I searched GitHub for Python test files that reference credit_score. Across about 100 repos, every single one hardcodes an integer: user["credit_score"] = 720 Not one of them uses a generator, a factory, or even random.randint. They just pick a number and move on and random.randint isn't much better. A FICO 8 score of 720 falls in the "good" tier. But if your code handles Equifax Beacon 5.0 scores, the valid range is 334 - 818 vs. 300 - 850. A score of 720 is still "good," but the boundary math is different. If your lending flow branches on tier thresholds and you're testing with a hardcoded 720, you're testing one path of one model. You'll never catch the edge case where a 320 is valid for FICO 8 but impossible for Equifax Beacon 5.0. random.randint(300, 850) will happily give you 845. That's a valid FICO 8 score, but it's above the max value for Equifax Beacon 5.0. If your code doesn't validate against model-specific ranges, you won't know until production. Here's what surprised me most: many of the repos I looked at already use Faker for names, addresses, and emails. But when it comes to credit scores, they drop back to a hardcoded integer. The gap exists because there hasn't been a Faker provider for credit scores -- so even developers who know better default to 720 and move on. I wanted fake credit scores that: Come from real scoring models with real ranges Can be constrained to a tier (poor, fair, good, very_good, exceptional) Include the bureau name and model name, not just a number Leverage Faker, a testing scaffolding I'm a big fan of Install it: pip install faker-credit-score Add it to your Faker setup: from faker import Faker from faker_credit_score import CreditScore fake = Faker() fake.add_provider(CreditScore) Generate scores: fake.credit_score() # 791 fake.credit_score("fico5") # 687 (Equifax Beacon 5.0, range 334-818) Test specific tiers: fake.credit_score(tier="poor") # 542 fake.credit_score(tier="exceptional") # 831 Get the full picture: result = fake.credit_score_full("fico5") # CreditScoreResult(name='Equifax Beacon 5.0', provider='Equifax', score=687) name, provider, score = result # destructure it Classify a score you already have: fake.credit_score_tier(score=720) # 'good' You can also combine tier and model. If you ask for an "exceptional" score on a model that tops out at 818, the range gets clamped: fake.credit_score(score_type="fico5", tier="exceptional") # 812 (clamped to 800-818) Ten scoring models, each with real ranges: Model Range FICO Score 8, 9, 10, 10 T 300-850 VantageScore 3.0, 4.0 300-850 UltraFICO 300-850 Equifax Beacon 5.0 334-818 Experian/Fair Isaac Risk Model V2SM 320-844 TransUnion FICO Risk Score, Classic 04 309-839 If your code branches on credit scores, your tests should use scores that behave like real ones. faker-credit-score on PyPI | GitHub