Credit Risk Team

Credit Risk Intelligence
MCP Services

Ten MCP services for the Credit Risk team — each callable by any AI assistant to deliver real-time borrower health analysis, hiring trend signals, revenue model detection, leadership stability monitoring, and portfolio risk aggregation using web scraping, AI analysis, and the 100M+ domain database.

1Borrower Website Health Scanner

Scrapes a borrower's corporate website and uses AI to assess overall business health from digital signals — career page activity, leadership stability, product offerings, and press frequency — returning a composite health score.

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MCP Tool Definition
Web Scraping GPT-4o Domain DB
borrower_website_health_scan domain: string — Target borrower domain (e.g. "acmecorp.com") depth: string — "quick" (homepage only) or "deep" (all key pages) include_history: boolean — Include historical change signals
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AI Processing Pipeline
PROCESSING PIPELINE ════════════════════════════════════════════════════════════ Step 1: Scrape target domain /careers, /about, /leadership, /press, /products, /investors Step 2: Query domain DB for IAB category, PageRank, domain age, country Step 3: Send scraped content to GPT-4o with credit risk assessment prompt Step 4: AI evaluates: hiring trends, leadership stability, product vitality, press activity Step 5: Calculate composite health score (0-100) from weighted signals Step 6: Compare against sector peers from domain DB (same IAB category) Step 7: Generate structured JSON with scores, flags, and recommendations
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Example Output
MCP RESPONSE — borrower_website_health_scan ════════════════════════════════════════════════════════════ acmecorp.com | IAB: Manufacturing | PageRank: 4.2/10 | Age: 6,847 days HEALTH SCORE: 47/100 (Below Average) CAREER SIGNALS: Job postings: 18 active (was 142 three months ago) — 87% decline Engineering roles: 0 open (was 34) — complete hiring freeze Senior positions: 2 director-level roles posted (backfilling?) LEADERSHIP SIGNALS: Executive team: CFO removed from /leadership page on 2026-01-15 Team size: 7 listed (was 9) — 2 departures in 90 days PRODUCT SIGNALS: Product lines: 3 product pages removed (consolidation?) Pricing page: Still active, last updated 2026-02-10 PRESS SIGNALS: Press releases: 0 in last 4 months (avg was 2/month) Blog activity: 1 post in 90 days (was weekly) PEER COMPARISON (Manufacturing sector, n=2,847): Percentile rank: 12th percentile Sector avg health: 68/100 | This borrower: 47/100 RECOMMENDATION: ELEVATED RISK — Recommend immediate credit review

2Corporate Hiring Trend Analyzer

Scrapes careers pages across a portfolio of borrower domains and uses AI to classify hiring patterns — growth vs. contraction, department focus shifts, geographic expansion or retreat — as early credit signals.

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MCP Tool Definition
Web Scraping GPT-4o Domain DB
corporate_hiring_trend_analyzer domains: array — List of borrower domains to analyze iab_category: string — Or filter by IAB category from domain DB lookback_days: integer — Historical comparison window (default: 90)
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AI Processing Pipeline
PROCESSING PIPELINE ════════════════════════════════════════════════════════════ Step 1: Scrape /careers page for each domain in portfolio Step 2: Extract job titles, departments, locations, seniority levels via AI Step 3: Compare current postings vs. historical baseline Step 4: Classify trend: GROWING / STABLE / CONTRACTING / RESTRUCTURING Step 5: Detect department shifts (e.g. engineering → sales = pivot signal) Step 6: Flag anomalies: sudden hiring freezes, mass backfills, C-suite searches
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Example Output
MCP RESPONSE — corporate_hiring_trend_analyzer ════════════════════════════════════════════════════════════ PORTFOLIO: 847 borrower domains analyzed | Lookback: 90 days CONTRACTION ALERTS (37 borrowers): steelworks-intl.com — CONTRACTING Current: 8 postings | 90-day-ago: 67 | Change: -88% Pattern: All engineering roles removed, only HR & legal remaining Signal: Potential restructuring or wind-down retailchain-group.com — RESTRUCTURING Current: 24 postings | 90-day-ago: 45 | Change: -47% Pattern: Store manager roles down 60%, but e-commerce hiring +200% Signal: Channel pivot from physical to digital GROWTH SIGNALS (124 borrowers): cloudfintech.io — GROWING Current: 89 postings | 90-day-ago: 41 | Change: +117% Pattern: Engineering +150%, Sales +80%, new compliance team forming Signal: Rapid scaling, likely post-funding expansion SECTOR SUMMARY: Manufacturing: Net contraction -23% | Technology: Net growth +34% Retail: Mixed (-12% physical, +45% digital)

3Revenue Model Detector

Scrapes pricing, products, and subscription pages to classify a company's revenue model using AI — SaaS recurring, usage-based, freemium, enterprise, marketplace, or hybrid — providing credit analysts with revenue predictability insights.

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MCP Tool Definition
Web Scraping Gemini Pro Vision AI
revenue_model_detector domain: string — Target company domain screenshot: boolean — Include visual pricing page analysis (default: true)
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AI Processing Pipeline
PROCESSING PIPELINE ════════════════════════════════════════════════════════════ Step 1: Scrape /pricing, /plans, /products, /solutions pages Step 2: Take screenshots of pricing pages for visual analysis Step 3: Send text + screenshots to Gemini Pro Vision Step 4: Classify: SaaS / Usage-Based / Freemium / Enterprise / Marketplace / Hybrid Step 5: Extract price points, tier structures, contract terms Step 6: Assess revenue predictability score based on model type
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Example Output
MCP RESPONSE — revenue_model_detector ════════════════════════════════════════════════════════════ cloudfintech.io REVENUE MODEL: SaaS Recurring + Usage-Based Hybrid PRICING TIERS DETECTED: Starter: $49/mo — 1,000 API calls included Professional: $199/mo — 10,000 API calls, priority support Enterprise: Custom pricing — "Contact Sales" (annual contracts) MODEL CHARACTERISTICS: Base: Monthly recurring subscriptions (high predictability) Variable: Usage-based overage charges (moderate variability) Enterprise: Annual contracts detected (revenue visibility) REVENUE PREDICTABILITY SCORE: 78/100 Recurring component: ~70% of estimated revenue Usage-based component: ~25% of estimated revenue One-time/services: ~5% CREDIT IMPLICATIONS: Strong recurring base provides debt service coverage predictability Usage-based component creates moderate revenue variability Enterprise tier suggests large contract backlog

4Leadership Stability Monitor

Tracks executive team pages across borrower websites to detect leadership changes — departures, new appointments, interim roles, and team restructuring — as early indicators of corporate stability or distress.

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MCP Tool Definition
Web Scraping GPT-4o Vision AI
leadership_stability_monitor domain: string — Target company domain baseline_date: string — Compare against this date's snapshot (ISO format) alert_on: array — ["cfo_change","ceo_change","board_change","mass_departure"]
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AI Processing Pipeline
PROCESSING PIPELINE ════════════════════════════════════════════════════════════ Step 1: Scrape /leadership, /about, /team, /board pages Step 2: Screenshot executive team section for visual analysis Step 3: Extract names, titles, photos via GPT-4o Vision Step 4: Compare against historical baseline snapshot Step 5: Classify changes: DEPARTURE / NEW_HIRE / TITLE_CHANGE / INTERIM Step 6: Calculate leadership stability score and flag alerts
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Example Output
MCP RESPONSE — leadership_stability_monitor ════════════════════════════════════════════════════════════ megacorpenergy.com | Baseline: 2025-10-01 STABILITY SCORE: 34/100 (Critical) CHANGES DETECTED: DEPARTURE: Sarah Chen, CFO — Removed from /leadership (2026-01-15) DEPARTURE: Michael Park, VP Finance — Removed (2026-02-03) TITLE_CHANGE: James Wei, Controller → "Interim CFO" (2026-01-20) NEW_HIRE: Lisa Rodriguez, VP Legal — Added (2025-12-10) ALERT TRIGGERS: CFO_CHANGE: CFO departed, interim appointment detected MASS_DEPARTURE: 2 finance executives in 19 days TEAM COMPOSITION: Current: 7 executives listed (was 9) Finance function: Both top finance leaders departed Interim roles: 1 interim appointment (CFO) HISTORICAL PATTERN: Dual finance departure + interim CFO matches pre-distress pattern 82% correlation with rating downgrade within 6 months

5Industry Peer Benchmarker

Queries the 100M domain database to find industry peers by IAB category, then scrapes and compares key web metrics across the peer set — providing relative positioning for credit assessment and sector analysis.

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MCP Tool Definition
Domain DB Web Scraping GPT-4o
industry_peer_benchmarker domain: string — Target company domain to benchmark peer_count: integer — Number of peers to compare (default: 20) metrics: array — ["hiring","pricing","tech_stack","content_quality","web_authority"]
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AI Processing Pipeline
PROCESSING PIPELINE ════════════════════════════════════════════════════════════ Step 1: Look up target domain's IAB category in 100M DB Step 2: Find top N peers by PageRank in same IAB category Step 3: Scrape key pages (/pricing, /careers, /products) for target + peers Step 4: Use AI to extract comparable metrics from each company Step 5: Calculate percentile rankings across all metrics Step 6: Generate peer comparison matrix with positioning analysis
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Example Output
MCP RESPONSE — industry_peer_benchmarker ════════════════════════════════════════════════════════════ acmecorp.com | IAB: Manufacturing | Peers found: 2,847 PEER BENCHMARK MATRIX (Top 20 peers by PageRank): PageRank Hiring Pricing Content Overall acmecorp.com 4.2 12th 34th 28th 23rd industryking.com 7.8 95th 89th 92nd 94th smartmanuf.com 6.4 87th 91st 78th 85th legacymfg.net 3.9 22nd 45th 31st 33rd RELATIVE POSITIONING: Web Authority: Below median (4.2 vs sector median 5.1) Hiring Activity: Bottom quartile (12th percentile) Pricing Sophistication: Below median (34th percentile) Content Quality: Below median (28th percentile) CREDIT ASSESSMENT: Target underperforms sector peers across all measured dimensions Consistent with company in competitive decline phase

6Financial News Sentiment Scorer

Scrapes press releases, blog posts, and news pages from borrower websites and applies AI sentiment analysis to score corporate communications tone — detecting shifts from positive to defensive or crisis language patterns.

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MCP Tool Definition
Web Scraping GPT-4o
financial_news_sentiment_scorer domain: string — Target company domain content_types: array — ["press","blog","news"] pages to analyze max_articles: integer — Maximum articles to process (default: 20)
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AI Processing Pipeline
PROCESSING PIPELINE ════════════════════════════════════════════════════════════ Step 1: Scrape /press, /blog, /news pages and extract article list Step 2: Parse individual articles (title, date, body text) Step 3: Run GPT-4o sentiment analysis on each article Step 4: Classify tone: POSITIVE / NEUTRAL / DEFENSIVE / CRISIS Step 5: Detect language shifts (e.g. "growth" → "challenges", "restructuring") Step 6: Calculate trend: improving, stable, or deteriorating sentiment
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Example Output
MCP RESPONSE — financial_news_sentiment_scorer ════════════════════════════════════════════════════════════ megacorpenergy.com | Articles analyzed: 14 OVERALL SENTIMENT: DETERIORATING Trend: Positive → Defensive over 6 months Current score: -0.34 (scale: -1.0 to +1.0) RECENT ARTICLES: 2026-02-15: "Navigating Market Headwinds" — DEFENSIVE (-0.21) 2026-01-28: "Cost Optimization Program Update" — CRISIS (-0.45) 2025-12-10: "Strategic Review Announcement" — DEFENSIVE (-0.38) 2025-11-05: "Q3 Results Discussion" — NEUTRAL (-0.08) 2025-09-20: "Industry Leadership Award" — POSITIVE (+0.67) LANGUAGE SHIFT DETECTED: "Growth" mentions: 12 → 2 over 6 months "Restructuring" appeared 8 times in last 3 articles (0 previously) "Challenges" / "headwinds" frequency increased 400% CREDIT SIGNAL: Defensive language pattern consistent with pre-downgrade communications

7Supply Chain Web Mapper

Discovers supplier and partner relationships by scraping /partners, /integrations, and /case-studies pages, then maps the supply chain web to identify concentration risks and dependency vulnerabilities across the borrower's ecosystem.

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MCP Tool Definition
Web Scraping GPT-4o Domain DB
supply_chain_web_mapper domain: string — Target company domain depth: integer — Mapping depth: 1 = direct, 2 = sub-suppliers (default: 1) risk_threshold: string — "low","medium","high" — filter by risk level
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AI Processing Pipeline
PROCESSING PIPELINE ════════════════════════════════════════════════════════════ Step 1: Scrape /partners, /integrations, /case-studies, /suppliers Step 2: Extract company names and domains via AI entity extraction Step 3: Look up each partner/supplier in 100M domain DB Step 4: Assess each partner's health (PageRank, domain age, category) Step 5: Identify concentration risks (geography, sector, single-source) Step 6: Generate supply chain map with risk scoring
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Example Output
MCP RESPONSE — supply_chain_web_mapper ════════════════════════════════════════════════════════════ acmecorp.com | Partners/Suppliers found: 23 SUPPLY CHAIN MAP: TIER 1 — Critical Suppliers (7): rawmaterials-asia.com | Country: CN | PageRank: 3.1 | MEDIUM RISK chipmaker-global.com | Country: TW | PageRank: 6.8 | LOW RISK logisticspartner.eu | Country: DE | PageRank: 5.2 | LOW RISK TIER 2 — Technology Partners (9): erpsolutions.com | Country: US | PageRank: 7.1 | LOW RISK cloudhostingpro.com | Country: US | PageRank: 5.9 | LOW RISK CONCENTRATION RISKS: Geographic: 4 of 7 critical suppliers in Asia-Pacific Single-source: Raw materials sourced from 1 supplier Technology: Diversified across 3 providers RISK SCORE: 62/100 (Moderate concentration risk)

8ESG Disclosure Analyzer

Scrapes sustainability, ESG, and corporate responsibility pages and uses AI to score the quality and completeness of environmental, social, and governance disclosures — identifying greenwashing risks and compliance gaps.

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MCP Tool Definition
Web Scraping GPT-4o Vision AI
esg_disclosure_analyzer domain: string — Target company domain framework: string — "GRI","SASB","TCFD","EU_TAXONOMY" (default: all) include_visual: boolean — Screenshot ESG pages for visual quality analysis
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AI Processing Pipeline
PROCESSING PIPELINE ════════════════════════════════════════════════════════════ Step 1: Scrape /sustainability, /esg, /csr, /impact, /responsibility pages Step 2: Extract ESG claims, metrics, targets, and certifications Step 3: Screenshot ESG reports/pages for visual quality assessment Step 4: AI evaluates against selected framework requirements Step 5: Detect greenwashing patterns (vague claims, missing metrics) Step 6: Score E, S, G components separately and overall
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Example Output
MCP RESPONSE — esg_disclosure_analyzer ════════════════════════════════════════════════════════════ megacorpenergy.com | Framework: TCFD ESG DISCLOSURE SCORE: 41/100 ENVIRONMENTAL (E): 28/100 Carbon targets: No quantitative targets found Emissions data: Scope 1 only, no Scope 2/3 Certifications: None detected GREENWASHING FLAG: "committed to sustainability" with no metrics SOCIAL (S): 52/100 Diversity data: General statement, no specific metrics Safety records: OSHA recordable rate disclosed Community: $2M community investment program detailed GOVERNANCE (G): 44/100 Board diversity: Listed but no demographic breakdown Ethics policy: Code of conduct publicly available Executive comp: No ESG-linked compensation disclosed RECENT CHANGE: ESG report was removed from site navigation (2026-01-14) Previously featured sustainability page now returns 404

9Domain Authority Credit Scorer

Uses PageRank, domain age, IAB classification, and web presence metrics from the 100M domain database to generate a web-authority-based credit signal — correlating digital presence strength with business stability and market positioning.

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MCP Tool Definition
Domain DB GPT-4o
domain_authority_credit_scorer domains: array — List of borrower domains to score sector_adjust: boolean — Adjust scores relative to sector peers (default: true) include_components: boolean — Break down score by component
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AI Processing Pipeline
PROCESSING PIPELINE ════════════════════════════════════════════════════════════ Step 1: Query 100M domain DB for PageRank, domain age, IAB category Step 2: Pull web filtering category and country data Step 3: Calculate raw authority score from weighted metrics Step 4: Find sector peers (same IAB category) for normalization Step 5: Sector-adjust scores to generate percentile ranking Step 6: Map authority score to credit risk tier (AAA → D)
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Example Output
MCP RESPONSE — domain_authority_credit_scorer ════════════════════════════════════════════════════════════ BATCH: 5 borrower domains scored acmecorp.com PageRank: 4.2/10 | Age: 6,847 days | IAB: Manufacturing Raw Score: 52/100 | Sector-Adjusted: 38th percentile Credit Tier: BB | Trend: Declining (was 67/100 last quarter) cloudfintech.io PageRank: 5.1/10 | Age: 847 days | IAB: Financial Services Raw Score: 71/100 | Sector-Adjusted: 72nd percentile Credit Tier: A- | Trend: Improving (+15 points QoQ) globalretailgroup.com PageRank: 4.1/10 | Age: 4,218 days | IAB: Retail Raw Score: 48/100 | Sector-Adjusted: 41st percentile Credit Tier: BB- | Trend: Stable (±2 points) COMPONENT BREAKDOWN (acmecorp.com): PageRank weight (40%): 42/100 Domain Age weight (20%): 85/100 Web Presence weight (25%): 32/100 Category Risk weight (15%): 55/100

10Portfolio Risk Aggregator

Aggregates signals from multiple MCP services across an entire loan portfolio — combining health scores, hiring trends, leadership changes, sentiment shifts, and authority scores into a unified portfolio risk dashboard with sector-level heat maps.

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MCP Tool Definition
All MCP Services Domain DB GPT-4o
portfolio_risk_aggregator portfolio: array — [{domain, exposure_amount, current_rating}] services: array — MCP services to include in aggregation group_by: string — "sector","geography","rating" for heat map
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AI Processing Pipeline
PROCESSING PIPELINE ════════════════════════════════════════════════════════════ Step 1: Run selected MCP services across all portfolio domains Step 2: Collect: health scores, hiring trends, leadership stability, sentiment Step 3: Aggregate by sector using IAB categories from domain DB Step 4: Weight signals by exposure amount for risk-adjusted view Step 5: Generate sector heat map and watchlist recommendations Step 6: AI synthesizes portfolio-level narrative and action items
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Example Output

Portfolio Risk Aggregation — Q1 2026

PORTFOLIO SUMMARY ──────────────────────────────────────── Borrowers analyzed: 2,847 | Total exposure: $14.2B MCP services applied: 9 | Signals generated: 25,623 SECTOR HEAT MAP (by exposure-weighted risk): Manufacturing: ████████████████░░░░ HIGH RISK ($3.2B, avg score: 41) Retail: ████████████░░░░░░░░ MODERATE ($2.1B, avg score: 54) Energy: ██████████████░░░░░░ ELEVATED ($1.8B, avg score: 48) Technology: ██████░░░░░░░░░░░░░░ LOW RISK ($2.8B, avg score: 74) Healthcare: ████░░░░░░░░░░░░░░░░ LOW RISK ($1.9B, avg score: 79) WATCHLIST RECOMMENDATIONS: 1. acmecorp.com — Move to Watch List (health: 47, leadership: 34, sentiment: -0.34) 2. megacorpenergy.com — Immediate review (leadership: 34, ESG: 41, 10-K delay) 3. retailchain-group.com — Enhanced monitoring (hiring restructuring detected) ACTION ITEMS: 1. Schedule credit committee review for 3 elevated-risk borrowers ($1.2B exposure) 2. Stress test retail sector portfolio ($2.1B) for channel pivot scenario 3. Consider exposure increase for tech sector (strong web signals, avg score 74)
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