# Commercial Predictive Counterparty Risk

> Predict counterparty risk from historical payment patterns, market indicators, and performance data using predictive analytics

- Skill: `construct-ai-primary/commercial-predictive-counterparty-risk` (Agent Skill)
- Install (CLI): `npx skillmds@latest add construct-ai-primary/commercial-predictive-counterparty-risk`
- Raw SKILL.md: https://api.skillmd.com/api/skills/construct-ai-primary/commercial-predictive-counterparty-risk/raw
- Safety review: pending (external: skill-scanner PASS, skillspector PASS)
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: Data & Analytics
- Author: Construct-AI-primary (https://skillmd.com/u/construct-ai-primary)
- Updated: 2026-09-21
- Page: https://skillmd.com/skills/construct-ai-primary/commercial-predictive-counterparty-risk

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# Commercial Predictive Counterparty Risk

## Overview
Predict counterparty failure risk by analyzing historical payment patterns, market indicators, performance data, and financial trends using predictive analytics and machine learning models.
**Announce at start:** "I'm using the commercial-predictive-counterparty-risk skill to predict counterparty risk."

## When to Use This Skill
**Trigger Conditions:** Periodic risk forecast update, early warning triggered, new counterparty onboarded, market disruption detected
**Prerequisites:** Historical counterparty data available, market indicators accessible

## Step-by-Step Procedure
### Step 1: Gather Historical Data
1. Extract historical payment records (on-time, late, disputed, unpaid)
2. Gather market indicator data (credit spreads, CDS prices, stock performance)
3. Compile performance history (quality, delays, disputes)
4. Collect financial filing data

### Step 2: Analyze Payment Patterns
1. Calculate payment timeliness trends (improving/worsening)
2. Identify payment pattern anomalies (sudden delays, partial payments)
3. Calculate payment velocity trend

### Step 3: Assess Market Indicators
1. Analyze credit rating changes (upgrades/downgrades)
2. Monitor CDS spread widening
3. Check for negative news events
4. Analyze sector-wide stress indicators

### Step 4: Apply Predictive Model
1. Feed inputs into predictive risk model
2. Calculate failure probability score
3. Generate risk forecast horizon
4. Calculate confidence level

### Step 5: Generate Risk Forecast Report
1. Risk score and trend
2. Early warning alerts
3. Confidence intervals
4. Recommend early intervention

## Success Criteria
- [ ] Historical data gathered from all sources
- [ ] Payment patterns analyzed for trends
- [ ] Market indicators assessed
- [ ] Predictive model applied with confidence level
- [ ] Risk forecast report generated with recommendations

## Common Pitfalls
1. **Small Dataset** — Insufficient historical data reduces prediction accuracy. Flag low confidence when data is sparse.
2. **Black Swan Events** — Unpredictable events (pandemic, war) may not be captured in historical patterns. Note model limitations.

## Cross-References
### Related Skills
- `commercial-counterparty-risk-assessment` — Fundamental counterparty risk assessment
- `commercial-market-intelligence` — Market indicator input
### Related Agents
- `COM-004` Commercial Market Intelligence Agent — Primary agent
- `COM-011` Commercial Risk Register Agent — Supporting agent