850 is the top of the FICO range (300–850) but only 1.76% of Americans had a perfect 850 in 2025; scores of 800+ are already classified as 'exceptional' and generally secure the same lender benefits (best mortgage/loan rates, high limits, premium cards). The piece argues chasing a perfect 850 is impractical for most and recommends targeting an 800 instead, offering three actionable tips: always pay on time, keep credit utilization below ~30%, and keep older accounts open to preserve average age of credit.
The consumer focus on marginal credit-score improvements creates a subtle revenue shift across the lending ecosystem: marketing and underwriting budgets increasingly target the thin slice of ultra-prime consumers, raising marginal customer acquisition costs while compressing yield on incremental card spend. That makes data/analytics that can more finely segment borrower behavior — not raw score bands — the most valuable asset; firms that sell predictive overlays or rent access to bureau-derived signals will capture outsized pricing power over commoditized score providers. On the liability and securitization side, a persistent concentration of extremely low-risk borrowers changes pool composition and spreads: originators will hoard ultra-prime flows for brand cards and cheap funding, forcing mid-prime and near-prime paper into the wholesale ABS market where credit enhancement demands rise. Over a 6–24 month horizon this can widen spreads on mid-tier consumer ABS by several dozen basis points if originators tighten retail acquisition to protect ROE. Consumer behavior consequences are underappreciated. As more borrowers optimize utilization and keep dormant accounts open for score maintenance, revolving balances and interchange growth can decelerate even in a benign macro, pressuring card issuers’ top-line unless they innovate fee structures or cross-sell non-transaction products. Countervailing catalysts that would reverse these trends include a spike in employment-driven lending (lifting utilization) or regulatory changes that reduce the predictive value of current score components, both of which would reprice risk models within quarters.
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