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AICost Industry Solutions

Banking and capital markets

3 AI automations this industry runs today, each with the process, the AICost engine chain that prices and governs it, and the evidence behind it.

Cost engine Governance engine Each step priced and controlled. Click any engine to open its calculator or playground.
KYC/AML alert review and case investigationCommercial credit: spreading and credit memo draftingAdvisor and banker assistant (knowledge Q&A and meeting notes)

KYC/AML alert review and case investigation

Owner: Financial Crimes Compliance (FCC): KYC operations, AML investigations, BSA Officer

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Scale: Banks often assign 10-15% of FTEs to KYC/AML; agentic squads claim 200-2,000% productivity gains (McKinsey)

How the work runs

  1. Trigger: onboarding, periodic refresh or transaction-monitoring alert
  2. Gather data: registries, filings, adverse media, transactions
  3. Map ownership and identify UBOs; screen for PEPs and sanctions
  4. Analyze transactions and relationship purpose
  5. Draft the KYC memo or SAR narrative with a recommendation
  6. Human investigator decides and files (SAR); full audit trail kept

AICost engine chain

3 cost engines 5 governance engines

Each step priced and controlled. Click any engine to open its calculator or playground.

Where cost leaks

  • Multi-agent squads (4-5 agents per step, 10 steps) multiplying tokens per case
  • Adverse-media web searches and long filings per entity
  • Re-running full investigations on periodic refresh

Where risk sits

  • Missed suspicious activity or sanctions hit (regulatory enforcement)
  • Unexplainable alert closure under SR 11-7
  • Hallucinated facts in SAR narratives

Rules that apply

  • BSA/AML and FinCEN SAR rules
  • OFAC
  • SR 11-7 / OCC 2011-12 model risk management
  • EU AMLD and AMLA
  • GDPR

Runs on

NICE ActimizeSAS AMLOracle FCCMQuantexaLexisNexisMoody'sFenergoTemenos

Evidence

mckinsey.com ↗ey.com ↗

Commercial credit: spreading and credit memo drafting

Owner: Commercial / small-business lending: relationship managers, credit analysts, credit risk

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Scale: nCino claims application-to-decision time cut 62% for small-business lending (nCino nSight 2025, vendor claim)

How the work runs

  1. Collect financial statements, tax returns and the application
  2. Automated financial spreading and validation
  3. Risk rating and covenant analysis
  4. Draft the credit memo (borrower, industry, cash flow, risks)
  5. Credit officer review, approval and booking

AICost engine chain

5 cost engines 4 governance engines

Each step priced and controlled. Click any engine to open its calculator or playground.

Where cost leaks

  • Multi-year statements re-extracted for each renewal
  • Long memos regenerated per review comment
  • Bank-wide copilots priced per seat with low use

Where risk sits

  • Spreading errors flowing into risk ratings
  • Unexplainable adverse decisions
  • Bias in small-business credit

Rules that apply

  • SR 11-7 model risk
  • ECOA / Reg B adverse action reasons (CFPB circulars)
  • fair lending
  • EU AI Act Annex III creditworthiness (high risk for natural persons)
  • Colorado ADMT law (lending)

Runs on

nCino (Banking Advisor, agentic spreading)Moody's CreditLensSalesforce Financial Services CloudFISFiservJack Henry

Evidence

ncino.com ↗nasdaq.com ↗

Advisor and banker assistant (knowledge Q&A and meeting notes)

Owner: Wealth management advisors, investment bankers, front office; supervision and compliance

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Scale: Morgan Stanley: over 98% of advisor teams use the Assistant; corpus of 100,000 documents (OpenAI case study)

How the work runs

  1. Advisor asks the knowledge assistant over research and procedures
  2. Record the client meeting with consent
  3. Generate notes, action items and a follow-up email draft
  4. Save to CRM after advisor review
  5. Compliance supervision and record retention

AICost engine chain

5 cost engines 3 governance engines

Each step priced and controlled. Click any engine to open its calculator or playground.

Where cost leaks

  • Seat licenses for tens of thousands of employees
  • Large-corpus retrieval with long contexts per question
  • Meeting transcription of every call by default

Where risk sits

  • Unsuitable recommendations in AI-drafted client emails
  • Off-channel or unretained records
  • Confidential MNPI leaking across walls

Rules that apply

  • FINRA Rule 3110 supervision and FINRA Regulatory Notice 24-09
  • SEC 17a-4 / FINRA 4511 recordkeeping
  • Reg BI
  • GDPR

Runs on

AI @ Morgan Stanley Assistant and DebriefJPMorgan LLM SuiteSalesforce Financial Services CloudMicrosoft 365 Copilot

Evidence

openai.com ↗celent.com ↗

Reference chains are a starting point. Your volumes, models and vendors change the numbers; the engines recompute them from live prices.

Your process is not on the list, or runs differently? These chains are a starting point. We assemble the right engines for your workflow, your models and your vendors, and you run it over MCP or in the calculators.

Business solutions → Build my chain
📖 Data sources & methodology 172 text models · 9 embeddings · 44 vision · 62 audio · 8 vector DBs across 10 vendor pages · last verified 2026-10-15

Methodology

  • All prices are USD per 1 million tokens, current as of 2026-10-15.
  • Vendor-published values have no mark. Inferred/extrapolated values are marked with * and listed below.
  • Batch API discounts are 50% off standard rates across providers that offer Batch mode.
  • Prompt caching discounts vary by provider (typically 80-90% off cached input tokens).
  • Regional data-residency surcharges (Anthropic 1.1x, OpenAI 1.1x, Google regional tiers) are NOT included in base rates.
  • Long-context pricing tiers apply when input exceeds model threshold.
  • Embedding prices are input-only (no output tokens generated).

Primary sources

Last-verified date is the most recent successful daily snapshot (aicost_pricing_snapshots) or, when no snapshot exists yet, the latest successful crawler run (aicost_crawler_runs). 10 of 10 vendors are currently verified. Aggregator services (TokenCost, AI Pricing Guru, etc.) are not listed.

Anthropic
2026-10-14
https://www.anthropic.com/pricing
Daily snapshot since Sep 2023 · 700 days captured
Anthropic Docs
2026-10-14
https://platform.claude.com/docs/en/about-claude/pricing
Daily snapshot since Sep 2023 · 700 days captured
OpenAI
2026-10-14
https://openai.com/api/pricing/
Daily snapshot since Sep 2023 · 701 days captured
Google AI
2026-10-14
https://ai.google.dev/gemini-api/docs/pricing
Daily snapshot since Dec 2023 · 676 days captured
Google Vertex
2026-10-14
https://cloud.google.com/vertex-ai/generative-ai/pricing
Daily snapshot since Dec 2023 · 676 days captured
DeepSeek
2026-10-14
https://api-docs.deepseek.com/quick_start/pricing
Daily snapshot since May 2024 · 615 days captured
xAI
2026-10-14
https://x.ai/api
Daily snapshot since Nov 2024 · 533 days captured
Mistral
2026-10-14
https://mistral.ai/pricing
Daily snapshot since Dec 2023 · 674 days captured
Cohere
2026-10-14
https://cohere.com/pricing
Daily snapshot since Sep 2023 · 700 days captured
Voyage AI
2026-10-15
https://docs.voyageai.com/docs/pricing

Inferred values (marked with * in calculator tables)

Derived from industry conventions, not directly published by the vendor. Typical conventions: cached input = 10% of base (90% off), Batch API = 50% of base (50% off).

Vendor / Model Field Why it’s inferred
Anthropic — Claude Sonnet 4.6 cachedInput Derived at 10% of input rate — Anthropic publishes 90% cache-hit discount on this tier.
Anthropic — Claude Sonnet 4.5 cachedInput Derived at 10% of input rate; same 90% cache-hit convention as Sonnet 4.6.
Anthropic — Claude Sonnet 4.5 batchInput Derived at 50% of standard input — Anthropic documents uniform 50% Batch discount.
Anthropic — Claude Sonnet 4.5 batchOutput Derived at 50% of standard output — Anthropic documents uniform 50% Batch discount.
Anthropic — Claude Haiku 4.5 cachedInput Derived at 10% of input rate — Anthropic 90% cache-hit discount convention.
OpenAI — GPT-5.4 Mini cachedInput Derived at 10% of input — OpenAI documents automatic 90% discount on cache hits across GPT-5.x tier.
OpenAI — GPT-5.4 Nano cachedInput Derived at 10% of input — OpenAI 90% cache-hit convention.
OpenAI — GPT-5.4 Nano batchInput Derived at 50% of input — OpenAI Batch API uniform 50% discount.
OpenAI — GPT-5.4 Nano batchOutput Derived at 50% of output — OpenAI Batch API uniform 50% discount.
OpenAI — GPT-5.4 Pro cachedInput Derived at 10% of input — OpenAI 90% cache-hit convention.
OpenAI — GPT-5.4 Pro batchInput Derived at 50% of input — OpenAI Batch API uniform 50% discount.
OpenAI — GPT-5.4 Pro batchOutput Derived at 50% of output — OpenAI Batch API uniform 50% discount.
OpenAI — GPT-5.2 cachedInput Derived at 10% of input; no residency uplift.
OpenAI — GPT-5.2 batchInput Derived at 50% of input.
OpenAI — GPT-5.2 batchOutput Derived at 50% of output.
OpenAI — GPT-5 cachedInput Derived at 10% of input.
OpenAI — GPT-5 batchInput Derived at 50% of input.
OpenAI — GPT-5 batchOutput Derived at 50% of output.
OpenAI — GPT-5.5 Pro cachedInput Derived at 10% of input — OpenAI does not publish a cached rate for *-pro models; using the family convention.
OpenAI — GPT-5.5 Pro batchInput Derived at 50% of input.
OpenAI — GPT-5.5 Pro batchOutput Derived at 50% of output.
OpenAI — GPT-5.2 Pro cachedInput Derived at 10% of input — pro-tier convention.
OpenAI — GPT-5.2 Pro batchInput Derived at 50% of input.
OpenAI — GPT-5.2 Pro batchOutput Derived at 50% of output.
OpenAI — GPT-5.1 batchInput Derived at 50% of input.
OpenAI — GPT-5.1 batchOutput Derived at 50% of output.
OpenAI — GPT-5 Pro batchInput Derived at 50% of input.
OpenAI — GPT-5 Pro batchOutput Derived at 50% of output.
OpenAI — GPT-5 Nano cachedInput Derived at 10% of input.
OpenAI — GPT-5 Nano batchInput Derived at 50% of input.
OpenAI — GPT-5 Nano batchOutput Derived at 50% of output.
Google — Gemini 3 Flash cachedInput Derived at 10% of input — Google caching discount convention ~90%.
Google — Gemini 3.1 Flash-Lite cachedInput Derived at 10% of input — Google caching convention.
Google — Gemini 3.1 Flash-Lite batchInput Derived at 50% of input — Google Batch API uniform 50% discount.
Google — Gemini 3.1 Flash-Lite batchOutput Derived at 50% of output — Google Batch API uniform 50% discount.
Google — Gemini 2.5 Pro cachedInput Derived at 10% of input.
Google — Gemini 2.5 Flash cachedInput Derived at 10% of input.
Google — Gemini 2.5 Flash-Lite cachedInput Derived at 10% of input — Google caching convention.
Google — Gemini 2.5 Flash-Lite batchInput Derived at 50% of input — Google Batch API uniform 50% discount.
Google — Gemini 2.5 Flash-Lite batchOutput Derived at 50% of output — Google Batch API uniform 50% discount.
xAI — Grok 4 (legacy) cachedInput Extrapolated at 25% of base.

Pricing is cross-verified against the LiteLLM community registry when available. Daily snapshots are kept in aicost_pricing_snapshots; every change is logged to aicost_price_changelog with old & new values for full audit trail. Read the full methodology →