Digital Team

Digital Banking & Fintech
MCP Services

Ten MCP services for the Digital Banking team — each callable by any AI assistant to deliver fintech competitor analysis, neobank feature benchmarking, open banking API discovery, digital wallet ecosystem mapping, and payment infrastructure intelligence using web scraping, AI vision, and the 100M+ domain database.

1Fintech Competitor Analyzer

Scrapes fintech competitor websites for feature comparison — products, pricing tiers, tech stack, and integrations. Uses AI vision to compare UX design quality and identify competitive gaps in your digital banking offering.

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MCP Tool Definition
Web Scraping Vision AI Domain DB GPT-4o
fintech_competitor_analyzer competitors: array — List of fintech competitor domains to analyze analysis_type: string — "features" | "pricing" | "tech_stack" | "ux_design" | "all" include_screenshots: boolean — Capture and analyze UX screenshots (default: true) your_domain: string — Your product domain for gap analysis comparison
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AI Processing Pipeline
PROCESSING PIPELINE ════════════════════════════════════════════════════════════ Step 1: Scrape competitor /features, /pricing, /integrations, /docs pages Step 2: Screenshot key product pages for AI vision UX comparison Step 3: Query domain DB for competitor IAB categories, PageRank, tech signals Step 4: GPT-4o extracts: product features, pricing tiers, integration partners Step 5: Vision AI evaluates UX quality: layout, mobile-readiness, design maturity Step 6: Build feature comparison matrix with gap analysis vs. your product Step 7: Generate competitive positioning report with threat assessment
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Example Output
MCP RESPONSE — fintech_competitor_analyzer ════════════════════════════════════════════════════════════ COMPETITORS ANALYZED: 6 fintech domains | Mode: all FEATURE COMPARISON MATRIX: Instant Multi- Crypto API AI Payments Currency Trading Access Advisor revolut.com YES YES YES YES YES wise.com YES YES NO YES NO n26.com YES YES BETA NO NO YOUR PRODUCT YES NO NO BETA NO PRICING INTELLIGENCE: revolut.com — Standard: Free | Plus: $2.99/mo | Premium: $7.99/mo | Metal: $13.99/mo wise.com — Per-transfer fee: 0.41%-1.5% | No subscription model n26.com — Standard: Free | Smart: $4.90/mo | You: $9.90/mo | Metal: $16.90/mo UX DESIGN QUALITY (Vision AI): revolut.com — 92/100 — Clean, modern, excellent mobile-first design wise.com — 88/100 — Strong transparency focus, clear fee display n26.com — 85/100 — Minimalist, good onboarding flow YOUR PRODUCT — 62/100 — Dated layout, mobile responsiveness issues COMPETITIVE GAPS: Critical: Multi-currency support missing (all 3 competitors offer this) High: No AI-powered financial advisor feature Medium: API access still in beta (2 of 3 competitors fully launched) Medium: UX design trails competitors by 23-30 points

2Neobank Feature Benchmarker

Scrapes neobank and challenger bank websites to build feature comparison matrices — account types, fee structures, mobile capabilities, and API offerings — helping your digital team track the competitive landscape in real time.

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MCP Tool Definition
Web Scraping Domain DB GPT-4o
neobank_feature_benchmarker neobanks: array — List of neobank domains to benchmark (or use auto-discover) auto_discover: boolean — Find neobanks from domain DB by IAB category (default: false) categories: array — ["accounts","fees","mobile","api","cards","savings"] region: string — Geographic filter: "US","EU","UK","APAC","global"
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AI Processing Pipeline
PROCESSING PIPELINE ════════════════════════════════════════════════════════════ Step 1: Query domain DB for Financial Services IAB category, filter by neobank signals Step 2: Scrape /personal, /business, /pricing, /features, /cards pages per neobank Step 3: GPT-4o extracts account types, fee schedules, feature lists Step 4: Normalize data into standardized comparison matrix Step 5: Identify feature leaders and laggards per category Step 6: Generate benchmark report with trend indicators
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Example Output
MCP RESPONSE — neobank_feature_benchmarker ════════════════════════════════════════════════════════════ REGION: EU | Neobanks analyzed: 12 | Categories: all ACCOUNT TYPES: revolut.com — Personal, Business, Junior, Joint | 4 types n26.com — Standard, Smart, You, Metal, Business | 5 types monzo.com — Personal, Joint, Business, Under-18 | 4 types bunq.com — Easy Personal, Easy Business, Easy Green | 3 types FEE STRUCTURE BENCHMARK: Monthly ATM FX Fee Card Overdraft revolut.com Free* $0 0.00% Free N/A n26.com Free* $0 0.00% Free $0 setup monzo.com Free $0** 0.00% Free Available bunq.com $2.99 $0 0.50% Free N/A * Freemium — paid tiers unlock additional features ** Free up to $250/mo, then 3% fee MOBILE CAPABILITIES: Biometric login: 12/12 support (industry standard) Instant notifications: 12/12 support Card freeze/unfreeze: 11/12 support Spending analytics: 10/12 support Budgeting tools: 8/12 support Crypto trading: 5/12 support (emerging feature) Stock trading: 3/12 support (differentiator) FEATURE LEADERS: Most complete: revolut.com (92% feature coverage) Best fees: n26.com (lowest total cost of ownership) Best API: bunq.com (full PSD2 + proprietary API)

3Open Banking API Discovery

Searches the domain database for companies with /api or /docs pages in the financial services IAB category. Scrapes discovered endpoints to catalog available APIs, authentication methods, rate limits, and pricing models.

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MCP Tool Definition
Domain DB Web Scraping GPT-4o Page Signals
open_banking_api_discovery iab_categories: array — IAB categories to search (default: ["Financial Services"]) api_type: string — "psd2" | "account_info" | "payments" | "lending" | "all" region: string — Filter by country from domain DB (e.g. "UK","EU","US") min_pagerank: number — Minimum PageRank to filter established providers
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AI Processing Pipeline
PROCESSING PIPELINE ════════════════════════════════════════════════════════════ Step 1: Query domain DB for Financial Services domains with PageRank filter Step 2: Probe each domain for /api, /docs, /developers, /open-banking paths Step 3: Scrape discovered API documentation pages Step 4: GPT-4o extracts: endpoints, auth methods, rate limits, data formats Step 5: Classify APIs by type: PSD2, Account Info, Payments, Lending, KYC Step 6: Extract pricing models and partnership requirements Step 7: Generate API catalog with compatibility scoring
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Example Output
MCP RESPONSE — open_banking_api_discovery ════════════════════════════════════════════════════════════ SEARCH: Financial Services | Region: EU | Min PageRank: 3.0 RESULTS: 847 domains scanned | 142 with API documentation found PAYMENT APIs (38 providers): truelayer.com | PageRank: 5.4 | PRODUCTION-READY Type: PSD2 Account Info + Payment Initiation Auth: OAuth 2.0 | Format: REST/JSON Endpoints: /data/v1/accounts, /payments/v1/single-payment Rate limit: 10,000 req/min | Pricing: Per-API-call ($0.10-$0.50) Coverage: UK, EU (12 countries), 98% bank coverage plaid.com | PageRank: 6.1 | PRODUCTION-READY Type: Account Aggregation + Identity Verification Auth: API Key + Client Token | Format: REST/JSON Endpoints: /accounts/get, /transactions/get, /identity/get Rate limit: Custom | Pricing: Per-connection ($0.25-$3.00) Coverage: US, CA, UK, EU — 12,000+ institutions yapily.com | PageRank: 4.2 | PRODUCTION-READY Type: Open Banking Aggregation Auth: OAuth 2.0 | Format: REST/JSON Endpoints: /accounts, /transactions, /payments Rate limit: 5,000 req/min | Pricing: Tiered monthly plans Coverage: UK, EU (8 countries) — growing LENDING APIs (22 providers): creditkudos.com — Open banking credit scoring | PSD2 compliant affordability-api.co.uk — Income verification | UK only KYC/IDENTITY APIs (18 providers): onfido.com — Document + biometric verification | Global coverage sumsub.com — Full KYC/AML suite | 195 countries SUMMARY: 142 API providers cataloged across 6 categories 38 payment, 22 lending, 18 KYC, 28 data, 14 infrastructure, 12 other

4Digital Wallet Ecosystem Mapper

Maps the digital payment and wallet ecosystem by finding relevant domains in the database and scraping for integration partners, supported countries, transaction types, and merchant acceptance to visualize the competitive landscape.

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MCP Tool Definition
Domain DB Web Scraping GPT-4o Vision AI
digital_wallet_ecosystem_mapper wallet_domains: array — Specific wallet domains (or auto-discover from DB) ecosystem_scope: string — "consumer" | "merchant" | "p2p" | "crypto" | "all" map_depth: integer — 1 = direct partners, 2 = sub-integrations (default: 1) regions: array — Geographic regions to map ["NA","EU","APAC","LATAM"]
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AI Processing Pipeline
PROCESSING PIPELINE ════════════════════════════════════════════════════════════ Step 1: Query domain DB for digital payment/wallet companies by IAB category Step 2: Scrape /partners, /integrations, /merchants, /supported-countries pages Step 3: Screenshot wallet app pages for visual ecosystem mapping Step 4: GPT-4o extracts: partner networks, supported currencies, transaction types Step 5: Cross-reference integration partners in domain DB for validation Step 6: Build ecosystem dependency graph with market share estimates Step 7: Generate geographic coverage heat map and white-space analysis
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Example Output
MCP RESPONSE — digital_wallet_ecosystem_mapper ════════════════════════════════════════════════════════════ SCOPE: Consumer wallets | Regions: EU, NA | Domains mapped: 84 ECOSYSTEM MAP — TOP WALLETS: paypal.com | PageRank: 8.9 | DOMINANT Countries: 200+ | Currencies: 25 | Merchants: 35M+ Transaction types: P2P, Online, In-store (QR), Cross-border Integration partners: Shopify, WooCommerce, Stripe, Braintree Network effect: STRONG — self-reinforcing merchant-consumer loop applepay.apple.com | PageRank: 9.4 | DOMINANT Countries: 78 | Currencies: Local | Merchants: 85M+ terminals Transaction types: In-store (NFC), Online, In-app Integration partners: Visa, Mastercard, Amex (all major issuers) Network effect: STRONG — device ecosystem lock-in revolut.com | PageRank: 5.4 | GROWING Countries: 38 | Currencies: 30+ | Merchants: Via Visa network Transaction types: P2P, Online, In-store (card), Cross-border, Crypto Integration partners: Visa, Apple Pay, Google Pay Network effect: MODERATE — growing but card-network dependent GEOGRAPHIC COVERAGE HEAT MAP: North America: ████████████████████ Saturated (PayPal, Apple Pay, Google Pay) Western EU: ██████████████████░░ High coverage (PayPal, Revolut, N26) Eastern EU: ████████████░░░░░░░░ Moderate (local players + Revolut) LATAM: ████████░░░░░░░░░░░░ Emerging (MercadoPago, Nu Bank) Africa: ████░░░░░░░░░░░░░░░░ Low coverage (M-Pesa dominant) WHITE-SPACE OPPORTUNITIES: Eastern EU cross-border P2P — underserved by major wallets LATAM-to-EU remittance corridor — high demand, few digital options B2B payments in APAC — fragmented, no dominant wallet player

5Banking App Review Analyzer

Scrapes app store pages and review sites for banking app sentiment analysis, feature requests, and complaint patterns using AI classification — providing product teams with actionable voice-of-customer intelligence.

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MCP Tool Definition
Web Scraping GPT-4o Domain DB
banking_app_review_analyzer app_names: array — Banking app names or bundle IDs to analyze platforms: array — ["ios","android"] (default: both) review_count: integer — Maximum reviews per app to analyze (default: 500) time_period: string — "30d" | "90d" | "6mo" | "1yr" — review recency filter
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AI Processing Pipeline
PROCESSING PIPELINE ════════════════════════════════════════════════════════════ Step 1: Scrape App Store and Google Play listing pages for target apps Step 2: Extract reviews, ratings, review dates, and user metadata Step 3: Scrape third-party review sites (Trustpilot, G2) for additional data Step 4: GPT-4o classifies each review: feature request, bug, praise, complaint Step 5: Cluster complaints into themes: crashes, UX, fees, support, security Step 6: Identify trending feature requests and sentiment shifts over time Step 7: Generate comparative sentiment report across all analyzed apps
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Example Output
MCP RESPONSE — banking_app_review_analyzer ════════════════════════════════════════════════════════════ APPS ANALYZED: 5 banking apps | Period: 90 days | Reviews: 2,340 SENTIMENT OVERVIEW: Rating Sentiment Trend Reviews Revolut 4.6 +0.72 +0.08 1,247 Monzo 4.4 +0.61 -0.05 834 N26 3.8 +0.23 -0.18 412 Chime 4.5 +0.65 +0.03 1,089 YOUR APP 3.2 -0.14 -0.22 658 TOP COMPLAINT CLUSTERS (Your App): 1. App Crashes (28% of complaints) — "Crashes when checking balance" Frequency: +340% vs. prior period | Linked to v4.2.1 update 2. Slow Transfers (22%) — "Takes 3 days for transfers to clear" Competitor benchmark: Revolut instant, Monzo same-day 3. Customer Support (18%) — "Can't reach anyone, chatbot useless" Avg response time complaint: 48+ hours 4. Hidden Fees (14%) — "Charged $2.50 ATM fee not disclosed" TRENDING FEATURE REQUESTS: 1. Cryptocurrency trading — 847 mentions across all apps 2. Spending analytics/AI insights — 623 mentions 3. Virtual card numbers — 418 mentions 4. Carbon footprint tracking — 234 mentions PRIORITY ACTIONS: P0: Fix crash regression in v4.2.1 (destroying ratings) P1: Accelerate transfer speed (competitors set expectation at instant) P2: Improve support response time (target: under 4 hours)

6Fintech Funding Intelligence

Scrapes fintech company /press, /investors, and /about pages for funding announcements, valuations, and investor names. Enriches findings with domain DB signals like PageRank growth and web presence expansion to identify rising competitors.

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MCP Tool Definition
Web Scraping Domain DB GPT-4o Page Signals
fintech_funding_intelligence domains: array — Fintech domains to scan for funding signals iab_filter: string — IAB category to auto-discover fintechs from DB funding_stage: string — "seed" | "series_a" | "series_b" | "late_stage" | "all" min_amount: string — Minimum funding amount filter (e.g. "$10M")
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AI Processing Pipeline
PROCESSING PIPELINE ════════════════════════════════════════════════════════════ Step 1: Query domain DB for Financial Services/Fintech domains Step 2: Scrape /press, /news, /investors, /about pages for each domain Step 3: GPT-4o extracts: funding amounts, round types, investor names, dates Step 4: Cross-reference domain DB signals: PageRank growth, domain age, country Step 5: Correlate funding events with web presence expansion patterns Step 6: Score competitive threat level based on funding + digital growth Step 7: Generate funding intelligence report with threat rankings
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Example Output
MCP RESPONSE — fintech_funding_intelligence ════════════════════════════════════════════════════════════ SCAN: 234 fintech domains | Stage: all | Min: $5M RECENT FUNDING EVENTS (Last 90 days): neofinance-platform.com | PageRank: 4.8 (+1.2 QoQ) Series C: $120M | Valuation: ~$1.2B (unicorn) Lead: Andreessen Horowitz | Co: Tiger Global, Ribbit Capital Press: "Expanding into SME lending and cross-border payments" Web signal: Careers page grew from 12 to 67 postings Threat level: HIGH — direct competitor entering your market paytech-solutions.io | PageRank: 3.9 (+0.8 QoQ) Series B: $45M | Valuation: ~$350M Lead: Index Ventures | Co: Balderton Capital Press: "AI-powered fraud detection for digital banks" Web signal: Added /partners page listing 8 bank integrations Threat level: MEDIUM — potential vendor or competitor embedded-bank.com | PageRank: 2.8 (+1.5 QoQ) Series A: $18M | Valuation: ~$90M Lead: Accel | Co: Finch Capital Press: "Banking-as-a-Service for vertical SaaS platforms" Web signal: Domain age only 14 months but rapid PageRank climb Threat level: MEDIUM — BaaS could disintermediate your distribution INVESTOR ACTIVITY PATTERNS: a16z — 4 fintech deals this quarter (payments, lending, infrastructure) Tiger Global — 3 deals (all late-stage, $100M+ rounds) Ribbit Capital — 5 deals (neobanks, embedded finance focus) COMPETITIVE THREAT SUMMARY: 3 directly competitive fintechs raised $183M combined 12 adjacent fintechs raised $340M (potential future competitors) 4 complementary fintechs raised $95M (partnership opportunities)

7Embedded Finance Provider Finder

Searches the domain database for Banking-as-a-Service and embedded finance providers. Scrapes their API documentation, pricing pages, and compliance capabilities to build a curated vendor shortlist for BaaS partnerships.

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MCP Tool Definition
Domain DB Web Scraping GPT-4o Page Signals
embedded_finance_provider_finder provider_type: string — "baas" | "embedded_lending" | "embedded_payments" | "embedded_insurance" | "all" region: string — Target region for regulatory compatibility capabilities: array — Required: ["accounts","cards","lending","kyc","aml"] min_pagerank: number — Minimum PageRank for established providers (default: 3.0)
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AI Processing Pipeline
PROCESSING PIPELINE ════════════════════════════════════════════════════════════ Step 1: Query domain DB for BaaS/embedded finance companies by category + keywords Step 2: Scrape /products, /api, /docs, /compliance, /pricing pages Step 3: GPT-4o extracts: capabilities, API specs, licensing, compliance frameworks Step 4: Score providers on: API maturity, compliance coverage, documentation quality Step 5: Cross-reference with domain DB for company health signals Step 6: Filter by required capabilities and regional compliance Step 7: Generate ranked vendor shortlist with compatibility matrix
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Example Output
MCP RESPONSE — embedded_finance_provider_finder ════════════════════════════════════════════════════════════ SEARCH: BaaS providers | Region: EU | Required: accounts, cards, KYC RESULTS: 312 domains scanned | 28 BaaS providers identified TOP PROVIDERS (Ranked by capability match): solarisbank.com | PageRank: 5.1 | Match: 98% License: Full German banking license (BaFin regulated) Capabilities: Accounts | Cards (Visa) | KYC/AML | Lending API: REST + webhooks | Docs quality: Excellent (OpenAPI spec) Pricing: Revenue share + per-API-call | Setup: 4-8 weeks Clients listed: 7 named partners on /customers page railsr.com | PageRank: 4.3 | Match: 92% License: UK EMI + EU passported Capabilities: Accounts | Cards (Visa/MC) | KYC | Lending (limited) API: REST/JSON | Docs quality: Good (sandbox available) Pricing: Tiered volume-based | Setup: 2-6 weeks Clients listed: 12 named partners swan.io | PageRank: 3.8 | Match: 85% License: French ACPR licensed Capabilities: Accounts (IBAN) | Cards (MC) | KYC | No lending API: GraphQL | Docs quality: Excellent (interactive docs) Pricing: Per-account + transaction fees | Setup: 2-4 weeks Clients listed: 5 named partners CAPABILITY MATRIX: Accounts Cards KYC/AML Lending FX Insurance solarisbank.com YES YES YES YES YES NO railsr.com YES YES YES LTD YES NO swan.io YES YES YES NO LTD NO RECOMMENDATION: Best overall: solarisbank.com (98% match, full banking license) Best for speed: swan.io (fastest setup, excellent DX) Note: Lending capability limited across most BaaS providers

8BNPL Market Intelligence

Analyzes Buy Now Pay Later provider websites for market positioning — merchant coverage, payment terms, default rate disclosures, and regulatory compliance status using AI to map the competitive BNPL landscape.

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MCP Tool Definition
Web Scraping Domain DB GPT-4o Vision AI
bnpl_market_intelligence providers: array — BNPL provider domains (or auto-discover from DB) analysis_focus: array — ["terms","merchants","compliance","defaults","pricing"] region: string — Market region: "US","EU","UK","AU","global" include_regulatory: boolean — Include regulatory compliance analysis (default: true)
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AI Processing Pipeline
PROCESSING PIPELINE ════════════════════════════════════════════════════════════ Step 1: Query domain DB for BNPL/payment companies in Financial Services IAB Step 2: Scrape /how-it-works, /merchants, /terms, /legal, /investors pages Step 3: Screenshot checkout flows and merchant integration pages Step 4: GPT-4o extracts: payment terms, fees, merchant categories, limits Step 5: AI analyzes regulatory disclosures against regional requirements Step 6: Scrape investor/press pages for default rate and volume disclosures Step 7: Generate competitive positioning map with regulatory risk flags
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Example Output
MCP RESPONSE — bnpl_market_intelligence ════════════════════════════════════════════════════════════ REGION: Global | Providers analyzed: 18 | Focus: all BNPL PROVIDER COMPARISON: klarna.com | PageRank: 7.2 | Market: Leader Terms: Pay in 4 (0% APR) | Pay in 30 | Financing (up to 36mo) Merchants: 500,000+ across 45 markets Limits: $10-$10,000 per purchase Disclosed default rate: ~2.5% (from investor presentation) Regulatory: FCA authorized (UK), Swedish banking license afterpay.com | PageRank: 6.1 | Market: Major player Terms: Pay in 4 (0% interest) | Fixed schedule biweekly Merchants: 100,000+ across 10 markets Limits: $10-$2,000 per purchase Disclosed default rate: ~1.0% (from Block/Square filings) Regulatory: ASIC regulated (AU), expanding US compliance affirm.com | PageRank: 5.8 | Market: Major player Terms: Pay in 4 (0%) | Monthly (0-36% APR) | 3-60 months Merchants: 250,000+ across 3 markets Limits: $50-$30,000 per purchase Disclosed default rate: ~2.8% (from SEC filings) Regulatory: US state-licensed lender, TILA compliant MERCHANT FEE COMPARISON: klarna.com — 3.29% + $0.30 per transaction (merchant-paid) afterpay.com — 4-6% + $0.30 per transaction (merchant-paid) affirm.com — 5-6% per transaction (merchant-paid, varies by risk) REGULATORY COMPLIANCE SCORECARD: Credit Late Fee APR Consumer Advertising Check Caps Disclosure Protection Standards klarna.com YES YES YES Strong Compliant afterpay.com Soft YES N/A(0%) Moderate Compliant affirm.com YES YES YES Strong Compliant MARKET TRENDS: Regulation tightening: UK FCA, EU CCD2, CFPB rules expanding oversight Bank BNPL entry: JPMorgan, Citi, Barclays all launched BNPL products Super-app evolution: Klarna pivoting to shopping + banking + payments

9Digital Onboarding Analyzer

Screenshots and analyzes bank and fintech signup flows using AI vision to benchmark digital onboarding quality — steps required, KYC process complexity, time-to-account estimates, and conversion friction points.

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MCP Tool Definition
Web Scraping Vision AI GPT-4o Domain DB
digital_onboarding_analyzer targets: array — Bank/fintech domains to analyze onboarding flows product_type: string — "personal_account" | "business_account" | "credit_card" | "loan" device: string — "mobile" | "desktop" | "both" (default: "both") benchmark_against: string — Your domain for comparative analysis
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AI Processing Pipeline
PROCESSING PIPELINE ════════════════════════════════════════════════════════════ Step 1: Navigate to each target's /signup, /register, /open-account pages Step 2: Screenshot every step of the signup flow (mobile + desktop) Step 3: Vision AI maps each step: form fields, KYC requirements, progress indicators Step 4: Count total steps, required fields, document uploads, verification methods Step 5: Estimate time-to-account based on step complexity and KYC process Step 6: Identify friction points: unnecessary fields, confusing UX, dead ends Step 7: Generate onboarding benchmark scorecard with improvement recommendations
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Example Output
MCP RESPONSE — digital_onboarding_analyzer ════════════════════════════════════════════════════════════ PRODUCT: Personal Account | Device: Both | Targets: 5 ONBOARDING BENCHMARK SCORECARD: revolut.com | Score: 94/100 | Best in class Steps: 4 | Fields: 8 | KYC: Selfie + ID photo Time-to-account: ~3 minutes | Verification: Instant AI Mobile: Native app flow, excellent UX Desktop: Progressive web app, responsive Friction points: None significant monzo.com | Score: 89/100 Steps: 5 | Fields: 10 | KYC: Video selfie + ID Time-to-account: ~5 minutes | Verification: AI + human review Mobile: App-only signup, streamlined Desktop: Redirects to app download (no web signup) Friction points: Video selfie can fail in low light n26.com | Score: 82/100 Steps: 6 | Fields: 14 | KYC: ID scan + video call Time-to-account: ~8 minutes | Verification: Live agent video Mobile: Good native flow Desktop: Full web signup available Friction points: Video call wait times (2-5 min avg) YOUR BANK | Score: 47/100 Steps: 12 | Fields: 34 | KYC: Branch visit required Time-to-account: ~3-5 business days | Verification: Manual review Mobile: Not available — desktop only Desktop: Multi-page form, no progress indicator Friction points: 1. No mobile signup option (60% of users are mobile) 2. 34 form fields vs. industry avg of 10 — 240% more friction 3. Branch visit for KYC — eliminates digital-native customers 4. No progress bar — users abandon at step 7 (estimated) 5. SSN required upfront (competitors defer to later step) IMPROVEMENT ROADMAP: P0: Enable mobile signup (potential +35% conversion) P1: Reduce fields from 34 to 10 (defer non-essential to post-signup) P2: Implement AI-based KYC (eliminate branch visit requirement) P3: Add progress indicator and save-and-resume capability Target: Match industry benchmark of 5 steps, 5 minutes

10Payment Infrastructure Mapper

Maps the payment infrastructure ecosystem using the domain database to find processors, gateways, and rails providers. Scrapes for capabilities, pricing, geographic coverage, and integration requirements to build a comprehensive payments technology landscape.

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MCP Tool Definition
Domain DB Web Scraping GPT-4o Page Signals
payment_infrastructure_mapper layer: string — "processors" | "gateways" | "rails" | "acquirers" | "issuers" | "all" payment_methods: array — ["cards","bank_transfer","wallets","crypto","real_time"] regions: array — Geographic regions to map ["NA","EU","UK","APAC","LATAM"] include_pricing: boolean — Scrape and compare pricing (default: true)
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AI Processing Pipeline
PROCESSING PIPELINE ════════════════════════════════════════════════════════════ Step 1: Query domain DB for payment infrastructure companies by IAB category Step 2: Classify domains by layer: processor, gateway, rails, acquirer, issuer Step 3: Scrape /pricing, /products, /coverage, /developers, /partners pages Step 4: GPT-4o extracts: capabilities, supported methods, geographic coverage Step 5: Extract pricing models, fee structures, volume tiers Step 6: Map interconnections between layers (who processes through whom) Step 7: Generate infrastructure topology with pricing comparison matrix
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Example Output

Payment Infrastructure Map — Global Overview

INFRASTRUCTURE TOPOLOGY ──────────────────────────────────────── Domains scanned: 1,247 | Payment companies identified: 312 Layers mapped: Processors, Gateways, Rails, Acquirers PAYMENT GATEWAYS (86 providers): stripe.com | PageRank: 8.4 | Coverage: 47 countries Methods: Cards, ACH, SEPA, iDEAL, Alipay, Apple Pay, Google Pay Pricing: 2.9% + $0.30 (US cards) | 3.9% + $0.30 (international) API quality: Best-in-class documentation, 99.999% uptime SLA Integration: REST API, SDKs (15 languages), no-code options adyen.com | PageRank: 7.1 | Coverage: Global (200+ markets) Methods: Cards, Bank transfers, Wallets, Local methods (250+) Pricing: Processing fee (varies) + per-transaction (€0.11) API quality: Enterprise-grade, unified commerce platform Integration: REST API, plugins, POS terminals checkout.com | PageRank: 5.9 | Coverage: 150+ currencies Methods: Cards, Apple Pay, Google Pay, SEPA, iDEAL, Klarna Pricing: Custom (volume-based) | IC++ model available API quality: Modern REST API, flow-based checkout PAYMENT RAILS (18 networks): Real-time rails: FedNow (US) | Faster Payments (UK) | SEPA Instant (EU) | UPI (IN) Coverage expanding: 72 countries now have real-time payment rails Legacy rails: ACH (US, 2-3 days) | BACS (UK, 3 days) | SEPA CT (EU, 1 day) PRICING COMPARISON MATRIX: Domestic Intl Card Bank Xfer Setup Monthly stripe.com 2.9%+30¢ 3.9%+30¢ 0.8% $0 $0 adyen.com IC+++11¢ IC+++11¢ €0.11 Custom Custom checkout.com Custom Custom Custom Custom Custom braintree.com 2.59%+49¢ 3.49%+49¢ 0.75% $0 $0 GEOGRAPHIC COVERAGE HEAT MAP: North America: ████████████████████ Saturated (Stripe, Adyen, Braintree) Western EU: ██████████████████░░ Strong (Adyen, Stripe, Checkout, Mollie) APAC: ████████████░░░░░░░░ Growing (local + global providers) LATAM: ████████░░░░░░░░░░░░ Emerging (dLocal, EBANX + global) Africa: ██████░░░░░░░░░░░░░░ Underserved (Flutterwave, Paystack) STRATEGIC INSIGHTS: 1. Real-time rails adoption accelerating — 72 countries, up from 55 last year 2. Gateway consolidation trend — top 5 process 68% of global e-commerce 3. Pricing compression — average gateway fees down 12% YoY 4. Embedded payments growing fastest — 340% YoY in BaaS-powered payments
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MCP Real-Time API View Pricing

Each MCP service can use web scraping, AI text/vision endpoints, and the 100M domain database.