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Score Mechanics

The Five Credit Score Factors Explained

What the models actually weigh: the five published factor categories, their approximate weights, and the behavior each one rewards.

Funditia Editorial Team
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8 min read
Weighted factor chart of credit score components
Key Points At A Glance
Payment History: ~35%
Amounts Owed: ~30%
History Length: ~15%
New Credit + Mix: ~20%

The most widely referenced scoring model publishes its input categories and approximate weights — rare transparency in a proprietary system. Five factors account for the entire score: payment history (~35%), amounts owed (~30%), length of credit history (~15%), new credit (~10%), and credit mix (~10%).

The weights describe the population overall, not your file — a thin file leans harder on utilization, while a decades-old file tolerates inquiries more easily. Still, the hierarchy reliably answers the practical question: which behaviors move the score most, fastest?

Mechanics

How Each Factor Is Measured

Payment history tracks whether obligations were paid as agreed — a single 30-day delinquency is the factor's signature event, and severity plus recency determine the damage. Amounts owed measures utilization on revolving accounts plus remaining balances on loans. Length of history averages account ages and rewards keeping old accounts open.

New credit counts recent hard inquiries and new accounts — a burst signals risk. Credit mix rewards managing both revolving and installment accounts, though its small weight never justifies opening products solely for diversity. Every model version reweights slightly, but the ordering — payments first, debt load second, time third — has remained stable for decades.

Balanced Assessment

Pros & Cons

Advantages
  • Published hierarchy — Unlike most proprietary formulas, the factor weights are public
  • Actionable mapping — Each factor maps to concrete controllable behavior
  • Stable ordering — The rank of factor importance has persisted across model generations
  • Diagnostic utility — Factor reason codes tell you which category is hurting most
Disadvantages
  • Approximate weights — Published percentages are population averages, not your file's math
  • Model variance — Newer versions weigh categories and data differently
  • Blind spots — Income, savings, and rent-by-default are invisible to the factors
  • Slow categories — History length cannot be accelerated — only preserved
Action Checklist

Practical Tips

  • Read the 'reason codes' on any score disclosure — they name your file's weakest factors.
  • Attack the top two factors first: flawless payments and low utilization dominate everything.
  • Never close your oldest card for mix reasons — age feeds the third factor directly.
  • Cluster loan rate-shopping inside ~14–45 days so inquiries collapse to one for scoring.
  • Ignore mix-chasing: a 10% factor never justifies a product you would not otherwise want.
Consumer Protection

CFPB & FTC Regulatory Guidance

The CFPB's consumer education confirms the factor hierarchy and directs consumers to the reason codes lenders must provide when scores drive adverse decisions — a mandatory disclosure under ECOA and FCRA. The FTC's guidance aligns: payment reliability and low balances are the repeatedly-cited primary drivers.

Funditia summarizes publicly documented factor weights educationally; exact formulas remain proprietary, and weight application varies by model version and file composition.

Educational references: Consumer Financial Protection Bureau (consumerfinance.gov) and Federal Trade Commission (consumer.ftc.gov). Funditia is an independent educational publication and is not a credit card issuer, lender, or credit repair organization.

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