quality, benchmark evidence, deployment channel, data rights and cost per accepted financial output.",
"category": "MODELS",
"sector": "Financial services",
"region": "GLOBAL",
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"transformChain": [],
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"url": "https://www.fca.org.uk/firms/innovation/ai-approach"
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"accessedAt": "2026-06-16",
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"url": "https://www.finra.org/rules-guidance/notices/24-09"
},
{
"id": "S6",
"label": "FINRA - GenAI adversarial-use threat intelligence series",
"sourceClass": "regulator",
"accessedAt": "2026-06-16",
"status": "verified",
"access": "sample",
"transformChain": [],
"url": "https://www.finra.org/compliance-tools/Industry_Risks_and_Threats/genaitips"
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"id": "S7",
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{
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"accessedAt": "2026-06-16",
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"url": "https://openai.com/index/morgan-stanley/"
},
{
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"accessedAt": "2026-06-16",
"status": "verified",
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"url": "https://arxiv.org/abs/2311.11944"
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"evidence": [
{
"id": "E1",
"sourceId": "S1",
"claimIds": [
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"summary": "The FSB says AI adoption in finance may amplify vulnerabilities including third-party dependencies, market correlations, cyber risk and model risk, while financial authorities face monitoring challenges because data on AI usage is limited.",
"status": "verified",
"confidence": 0.87,
"access": "sample"
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{
"id": "E3",
"sourceId": "S2",
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"summary": "The Bank of England and FCA survey groups respondents into UK banks, international banks, insurance, investment and capital markets, non-bank lending, FMIs, payments and other firms, which maps directly onto separate model routes.",
"status": "verified",
"confidence": 0.87,
"access": "sample"
},
{
"id": "E4",
"sourceId": "S2",
"claimIds": [
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"summary": "The 2024 UK survey received 118 responses and was designed to understand AI capabilities, development, deployment and use across regulated financial firms, including generative AI.",
"status": "verified",
"confidence": 0.87,
"access": "sample"
},
{
"id": "E13",
"sourceId": "S11",
"claimIds": [
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"C5"
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"summary": "The EU AI Act places certain creditworthiness, credit scoring and life or health insurance risk-assessment or pricing systems in high-risk territory, which changes the approval route for customer-impacting models.",
"status": "verified",
"confidence": 0.87,
"access": "sample"
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"id": "E16",
"sourceId": "S14",
"claimIds": [
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"summary": "Morgan Stanley's OpenAI case study says more than 98% of advisor teams actively use AI @ Morgan Stanley Assistant and that the deployment used an eval framework, retrieval tuning and quality assurance.",
"status": "verified",
"confidence": 0.87,
"access": "sample"
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{
"id": "E26",
"sourceId": "S23",
"claimIds": [
"C6"
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"summary": "FinanceBench contains 10,231 open-book financial QA questions with answers and evidence strings; on a public sample, GPT-4-Turbo with retrieval incorrectly answered or refused 81% of questions.",
"status": "verified",
"confidence": 0.87,
"access": "sample"
},
{
"id": "E48",
"sourceId": "S2",
"claimIds": [
"C1"
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"summary": "The Bank of England and FCA sector taxonomy supports a three-lane control map for banking, investment and capital markets, and insurance before splitting into narrower subvertical tracks.",
"status": "verified",
"confidence": 0.87,
"access": "sample"
},
{
"id": "E49",
"sourceId": "S23",
"claimIds": [
"C6"
],
"summary": "FinanceBench's evidence-string design shows why a route eval should keep source strings and accepted-output records, not only model answers.",
"status": "verified",
"confidence": 0.87,
"access": "sample"
},
{
"id": "E50",
"sourceId": "S7",
"claimIds": [
"C5"
],
"summary": "The NAIC bulletin applies across insurer AI systems rather than one product line, which supports keeping insurance in the same regulated-finance control map until line-specific evidence warrants a split.",
"status": "verified",
"confidence": 0.87,
"access": "sample"
}
],
"claims": [
{
"id": "C1",
"statement": "Banking, capital markets and insurance need a dedicated model control map below the broad financial-model layer because their approvals turn on different customer, market and policy consequences.",
"confidence": 0.9,
"evidenceIds": [
"E3",
"E4",
"E48"
],
"access": "sample"
},
{
"id": "C2",
"statement": "The useful procurement unit is a model route, not a model name: source, data class, model lane, host, tool permission, reviewer and reapproval trigger move together.",
"confidence": 0.9,
"evidenceIds": [
"E1"
],
"access": "sample"
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{
"id": "C3",
"statement": "Banking controls should split employee assistance, customer communications, credit, AML, fraud and payment authorization because the evidence, fairness, latency and write-path controls differ.",
"confidence": 0.89,
"evidenceIds": [
"E13"
],
"access": "sample"
},
{
"id": "C5",
"statement": "Insurance controls should split underwriting, pricing, claims, fraud, service and actuarial support because the control question changes by line, state and customer outcome.",
"confidence": 0.88,
"evidenceIds": [
"E13",
"E50"
],
"access": "sample"
},
{
"id": "C6",
"statement": "Financial model quality depends on retrieval, table capture, source authority, calculation trace and document parsing before generation quality can be trusted.",
"confidence": 0.91,
"evidenceIds": [
"E16",
"E26",
"E49"
],
"access": "sample"
}
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"colophon": {
"checkedAt": "2026-06-16",
"latestRevisionAt": "2026-06-16T00:00:00Z",
"reviewStandard": "expert_human_reviewed",
"kinetics": {
"speed": "moderate",
"impact": 0.95,
"momentum": 0.82,
"businessImpact": "Regulated-finance model approval has moved from model bake-off to route evidence across banking, markets and insurance.",
"rateOfChange": "Regulator signals, financial benchmarks, provider workflow releases and data-control terms now change faster than annual model reviews.",
"from": "one financial AI model shortlist",
"toward": "three route registers with source, reviewer, control and reapproval evidence"
},
"watch": [
"SEC Division of Examinations risk-alert page update naming AI representations by advisers or broker-dealers; capital-markets automated-tool evidence may need stronger substantiation [E6].",
"FINRA 2026 oversight or threat-intelligence update naming GenAI; fraud, account-takeover and market-deception tests may change [E8, E61].",
"NAIC AI model-bulletin implementation-map update adding or changing state-adoption status; insurance route requirements may diverge by state and line.",
"2 August 2026: EU AI Act GPAI enforcement powers begin; cross-border credit, insurance and provider-documentation routes may need renewed classification [E53].",
"Rogo BigFinanceBench public dataset or harness release adding retrieval, calculation or rubric failures; private eval packs may need new probes.",
"OpenAI enterprise privacy page change to training-use, retention or connector language; direct-API route approval may shift."
],
"gapAnalysis": "Comparable public evidence remains thin for credit adverse-action outputs, insurance line-specific fairness, live market-data entitlements, AML and fraud false negatives, model-provider concentration, regulator-ready audit exports and cost per accepted financial output."
},
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"dek": "The decision is control evidence.",
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"lead": "Do not buy one financial model for three different jobs. Banking, capital markets and insurance share a regulatory roof, but their model failures hurt people and markets in different ways.",
"body": [
"A banking route can affect credit, payments, fraud handling, complaints and customer treatment [E8, E11]. A capital-markets route can affect investment research, client communications, surveillance and trading-adjacent analysis [E34, E52]. An insurance route can affect underwriting, pricing, claims, fraud and actuarial judgement [E9, E46].",
"Use the broad financial-model layer for the full portfolio view. Use this control map to approve the regulated-finance use cases that sit below it. The object is still not a model name. It is the output path: source authority, data class, model lane, hosting path, reviewer, logs, action permissions and reapproval trigger [E1, E11].",
"The strongest public evidence says buyers should be humble. FinanceBench showed a GPT-4-Turbo retrieval route incorrectly answered or refused 81% of sampled financial questions, despite answers being grounded in public-company material [E26]. That is a filing-RAG warning, not a permanent model ranking."
],
"detail": [
"Keep banking, capital markets and insurance in one control map until demand justifies narrower tracks. The useful first split is a three-lane approval register. A retail-credit track, a market-surveillance track or a claims-pricing track is worth creating only when the source pack, owner and evaluation burden stop overlapping.",
"The finished output to measure is not a token. It is an approved credit explanation, resolved alert, cited research note, reviewed workbook, signed-off claims decision or archived customer message."
]
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"slug": "ai-models-financial-services",
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"dek": "Covers the full model portfolio, open-weight route, benchmark map, source-layer economics and cross-finance controls.",
"relation": "Related report"
},
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"dek": "Use the file-processing report for financial OCR, document AI, parser routes, bank statements, filings, invoices, IDs, claims files, controls, pricing and parser evals.",
"relation": "Related report"
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"question": "One control map or three?",
"answer": "One dedicated control map for the three regulated lanes now.",
"buyer_move": "Split later only when demand, source coverage and owner accountability diverge."
},
{
"question": "Which model wins?",
"answer": "The winning model changes by source, customer impact and action permission.",
"buyer_move": "Approve model lanes by control evidence, not by leaderboard."
},
{
"question": "Where is the main failure?",
"answer": "Wrong source, stale data, hidden table, bad formula or unsupported action.",
"buyer_move": "Test retrieval and evidence before prose."
},
{
"question": "What should procurement price?",
"answer": "Cost per accepted financial output.",
"buyer_move": "Count review minutes, false refusals, reruns, audit export and reapproval burden."
}
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"body": "Route means the approved path from work to accepted output: the source set, data boundary, model or vendor channel, tools, reviewer, evidence record and reapproval trigger that make an AI result usable."
}
]
},
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"dek": "The control problem became explicit.",
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"lead": "The main change is not that financial firms discovered AI. It is that the control perimeter caught up with the difference between assistant, model and action path.",
"body": [
"OCC Bulletin 2026-13 revised banking model-risk guidance while explicitly leaving generative and agentic AI outside scope, pending further work [E56, E67]. That creates a practical gap: banks still need control evidence for GenAI even when the traditional model-risk text does not settle every question.",
"Capital-markets and broker-dealer signals moved in parallel. IOSCO maps AI across advice, trading, research, surveillance and AML/CFT, while FINRA's 2026 material points to common GenAI uses such as summarization and information extraction alongside supervision, data and guardrail concerns [E60, E61].",
"For insurance, the control language is more customer-outcome specific. NAIC materials put governance, testing, documentation, proxy, fairness and complaint-handling questions close to underwriting and pricing routes [E9, E10]. NYDFS and EIOPA materials add governance and insurance-risk expectations for pricing and underwriting routes [E64, E65]."
],
"detail": [
"The result is a three-speed market. Frontier labs and cloud providers are shipping faster workflow wrappers. Regulators are clarifying sector expectations. Buyers are left to prove that each route is still fit for its duty."
]
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{
"old_question": "Which LLM is best?",
"new_question": "Which route clears this duty?",
"evidence_needed": "Source, reviewer, model version, action limit and reapproval trigger."
},
{
"old_question": "Can it summarize?",
"new_question": "Can it preserve the evidence that matters?",
"evidence_needed": "Source passage, table, formula, timestamp, permission and refusal log."
},
{
"old_question": "Can vendors claim AI?",
"new_question": "Are claims substantiated by actual operations?",
"evidence_needed": "Marketing, policy, workflow, model-use and audit evidence."
},
{
"old_question": "Is AI a separate policy?",
"new_question": "Which existing control owns the route?",
"evidence_needed": "Model risk, third-party risk, conduct, resilience and records mapping."
}
]
}
]
},
{
"id": "decision-frame",
"title": "Decision frame",
"access": "sample",
"dek": "Start with consequence, then choose the model lane.",
"claimIds": [
"C1",
"C2",
"C3",
"C5"
],
"evidenceIds": [
"E1",
"E3",
"E4",
"E13",
"E48",
"E50"
],
"blocks": [
{
"id": "decision-frame-narrative",
"evidenceIds": [
"E1",
"E3",
"E4",
"E13",
"E48",
"E50"
],
"type": "section_narrative",
"lead": "Financial AI splits cleanly when the buyer names the consequence of a bad output.",
"body": [
"If a model drafts an internal research note, the approval question is source quality and analyst review. If it influences credit, trading, insurance pricing or claims handling, the approval question becomes conduct, fairness, audit trail, human accountability and sometimes high-risk AI classification [E5, E13].",
"The Bank of England and FCA survey taxonomy is a useful cut: banks, insurance, investment and capital markets, FMIs, payments and adjacent firms have different operating risks [E3]. The control map keeps three main lanes because they share model, source and governance questions while retaining distinct acceptance tests."
],
"detail": [
"The model control should be written as a row, not narrated as a policy. Required row fields: use case, source corpus, data class, model lane, deployment channel, action permission, reviewer, log fields, fallback and reapproval trigger.",
"When those rows start naming different source owners, buyers and private eval packs, split the control map. Until then, a single regulated-finance control map prevents a false precision problem."
]
},
{
"id": "route-decision-tree",
"evidenceIds": [
"E1",
"E13"
],
"type": "decision_tree",
"lead": "Ask what the output can change.",
"steps": [
{
"prompt": "Can the output affect a customer, insured, investor, trade, payment, claim or credit decision?",
"tone": "warning",
"branches": [
{
"label": "Yes",
"outcome": "Regulated route.",
"next": "Add conduct, fairness, model-risk, evidence and human-approval gates.",
"tone": "blocked"
},
{
"label": "No",
"outcome": "Continue.",
"next": "Check data class, source authority and action permissions.",
"tone": "neutral"
}
]
},
{
"prompt": "Does the route use private customer, account, market, policy, claims or portfolio data?",
"tone": "warning",
"branches": [
{
"label": "Yes",
"outcome": "Controlled-data path.",
"next": "Approve retention, region, training use, subprocessors, access logs and deletion evidence.",
"tone": "warning"
},
{
"label": "No",
"outcome": "Continue.",
"next": "Check whether the route is source-authoritative.",
"tone": "neutral"
}
]
},
{
"prompt": "Can the system write, send, update, approve, trade, decline or settle?",
"tone": "warning",
"branches": [
{
"label": "Yes",
"outcome": "Action route.",
"next": "Require dry-run mode, tool limits, four-eye approval, audit export and rollback.",
"tone": "blocked"
},
{
"label": "No",
"outcome": "Assistant route.",
"next": "Run private evals and reviewer logging before scale.",
"tone": "success"
}
]
}
]
},
{
"id": "decision-frame-section-callout",
"title": "Keep the split useful",
"evidenceIds": [
"E3",
"E48",
"E50"
],
"type": "callout",
"tone": "info",
"body": "Banking, markets and insurance deserve one dedicated control map now. Separate tracks for retail credit, trading, claims or underwriting should wait for enough workflow volume, source coverage and accountable ownership."
}
]
},
{
"id": "model-economics",
"title": "Model economics",
"access": "sample",
"dek": "Price the finished regulated task.",
"claimIds": [
"C6"
],
"evidenceIds": [
"E16",
"E26",
"E49"
],
"blocks": [
{
"id": "model-economics-narrative",
"evidenceIds": [
"E16",
"E26",
"E49"
],
"type": "section_narrative",
"lead": "Token price is a weak denominator for regulated financial work.",
"body": [
"The expensive part of a financial AI route is often not the model call. It is finding the right source, preserving the table, checking the calculation, handling permission denial, recording reviewer decisions and proving the output after a complaint, exam or dispute [E26, E49].",
"Morgan Stanley's case study is useful because it describes an eval-led retrieval and quality-assurance process, not merely an assistant launch [E16, E17]. That is the economic pattern: spend money where it improves accepted-output rate and reduces reviewer rework.",
"Artificial Analysis-style comparisons are useful because they put price beside intelligence, output speed, latency and context [E105]. Banking, capital-markets and insurance buyers need one more column: accepted output. The unit is not a million tokens. It is an approved answer, reviewed memo, resolved alert, compliant message or claims decision.",
"Silicon Valley Bank's State of Enterprise Software survey adds a market warning: productivity and revenue are not the same claim. About 48% of respondents reported 25% or greater employee-productivity improvement from AI, but half said revenue-growth attribution was hard to measure and one-quarter put revenue lift below 10% [E106]. The same Silicon Valley Bank report also shows why seat price is a weak budgeting proxy: nearly 40% of respondents are primarily seat-priced today, but only 26% expect to stay there as AI labour augmentation and compute economics change pricing [E107]."
],
"detail": [
"Compare routes by total accepted-output cost: model usage, source licence, document processing, tool calls, latency, reruns, reviewer minutes, false negatives, false positives, audit exports and reapproval."
]
},
{
"id": "intelligence-price-decline-chart-split",
"title": "Token price misses regulated output",
"evidenceIds": [],
"type": "chart",
"chartType": "line",
"data": [
{
"date": "2024-03-04",
"seriesId": "token-0",
"seriesLabel": "Intelligence <10",
"metric": "token",
"panel": "token",
"tierIndex": 0,
"value": 0.1,
"valueLabel": "$0.1",
"model": "Llama 2 Chat 7B",
"observed": false,
"current": false
},
{
"date": "2024-04-18",
"seriesId": "token-0",
"seriesLabel": "Intelligence <10",
"metric": "token",
"panel": "token",
"tierIndex": 0,
"value": 0.07,
"valueLabel": "$0.07",
"model": "Llama 3 Instruct 8B",
"observed": true,
"current": false
},
{
"date": "2024-09-25",
"seriesId": "token-0",
"seriesLabel": "Intelligence <10",
"metric": "token",
"panel": "token",
"tierIndex": 0,
"value": 0.05,
"valueLabel": "$0.05",
"model": "Llama 3.2 Instruct 1B",
"observed": true,
"current": false
},
{
"date": "2025-06-26",
"seriesId": "token-0",
"seriesLabel": "Intelligence <10",
"metric": "token",
"panel": "token",
"tierIndex": 0,
"value": 0.025,
"valueLabel": "$0.025",
"model": "Gemma 3n E4B Instruct",
"observed": true,
"current": false
},
{
"date": "2026-03-02",
"seriesId": "token-0",
"seriesLabel": "Intelligence <10",
"metric": "token",
"panel": "token",
"tierIndex": 0,
"value": 0.02,
"valueLabel": "$0.02",
"model": "Qwen3.5 0.8B (Non-reasoning)",
"observed": true,
"current": false
},
{
"date": "2026-05-31",
"seriesId": "token-0",
"seriesLabel": "Intelligence <10",
"metric": "token",
"panel": "token",
"tierIndex": 0,
"value": 0.02,
"valueLabel": "$0.02",
"model": "Qwen3.5 0.8B (Non-reasoning)",
"observed": false,
"current": true
},
{
"date": "2024-03-04",
"seriesId": "token-1",
"seriesLabel": "Intelligence 10-20",
"metric": "token",
"panel": "token",
"tierIndex": 1,
"value": 0.15,
"valueLabel": "$0.15",
"model": "Solar Mini",
"observed": false,
"current": false
},
{
"date": "2024-07-23",
"seriesId": "token-1",
"seriesLabel": "Intelligence 10-20",
"metric": "token",
"panel": "token",
"tierIndex": 1,
"value": 0.1,
"valueLabel": "$0.1",
"model": "Llama 3.1 Instruct 8B",
"observed": true,
"current": false
},
{
"date": "2024-11-18",
"seriesId": "token-1",
"seriesLabel": "Intelligence 10-20",
"metric": "token",
"panel": "token",
"tierIndex": 1,
"value": 0.088,
"valueLabel": "$0.088",
"model": "Qwen2.5 Turbo",
"observed": true,
"current": false
},
{
"date": "2024-12-03",
"seriesId": "token-1",
"seriesLabel": "Intelligence 10-20",
"metric": "token",
"panel": "token",
"tierIndex": 1,
"value": 0.061,
"valueLabel": "$0.061",
"model": "Nova Micro",
"observed": true,
"current": false
},
{
"date": "2026-02-25",
"seriesId": "token-1",
"seriesLabel": "Intelligence 10-20",
"metric": "token",
"panel": "token",
"tierIndex": 1,
"value": 0.052,
"valueLabel": "$0.052",
"model": "LFM2 24B A2B",
"observed": true,
"current": false
},
{
"date": "2026-03-02",
"seriesId": "token-1",
"seriesLabel": "Intelligence 10-20",
"metric": "token",
"panel": "token",
"tierIndex": 1,
"value": 0.02,
"valueLabel": "$0.02",
"model": "Qwen3.5 0.8B (Reasoning)",
"observed": true,
"current": false
},
{
"date": "2026-05-31",
"seriesId": "token-1",
"seriesLabel": "Intelligence 10-20",
"metric": "token",
"panel": "token",
"tierIndex": 1,
"value": 0.02,
"valueLabel": "$0.02",
"model": "Qwen3.5 0.8B (Reasoning)",
"observed": false,
"current": true
},
{
"date": "2024-09-12",
"seriesId": "token-2",
"seriesLabel": "Intelligence 20-30",
"metric": "token",
"panel": "token",
"tierIndex": 2,
"value": 28.875,
"valueLabel": "$28.9",
"model": "o1-preview",
"observed": true,
"current": false
},
{
"date": "2025-01-31",
"seriesId": "token-2",
"seriesLabel": "Intelligence 20-30",
"metric": "token",
"panel": "token",
"tierIndex": 2,
"value": 1.925,
"valueLabel": "$1.93",
"model": "o3-mini (high)",
"observed": true,
"current": false
},
{
"date": "2025-03-25",
"seriesId": "token-2",
"seriesLabel": "Intelligence 20-30",
"metric": "token",
"panel": "token",
"tierIndex": 2,
"value": 1.209,
"valueLabel": "$1.21",
"model": "DeepSeek V3 0324",
"observed": true,
"current": false
},
{
"date": "2025-04-14",
"seriesId": "token-2",
"seriesLabel": "Intelligence 20-30",
"metric": "token",
"panel": "token",
"tierIndex": 2,
"value": 0.7,
"valueLabel": "$0.7",
"model": "GPT-4.1 mini",
"observed": true,
"current": false
},
{
"date": "2025-07-21",
"seriesId": "token-2",
"seriesLabel": "Intelligence 20-30",
"metric": "token",
"panel": "token",
"tierIndex": 2,
"value": 0.356,
"valueLabel": "$0.356",
"model": "Qwen3 235B A22B 2507 Instruct",
"observed": true,
"current": false
},
{
"date": "2025-07-31",
"seriesId": "token-2",
"seriesLabel": "Intelligence 20-30",
"metric": "token",
"panel": "token",
"tierIndex": 2,
"value": 0.352,
"valueLabel": "$0.352",
"model": "Qwen3 Coder 30B A3B Instruct",
"observed": true,
"current": false
},
{
"date": "2025-08-05",
"seriesId": "token-2",
"seriesLabel": "Intelligence 20-30",
"metric": "token",
"panel": "token",
"tierIndex": 2,
"value": 0.088,
"valueLabel": "$0.088",
"model": "gpt-oss-20B (high)",
"observed": true,
"current": false
},
{
"date": "2026-03-02",
"seriesId": "token-2",
"seriesLabel": "Intelligence 20-30",
"metric": "token",
"panel": "token",
"tierIndex": 2,
"value": 0.06,
"valueLabel": "$0.06",
"model": "Qwen3.5 4B (Non-reasoning)",
"observed": true,
"current": false
},
{
"date": "2026-05-31",
"seriesId": "token-2",
"seriesLabel": "Intelligence 20-30",
"metric": "token",
"panel": "token",
"tierIndex": 2,
"value": 0.06,
"valueLabel": "$0.06",
"model": "Qwen3.5 4B (Non-reasoning)",
"observed": false,
"current": true
},
{
"date": "2024-12-05",
"seriesId": "token-3",
"seriesLabel": "Intelligence 30-40",
"metric": "token",
"panel": "token",
"tierIndex": 3,
"value": 26.25,
"valueLabel": "$26.3",
"model": "o1",
"observed": true,
"current": false
},
{
"date": "2025-02-19",
"seriesId": "token-3",
"seriesLabel": "Intelligence 30-40",
"metric": "token",
"panel": "token",
"tierIndex": 3,
"value": 0.35,
"valueLabel": "$0.35",
"model": "Grok 3 mini Reasoning (high)",
"observed": true,
"current": false
},
{
"date": "2025-08-05",
"seriesId": "token-3",
"seriesLabel": "Intelligence 30-40",
"metric": "token",
"panel": "token",
"tierIndex": 3,
"value": 0.262,
"valueLabel": "$0.262",
"model": "gpt-oss-120b (high)",
"observed": true,
"current": false
},
{
"date": "2025-12-16",
"seriesId": "token-3",
"seriesLabel": "Intelligence 30-40",
"metric": "token",
"panel": "token",
"tierIndex": 3,
"value": 0.15,
"valueLabel": "$0.15",
"model": "MiMo-V2-Flash (Non-reasoning)",
"observed": true,
"current": false
},
{
"date": "2026-03-02",
"seriesId": "token-3",
"seriesLabel": "Intelligence 30-40",
"metric": "token",
"panel": "token",
"tierIndex": 3,
"value": 0.113,
"valueLabel": "$0.113",
"model": "Qwen3.5 9B (Reasoning)",
"observed": true,
"current": false
},
{
"date": "2026-05-31",
"seriesId": "token-3",
"seriesLabel": "Intelligence 30-40",
"metric": "token",
"panel": "token",
"tierIndex": 3,
"value": 0.113,
"valueLabel": "$0.113",
"model": "Qwen3.5 9B (Reasoning)",
"observed": false,
"current": true
},
{
"date": "2025-06-10",
"seriesId": "token-4",
"seriesLabel": "Intelligence 40-50",
"metric": "token",
"panel": "token",
"tierIndex": 4,
"value": 35,
"valueLabel": "$35",
"model": "o3-pro",
"observed": true,
"current": false
},
{
"date": "2025-07-10",
"seriesId": "token-4",
"seriesLabel": "Intelligence 40-50",
"metric": "token",
"panel": "token",
"tierIndex": 4,
"value": 11,
"valueLabel": "$11",
"model": "Grok 4",
"observed": true,
"current": false
},
{
"date": "2025-08-07",
"seriesId": "token-4",
"seriesLabel": "Intelligence 40-50",
"metric": "token",
"panel": "token",
"tierIndex": 4,
"value": 0.688,
"valueLabel": "$0.688",
"model": "GPT-5 mini (high)",
"observed": true,
"current": false
},
{
"date": "2025-12-01",
"seriesId": "token-4",
"seriesLabel": "Intelligence 40-50",
"metric": "token",
"panel": "token",
"tierIndex": 4,
"value": 0.337,
"valueLabel": "$0.337",
"model": "DeepSeek V3.2 (Reasoning)",
"observed": true,
"current": false
},
{
"date": "2025-12-16",
"seriesId": "token-4",
"seriesLabel": "Intelligence 40-50",
"metric": "token",
"panel": "token",
"tierIndex": 4,
"value": 0.15,
"valueLabel": "$0.15",
"model": "MiMo-V2-Flash (Feb 2026)",
"observed": true,
"current": false
},
{
"date": "2026-05-31",
"seriesId": "token-4",
"seriesLabel": "Intelligence 40-50",
"metric": "token",
"panel": "token",
"tierIndex": 4,
"value": 0.15,
"valueLabel": "$0.15",
"model": "MiMo-V2-Flash (Feb 2026)",
"observed": false,
"current": true
},
{
"date": "2025-12-11",
"seriesId": "token-5",
"seriesLabel": "Intelligence 50+",
"metric": "token",
"panel": "token",
"tierIndex": 5,
"value": 4.813,
"valueLabel": "$4.81",
"model": "GPT-5.2 (xhigh)",
"observed": true,
"current": false
},
{
"date": "2026-02-19",
"seriesId": "token-5",
"seriesLabel": "Intelligence 50+",
"metric": "token",
"panel": "token",
"tierIndex": 5,
"value": 4.5,
"valueLabel": "$4.5",
"model": "Gemini 3.1 Pro Preview",
"observed": true,
"current": false
},
{
"date": "2026-04-02",
"seriesId": "token-5",
"seriesLabel": "Intelligence 50+",
"metric": "token",
"panel": "token",
"tierIndex": 5,
"value": 1.125,
"valueLabel": "$1.13",
"model": "Qwen3.6 Plus",
"observed": true,
"current": false
},
{
"date": "2026-04-24",
"seriesId": "token-5",
"seriesLabel": "Intelligence 50+",
"metric": "token",
"panel": "token",
"tierIndex": 5,
"value": 0.544,
"valueLabel": "$0.544",
"model": "DeepSeek V4 Pro (Reasoning, Max Effort)",
"observed": true,
"current": false
},
{
"date": "2026-05-31",
"seriesId": "token-5",
"seriesLabel": "Intelligence 50+",
"metric": "token",
"panel": "token",
"tierIndex": 5,
"value": 0.544,
"valueLabel": "$0.544",
"model": "DeepSeek V4 Pro (Reasoning, Max Effort)",
"observed": false,
"current": true
},
{
"date": "2025-03-12",
"seriesId": "aa-0",
"seriesLabel": "Intelligence <10",
"metric": "aa",
"panel": "bill",
"tierIndex": 0,
"value": 6.518,
"valueLabel": "$6.52",
"model": "Gemma 3 4B Instruct",
"observed": true,
"current": false
},
{
"date": "2025-06-26",
"seriesId": "aa-0",
"seriesLabel": "Intelligence <10",
"metric": "aa",
"panel": "bill",
"tierIndex": 0,
"value": 5.7215,
"valueLabel": "$5.72",
"model": "Gemma 3n E4B Instruct",
"observed": true,
"current": false
},
{
"date": "2026-05-31",
"seriesId": "aa-0",
"seriesLabel": "Intelligence <10",
"metric": "aa",
"panel": "bill",
"tierIndex": 0,
"value": 5.7215,
"valueLabel": "$5.72",
"model": "Gemma 3n E4B Instruct",
"observed": false,
"current": true
},
{
"date": "2024-03-04",
"seriesId": "aa-1",
"seriesLabel": "Intelligence 10-20",
"metric": "aa",
"panel": "bill",
"tierIndex": 1,
"value": 36.4377,
"valueLabel": "$36.4",
"model": "Claude 3 Haiku",
"observed": true,
"current": false
},
{
"date": "2024-07-23",
"seriesId": "aa-1",
"seriesLabel": "Intelligence 10-20",
"metric": "aa",
"panel": "bill",
"tierIndex": 1,
"value": 27.7078,
"valueLabel": "$27.7",
"model": "Llama 3.1 Instruct 8B",
"observed": true,
"current": false
},
{
"date": "2024-12-03",
"seriesId": "aa-1",
"seriesLabel": "Intelligence 10-20",
"metric": "aa",
"panel": "bill",
"tierIndex": 1,
"value": 6.3848,
"valueLabel": "$6.38",
"model": "Nova Micro",
"observed": true,
"current": false
},
{
"date": "2025-09-22",
"seriesId": "aa-1",
"seriesLabel": "Intelligence 10-20",
"metric": "aa",
"panel": "bill",
"tierIndex": 1,
"value": 4.4809,
"valueLabel": "$4.48",
"model": "Granite 4.0 H Small",
"observed": true,
"current": false
},
{
"date": "2026-05-31",
"seriesId": "aa-1",
"seriesLabel": "Intelligence 10-20",
"metric": "aa",
"panel": "bill",
"tierIndex": 1,
"value": 4.4809,
"valueLabel": "$4.48",
"model": "Granite 4.0 H Small",
"observed": false,
"current": true
},
{
"date": "2025-01-31",
"seriesId": "aa-2",
"seriesLabel": "Intelligence 20-30",
"metric": "aa",
"panel": "bill",
"tierIndex": 2,
"value": 314.7215,
"valueLabel": "$315",
"model": "o3-mini (high)",
"observed": true,
"current": false
},
{
"date": "2025-03-25",
"seriesId": "aa-2",
"seriesLabel": "Intelligence 20-30",
"metric": "aa",
"panel": "bill",
"tierIndex": 2,
"value": 59.2474,
"valueLabel": "$59.2",
"model": "DeepSeek V3 0324",
"observed": true,
"current": false
},
{
"date": "2025-04-14",
"seriesId": "aa-2",
"seriesLabel": "Intelligence 20-30",
"metric": "aa",
"panel": "bill",
"tierIndex": 2,
"value": 53.7334,
"valueLabel": "$53.7",
"model": "GPT-4.1 mini",
"observed": true,
"current": false
},
{
"date": "2025-07-31",
"seriesId": "aa-2",
"seriesLabel": "Intelligence 20-30",
"metric": "aa",
"panel": "bill",
"tierIndex": 2,
"value": 29.2907,
"valueLabel": "$29.3",
"model": "Qwen3 Coder 30B A3B Instruct",
"observed": true,
"current": false
},
{
"date": "2025-08-05",
"seriesId": "aa-2",
"seriesLabel": "Intelligence 20-30",
"metric": "aa",
"panel": "bill",
"tierIndex": 2,
"value": 7.6768,
"valueLabel": "$7.68",
"model": "gpt-oss-20B (low)",
"observed": true,
"current": false
},
{
"date": "2026-05-31",
"seriesId": "aa-2",
"seriesLabel": "Intelligence 20-30",
"metric": "aa",
"panel": "bill",
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"value": 7.6768,
"valueLabel": "$7.68",
"model": "gpt-oss-20B (low)",
"observed": false,
"current": true
},
{
"date": "2024-12-05",
"seriesId": "aa-3",
"seriesLabel": "Intelligence 30-40",
"metric": "aa",
"panel": "bill",
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{
"lane": "Groq Batch and Flex",
"how_it_works": "Groq's Batch API is a discounted async lane; Flex processing trades immediacy for access to available capacity rather than a lower price. [E101, E102]",
"good_for": "Throughput-sensitive work where the buyer can separate background processing from live assistant use.",
"watch": "Low published developer limits can still make a clever route unusable until the account, plan or enterprise path changes."
},
{
"lane": "Usage tiers and quota requests",
"how_it_works": "Spend tiers, usage tiers, quota requests and account-specific limits can change the ceiling before any model changes. [E79, E81, E82, E83, E85, E87]",
"good_for": "New projects that need to absorb a sudden file drop, launch backlog or review queue.",
"watch": "Tier language is not capacity evidence. Check the live project, account, subscription, region and deployment type."
},
{
"lane": "Provisioned or reserved capacity",
"how_it_works": "Azure PTUs and reservations, Vertex Provisioned Throughput and Bedrock Provisioned Throughput buy predictability rather than a smarter model. [E84, E86, E89]",
"good_for": "Recurring volume, deadline-sensitive work and routes where shared-capacity 429s would be business failures.",
"watch": "Commitments, idle capacity, regional availability, spillover rules and reservation terms belong in the price model."
}
]
},
{
"id": "model-pricing-mechanics-cache-lead",
"evidenceIds": [],
"type": "paragraph",
"text": "Prompt caching is easy to misunderstand. It is not model memory and it does not cache the answer. It reuses the provider's work from the repeated beginning of a prompt: instructions, tool schemas, output schema, playbook text, matter taxonomy or source bundle. If the next request starts the same way, the provider can bill and process that repeated prefix differently. If the route prepends a timestamp, request id or one-off user sentence before the stable material, the cache may miss.",
"variant": "body"
},
{
"id": "model-pricing-mechanics-cache-design-lead",
"evidenceIds": [],
"type": "paragraph",
"text": "Design the prompt like a production artefact. Put the durable, reusable context first; put the unique question last; record cache hits in the banking, capital-markets and insurance route register. Cache savings are real only when the route keeps a stable prefix and still produces regulated output.",
"variant": "body"
},
{
"id": "model-pricing-mechanics-cache-design-table",
"title": "How to make caching work",
"evidenceIds": [],
"type": "table",
"columns": [
{
"key": "design_rule",
"label": "Design rule",
"align": "left"
},
{
"key": "why_it_matters",
"label": "Why it matters",
"align": "left"
},
{
"key": "implementation",
"label": "Implementation",
"align": "left"
}
],
"rows": [
{
"design_rule": "Put stable material first",
"why_it_matters": "Most cache mechanisms reward an identical or explicitly marked prefix.",
"implementation": "Start with system instructions, tool schemas, output schema, jurisdiction rules, playbook text and reusable source packs."
},
{
"design_rule": "Put variable material last",
"why_it_matters": "A unique timestamp, request id, user aside or matter-specific fact at the top can break a prefix match.",
"implementation": "Append the specific question, run id, new facts, reviewer note and requested output after the cacheable prefix."
},
{
"design_rule": "Keep tools and schemas stable",
"why_it_matters": "Tool definitions, JSON schemas and message ordering can be part of the cache key.",
"implementation": "Version schemas deliberately; avoid rebuilding tool lists, whitespace, examples or column order on every call."
},
{
"design_rule": "Warm the cache before fan-out",
"why_it_matters": "Parallel extraction can waste money if every worker pays to prefill the same long context.",
"implementation": "Send one cacheable request for the source pack or playbook, then fan out clause, entity, citation or issue tasks."
},
{
"design_rule": "Cache source packs, not shortcuts",
"why_it_matters": "The cache should preserve approved context, not hide missing retrieval or stale policy.",
"implementation": "Attach source version, route version and expiry; rebuild the prefix when authority, playbook or source set changes."
},
{
"design_rule": "Measure cached-token economics",
"why_it_matters": "A cache discount that never hits is procurement fiction.",
"implementation": "Log cache reads, writes, cached tokens, hit rate, latency and accepted-output rate beside the normal token bill."
},
{
"design_rule": "Respect the data boundary",
"why_it_matters": "Caching is still provider processing under the approved route.",
"implementation": "Cache only context that is allowed inside the approved data boundary. Caching is a billing and latency control, not permission to reuse one client's facts in another client's work."
}
]
},
{
"id": "model-pricing-mechanics-provider-cache-table",
"title": "Provider caching is not one thing",
"evidenceIds": [],
"type": "table",
"columns": [
{
"key": "provider",
"label": "Provider",
"align": "left"
},
{
"key": "mechanic",
"label": "Mechanic",
"align": "left"
},
{
"key": "buyer_read",
"label": "Buyer read",
"align": "left"
}
],
"rows": [
{
"provider": "OpenAI",
"mechanic": "Automatic prompt caching applies to repeated prompt prefixes above the documented threshold; usage surfaces cached tokens, and prompt-cache keys can help route related work. [E91]",
"buyer_read": "Keep the first part of the prompt identical: instructions, tools, schema and reusable source context before matter-specific text."
},
{
"provider": "Anthropic",
"mechanic": "Claude uses explicit cache-control breakpoints over tools, system and message content, with separate cache write and read economics and documented cache durations. [E93]",
"buyer_read": "Design cache checkpoints deliberately. The best target is the long shared context, not the short one-off question."
},
{
"provider": "Gemini / Vertex AI",
"mechanic": "Vertex supports context caching, including explicit cached content and implicit prefix caching; pricing separates cached input from ordinary input and batch treatment. [E94, E95]",
"buyer_read": "Check whether the route uses explicit cache objects, implicit prefix reuse or batch prediction; each has different cost and expiry behaviour."
},
{
"provider": "Amazon Bedrock",
"mechanic": "Bedrock prompt caching uses cache points with time-to-live controls and separate cache pricing, while batch inference has its own feature constraints. [E97, E98, E99]",
"buyer_read": "Do not assume a cached interactive prompt can simply move into batch. Confirm feature support by model and region."
},
{
"provider": "Groq",
"mechanic": "Groq prompt caching is automatic for exact repeated prefixes above the documented threshold, with discounted cached tokens and separate treatment in rate-limit accounting. [E100]",
"buyer_read": "The route must keep a stable prefix. This matters most when rate limits are low and large token packets would otherwise queue."
}
]
},
{
"id": "model-pricing-mechanics-approval-lead",
"evidenceIds": [],
"type": "paragraph",
"text": "The practical question is whether the buyer can deliberately choose the cheap lane without breaking the work. A cheaper async run is useful for overnight extraction. It is dangerous for an answer needed during a signing call. A cache hit is useful when the prefix is stable. It is imaginary when every prompt is rebuilt from scratch.",
"variant": "body"
},
{
"id": "model-pricing-mechanics-approval-table",
"title": "What to prove before calling the route cheap",
"evidenceIds": [],
"type": "table",
"columns": [
{
"key": "test",
"label": "Test",
"align": "left"
},
{
"key": "proof",
"label": "Proof",
"align": "left"
},
{
"key": "pass",
"label": "Pass condition",
"align": "left"
}
],
"rows": [
{
"test": "Interactive versus async split",
"proof": "Tag each workflow step as user-blocking, same-day, overnight or archive work.",
"pass": "Only non-blocking work moves to batch, flex or lower-priority processing."
},
{
"test": "Batch economics",
"proof": "Run the same packet through interactive and batch lanes, including failed files, retries, queue time and output handling.",
"pass": "The cheaper lane still produces accepted regulated output inside the work window."
},
{
"test": "Cache hit rate",
"proof": "Log cache reads, writes, cached tokens or provider-equivalent fields by route version.",
"pass": "The actual hit rate is high enough to change the finished-task bill, not just the theoretical token price."
},
{
"test": "Tier and capacity path",
"proof": "Capture account tier, quota, regional capacity, provisioned-capacity option and support escalation.",
"pass": "The buyer knows which spend, quota or reserved-capacity lever unlocks the next workload level."
},
{
"test": "Accepted-output denominator",
"proof": "Combine model spend, queue time, cache hit rate, reviewer minutes, failed runs and rework.",
"pass": "The route is cheaper per regulated output, not merely cheaper per million tokens."
}
]
},
{
"id": "accepted-output-cost-table",
"title": "Cost per accepted output",
"evidenceIds": [
"E16",
"E26",
"E49"
],
"type": "table",
"columns": [
{
"key": "cost_item",
"label": "Cost item",
"align": "left"
},
{
"key": "token_view",
"label": "Token view",
"align": "left"
},
{
"key": "accepted_output_view",
"label": "Accepted-output view",
"align": "left"
},
{
"key": "measurement",
"label": "Measurement",
"align": "left"
}
],
"rows": [
{
"cost_item": "Retrieval",
"token_view": "Tokens used.",
"accepted_output_view": "Correct source support.",
"measurement": "Correct source recall and unsupported-answer rate."
},
{
"cost_item": "Calculation",
"token_view": "Answer fluency.",
"accepted_output_view": "Verified calculation.",
"measurement": "Intermediate values, formula trace and reviewer acceptance."
},
{
"cost_item": "Fraud and AML",
"token_view": "Alert summary quality.",
"accepted_output_view": "Resolved investigation.",
"measurement": "False-negative rate, false-positive rate and investigator disposition."
},
{
"cost_item": "Customer output",
"token_view": "Message generation cost.",
"accepted_output_view": "Approved communication.",
"measurement": "Approved communication with source, reason, complaint flag and archive record."
},
{
"cost_item": "Model change",
"token_view": "New model price.",
"accepted_output_view": "Reapproved route.",
"measurement": "Holdout rerun cost and route reapproval burden."
}
]
},
{
"id": "model-pricing-mechanics-rule",
"title": "Pricing mechanics rule",
"evidenceIds": [],
"type": "callout",
"tone": "info",
"body": "Do not compare only the model row. First choose the lane: interactive, batch, lower-priority or flex, cached, provisioned, reserved or self-hosted. The same model can have different cost, latency, quota and review economics depending on the lane."
},
{
"id": "model-economics-section-callout",
"title": "Accepted output is the unit",
"evidenceIds": [
"E26",
"E49"
],
"type": "callout",
"tone": "info",
"body": "A cheap model is expensive when it adds source checks, false negatives, complaint files, rework or route reapproval."
}
]
},
{
"id": "throughput-rate-limits",
"title": "Throughput and rate limits",
"access": "locked",
"dek": "Rate limits decide whether the model can survive regulated workflow volume.",
"claimIds": [],
"evidenceIds": [],
"blocks": [
{
"id": "throughput-rate-limits-locked",
"title": "Locked section",
"evidenceIds": [],
"type": "callout",
"tone": "locked",
"body": "Purchase or subscribe to unlock this section."
}
]
},
{
"id": "model-lanes",
"title": "Model lanes",
"access": "locked",
"dek": "Different duties need different model families.",
"claimIds": [],
"evidenceIds": [],
"blocks": [
{
"id": "model-lanes-locked",
"title": "Locked section",
"evidenceIds": [],
"type": "callout",
"tone": "locked",
"body": "Purchase or subscribe to unlock this section."
}
]
},
{
"id": "use-case-routing",
"title": "Use-case route selection",
"access": "locked",
"dek": "Approve by work type, source and consequence.",
"claimIds": [],
"evidenceIds": [],
"blocks": [
{
"id": "use-case-routing-locked",
"title": "Locked section",
"evidenceIds": [],
"type": "callout",
"tone": "locked",
"body": "Purchase or subscribe to unlock this section."
}
]
},
{
"id": "benchmarks",
"title": "Evidence and benchmarks",
"access": "locked",
"dek": "Public benchmarks tell buyers what to test privately.",
"claimIds": [],
"evidenceIds": [],
"blocks": [
{
"id": "benchmarks-locked",
"title": "Locked section",
"evidenceIds": [],
"type": "callout",
"tone": "locked",
"body": "Purchase or subscribe to unlock this section."
}
]
},
{
"id": "source-layer",
"title": "Source layer",
"access": "locked",
"dek": "The model can only know the approved source.",
"claimIds": [],
"evidenceIds": [],
"blocks": [
{
"id": "source-layer-locked",
"title": "Locked section",
"evidenceIds": [],
"type": "callout",
"tone": "locked",
"body": "Purchase or subscribe to unlock this section."
}
]
},
{
"id": "banking-route",
"title": "Banking control path",
"access": "locked",
"dek": "Banking AI is a permissions and consequence problem.",
"claimIds": [],
"evidenceIds": [],
"blocks": [
{
"id": "banking-route-locked",
"title": "Locked section",
"evidenceIds": [],
"type": "callout",
"tone": "locked",
"body": "Purchase or subscribe to unlock this section."
}
]
},
{
"id": "capital-markets-route",
"title": "Capital-markets control path",
"access": "locked",
"dek": "Keep research far from execution until controls prove otherwise.",
"claimIds": [],
"evidenceIds": [],
"blocks": [
{
"id": "capital-markets-route-locked",
"title": "Locked section",
"evidenceIds": [],
"type": "callout",
"tone": "locked",
"body": "Purchase or subscribe to unlock this section."
}
]
},
{
"id": "insurance-route",
"title": "Insurance control path",
"access": "locked",
"dek": "Insurance models are local, line-specific and evidence-heavy.",
"claimIds": [],
"evidenceIds": [],
"blocks": [
{
"id": "insurance-route-locked",
"title": "Locked section",
"evidenceIds": [],
"type": "callout",
"tone": "locked",
"body": "Purchase or subscribe to unlock this section."
}
]
},
{
"id": "controls",
"title": "Risks and controls",
"access": "locked",
"dek": "Control the model path, not the slogan.",
"claimIds": [],
"evidenceIds": [],
"blocks": [
{
"id": "controls-locked",
"title": "Locked section",
"evidenceIds": [],
"type": "callout",
"tone": "locked",
"body": "Purchase or subscribe to unlock this section."
}
]
},
{
"id": "deployment-model",
"title": "Deployment model",
"access": "locked",
"dek": "Choose deployment by data and action, not preference.",
"claimIds": [],
"evidenceIds": [],
"blocks": [
{
"id": "deployment-model-locked",
"title": "Locked section",
"evidenceIds": [],
"type": "callout",
"tone": "locked",
"body": "Purchase or subscribe to unlock this section."
}
]
},
{
"id": "cross-border-limits",
"title": "Cross-border limits",
"access": "locked",
"dek": "Global finance needs local control files.",
"claimIds": [],
"evidenceIds": [],
"blocks": [
{
"id": "cross-border-limits-locked",
"title": "Locked section",
"evidenceIds": [],
"type": "callout",
"tone": "locked",
"body": "Purchase or subscribe to unlock this section."
}
]
},
{
"id": "private-eval",
"title": "Private eval design",
"access": "locked",
"dek": "Measure the finished job.",
"claimIds": [],
"evidenceIds": [],
"blocks": [
{
"id": "private-eval-locked",
"title": "Locked section",
"evidenceIds": [],
"type": "callout",
"tone": "locked",
"body": "Purchase or subscribe to unlock this section."
}
]
},
{
"id": "opportunities",
"title": "Opportunities",
"access": "locked",
"dek": "Use models where the duty stays reviewable.",
"claimIds": [],
"evidenceIds": [],
"blocks": [
{
"id": "opportunities-locked",
"title": "Locked section",
"evidenceIds": [],
"type": "callout",
"tone": "locked",
"body": "Purchase or subscribe to unlock this section."
}
]
},
{
"id": "operational-signals",
"title": "Operational signals",
"access": "locked",
"dek": "Signals reopen approval; they do not prove fitness.",
"claimIds": [],
"evidenceIds": [],
"blocks": [
{
"id": "operational-signals-locked",
"title": "Locked section",
"evidenceIds": [],
"type": "callout",
"tone": "locked",
"body": "Purchase or subscribe to unlock this section."
}
]
},
{
"id": "expert-watchlist",
"title": "Expert watchlist",
"access": "locked",
"dek": "Follow people who change the test, not people who summarize the market.",
"claimIds": [],
"evidenceIds": [],
"blocks": [
{
"id": "expert-watchlist-locked",
"title": "Locked section",
"evidenceIds": [],
"type": "callout",
"tone": "locked",
"body": "Purchase or subscribe to unlock this section."
}
]
},
{
"id": "vendor-directory",
"title": "Vendor directory",
"access": "locked",
"dek": "Use the directory to route diligence by layer.",
"claimIds": [],
"evidenceIds": [],
"blocks": [
{
"id": "vendor-directory-locked",
"title": "Locked section",
"evidenceIds": [],
"type": "callout",
"tone": "locked",
"body": "Purchase or subscribe to unlock this section."
}
]
},
{
"id": "known-unknowns",
"title": "Known unknowns",
"access": "locked",
"dek": "Missing public evidence should become private tests.",
"claimIds": [],
"evidenceIds": [],
"blocks": [
{
"id": "known-unknowns-locked",
"title": "Locked section",
"evidenceIds": [],
"type": "callout",
"tone": "locked",
"body": "Purchase or subscribe to unlock this section."
}
]
},
{
"id": "recommended-next-steps",
"title": "Recommended next steps",
"access": "locked",
"dek": "Build the approval artefacts before scaling usage.",
"claimIds": [],
"evidenceIds": [],
"blocks": [
{
"id": "recommended-next-steps-locked",
"title": "Locked section",
"evidenceIds": [],
"type": "callout",
"tone": "locked",
"body": "Purchase or subscribe to unlock this section."
}
]
},
{
"id": "source-ledger",
"title": "Source ledger",
"access": "locked",
"dek": "Primary sources, benchmarks, regulatory anchors and source-layer references.",
"claimIds": [],
"evidenceIds": [],
"blocks": [
{
"id": "source-ledger-locked",
"title": "Locked section",
"evidenceIds": [],
"type": "callout",
"tone": "locked",
"body": "Purchase or subscribe to unlock this section."
}
]
},
{
"id": "glossary",
"title": "Glossary",
"access": "sample",
"claimIds": [],
"evidenceIds": [],
"blocks": [
{
"id": "glossary-table",
"title": "Terms used in this report",
"caption": "Definitions are reader aids, not separate evidence claims.",
"evidenceIds": [],
"type": "table",
"columns": [
{
"key": "term",
"label": "Term",
"align": "left"
},
{
"key": "meaning",
"label": "Plain meaning",
"align": "left"
}
],
"rows": [
{
"term": "Route",
"meaning": "The approved path from work to accepted output: source set, data boundary, model or vendor channel, tools, reviewer, evidence record and reapproval trigger."
},
{
"term": "API",
"meaning": "Application programming interface: a governed software connection used by an app or workflow to call a model or service."
},
{
"term": "SDK",
"meaning": "Software development kit: packaged code and tools developers use to connect a product to an API or service."
},
{
"term": "LLM",
"meaning": "Large language model: a model trained to read and generate text-like tokens."
},
{
"term": "Token",
"meaning": "A chunk of text used for billing, context windows and rate limits. It is usually smaller than a word, not a page count."
},
{
"term": "RPM",
"meaning": "Requests per minute: how many separate API calls the route can accept each minute."
},
{
"term": "RPD",
"meaning": "Requests per day: the daily cap on separate API calls."
},
{
"term": "TPM",
"meaning": "Tokens per minute: how much token traffic a route can process each minute, unless the provider splits input and output separately."
},
{
"term": "TPD",
"meaning": "Tokens per day: the daily cap on token traffic."
},
{
"term": "ITPM",
"meaning": "Input tokens per minute: how many prompt and source-document tokens may enter the model each minute."
},
{
"term": "OTPM",
"meaning": "Output tokens per minute: how many generated tokens may leave the model each minute."
},
{
"term": "PTU",
"meaning": "Provisioned throughput unit: reserved Azure OpenAI capacity bought for steadier throughput."
},
{
"term": "GSU",
"meaning": "Google scale unit: provisioned Vertex AI capacity used to reserve Gemini throughput."
},
{
"term": "MU",
"meaning": "Model Unit: Amazon Bedrock provisioned-throughput capacity reserved for a specific model route."
},
{
"term": "PayGo",
"meaning": "Pay as you go: shared cloud capacity billed by usage rather than pre-reserved throughput."
},
{
"term": "RAG",
"meaning": "Retrieval-augmented generation: search approved sources first, then pass selected evidence into the model."
},
{
"term": "OCR",
"meaning": "Optical character recognition: converting scanned or image text into machine-readable text."
},
{
"term": "SLA",
"meaning": "Service-level agreement: a contractual availability or support commitment, not observed uptime history."
},
{
"term": "JSON",
"meaning": "JavaScript Object Notation: a structured text format commonly used for machine-readable API output."
},
{
"term": "429",
"meaning": "HTTP 429 means too many requests: the route hit a rate limit or shared-capacity throttle."
}
]
}
]
}
]
}
},
"gap_analysis": "Comparable public evidence remains thin for credit adverse-action outputs, insurance line-specific fairness, live market-data entitlements, AML and fraud false negatives, model-provider concentration, regulator-ready audit exports and cost per accepted financial output.",
"kinetics": {
"speed": "moderate",
"impact": 0.95,
"momentum": 0.82,
"businessImpact": "Regulated-finance model approval has moved from model bake-off to route evidence across banking, markets and insurance.",
"rateOfChange": "Regulator signals, financial benchmarks, provider workflow releases and data-control terms now change faster than annual model reviews.",
"from": "one financial AI model shortlist",
"toward": "three route registers with source, reviewer, control and reapproval evidence"
},
"watch_signals": [
"SEC Division of Examinations risk-alert page update naming AI representations by advisers or broker-dealers; capital-markets automated-tool evidence may need stronger substantiation [E6].",
"FINRA 2026 oversight or threat-intelligence update naming GenAI; fraud, account-takeover and market-deception tests may change [E8, E61].",
"NAIC AI model-bulletin implementation-map update adding or changing state-adoption status; insurance route requirements may diverge by state and line.",
"2 August 2026: EU AI Act GPAI enforcement powers begin; cross-border credit, insurance and provider-documentation routes may need renewed classification [E53].",
"Rogo BigFinanceBench public dataset or harness release adding retrieval, calculation or rubric failures; private eval packs may need new probes.",
"OpenAI enterprise privacy page change to training-use, retention or connector language; direct-API route approval may shift."
],
"sample_findings": [
"Banking, capital markets and insurance need separate control-evidence maps beneath the broad financial-model layer."
]
}
```