creditctl

Credit report analysis and scoring for lending operations. Part of the LendCtl Suite.

Features

• Score Analysis — Tier classification, mortgage eligibility, rate impact
• Report Analysis — Full tradeline analysis, risk flags, recommendations
• Rapid Rescore — Simulate score improvement from paydowns
• Tradeline Summary — Account breakdown by type and status
• Utilization Optimization — Find optimal paydown strategies

Installation

npm install -g creditctl

Or use directly with npx:

npx creditctl --help

Quick Start

Analyze a Score

creditctl score 720

Output:

═══════════════════════════════════════════════════════════════
CREDIT SCORE ANALYSIS
═══════════════════════════════════════════════════════════════

Score:              720
Tier:               NEARPRIME
                    Good credit - standard rates, most programs available

MORTGAGE ELIGIBILITY
───────────────────────────────────────────────────────────────
  ✓ Conventional    Good rates
  ✓ FHA             3.5% down payment
  ✓ VA              Most lenders require 580-620 minimum
  ✓ USDA            Automated approval requires 640+
  ✓ Jumbo           Most lenders

RATE IMPACT
───────────────────────────────────────────────────────────────
  Your bracket:     720-739
  Est. rate impact: +0.25% vs best rate

💡 Improving 20 points would reach the next tier.
═══════════════════════════════════════════════════════════════

Analyze a Full Credit Report

creditctl analyze -f credit_report.json

Simulate Rapid Rescore

# How much would paying $5,000 toward balances improve my score?
creditctl rescore -f credit_report.json --paydown 5000

# What if I target 10% utilization?
creditctl rescore -f credit_report.json --target-util 10

# Include paying off collections
creditctl rescore -f credit_report.json --paydown 5000 --pay-collections

List Tradelines

# All tradelines
creditctl tradelines -f credit_report.json

# Summary only
creditctl tradelines -f credit_report.json --summary

# Only derogatory accounts
creditctl tradelines -f credit_report.json --derogatory

# Filter by type
creditctl tradelines -f credit_report.json --type revolving

Credit Report Format

creditctl expects a JSON credit report in this format:

{
  "consumer": {
    "name": "John Smith",
    "addresses": ["123 Main St, Anytown, CA 90210"],
    "dateOfBirth": "1985-03-15"
  },
  "bureau": "experian",
  "reportDate": "2026-02-26",
  "scores": [
    {
      "score": 720,
      "model": "fico8",
      "bureau": "experian",
      "date": "2026-02-26",
      "factors": [
        { "code": "14", "description": "Length of credit history", "impact": "positive" }
      ]
    }
  ],
  "tradelines": [
    {
      "creditor": "Chase Sapphire",
      "accountNumber": "****1234",
      "accountType": "revolving",
      "status": "current",
      "dateOpened": "2020-05-15",
      "creditLimit": 10000,
      "balance": 2500,
      "monthlyPayment": 100
    }
  ],
  "publicRecords": [],
  "inquiries": [
    {
      "inquirer": "Mortgage Lender",
      "date": "2026-02-01",
      "type": "hard"
    }
  ]
}

Programmatic Usage

import { analyzeCreditReport, simulateRescore, getScoreTier } from 'creditctl';

// Analyze a report
const analysis = analyzeCreditReport(creditReport);
console.log(`Score: ${analysis.representativeScore}`);
console.log(`Tier: ${analysis.scoreTier}`);
console.log(`Utilization: ${analysis.utilizationRatio}%`);

// Simulate rescore
const simulation = simulateRescore(creditReport, {
  payDownAmount: 5000,
  payOffCollections: true,
});
console.log(`Projected score: ${simulation.projectedScore} (+${simulation.scoreChange})`);

// Quick score tier check
const tier = getScoreTier(720);
console.log(`Tier: ${tier}`); // 'nearprime'

Score Tiers

| Tier | Score Range | Description | |------|-------------|-------------| | Superprime | 800+ | Exceptional credit, best rates | | Prime | 740-799 | Very good credit, excellent rates | | Near-prime | 670-739 | Good credit, standard rates | | Subprime | 580-669 | Fair credit, limited options | | Deep Subprime | <580 | Poor credit, very limited options |

Mortgage Eligibility by Score

| Program | Minimum Score | Notes | |---------|---------------|-------| | Conventional | 620 | 740+ for best rates | | FHA | 500 | 580+ for 3.5% down | | VA | 580 | Lender-specific | | USDA | 640 | Automated approval | | Jumbo | 700+ | Lender-specific |

Risk Flags Detected

creditctl automatically identifies:

• Low credit scores
• High utilization
• Derogatory marks
• Collections
• Public records (bankruptcy, foreclosure)
• Thin credit files
• Excessive inquiries
• Short credit history
• Fraud alerts
• Credit freezes

Part of LendCtl Suite

creditctl is designed to work with other LendCtl tools:

# Full lending workflow
creditctl analyze -f credit.json --format json | \
  finctl analyze --stdin | \
  mortctl eligible --stdin

License

Apache-2.0 © Satyan Avatara