BFH vs LPRO

Bread Financial Holdings, Inc. vs Open Lending Corporation — Valuation Comparison 2026

BFH

Personal Credit Institutions
Bread Financial Holdings, Inc.
Quality
8.0
out of 10
Value Trap
12
SAFE
Price
$89.07
Last close
Models
12/13
Active
VS

LPRO

Personal Credit Institutions
Open Lending Corporation
Quality
7.2
out of 10
Value Trap
32
LOW
Price
$2.28
Last close
Models
13/13
Active

Model-by-Model Comparison

ModelType BFH Fair ValueBFH Upside LPRO Fair ValueLPRO Upside
Bayesian DCF Intrinsic $244.23 +174.2% $5.54 +142.9%
Earnings Power Value Intrinsic $139.91 +57.1% $7.75 +337.9%
EROIC Spread Intrinsic $•••.•• ••.•% $•••.•• ••.•%
First Chicago Scenario $•••.•• ••.•% $•••.•• ••.•%
Markov DDM Intrinsic $•••.•• ••.•% $•••.•• ••.•%
ML-RIV Intrinsic $•••.•• ••.•% $•••.•• ••.•%
Dynamic NAV Asset-Based $•••.•• ••.•% $•••.•• ••.•%
PWERM Option-Based $•••.•• ••.•% $•••.•• ••.•%
Regime Cross-Sectional Relative $•••.•• ••.•% $•••.•• ••.•%
Sentiment SOTP Hybrid $•••.•• ••.•% $•••.•• ••.•%
CUCE Ensemble Ensemble $•••.•• ••.•% $•••.•• ••.•%
FTNN Topology Relative $•••.•• ••.•% $•••.•• ••.•%
RCMH-DCF Intrinsic $•••.•• ••.•% $•••.•• ••.•%
🔒

Unlock Full 13-Model Comparison

Access all valuation models for BFH vs LPRO — including EROIC Spread, First Chicago, Markov DDM, PWERM, and 7 more.

Access Full Analysis — From $27/mo →

BFH vs LPRO — Which Stock Is More Undervalued?

BFH scores higher with a 8.0/10 quality rating vs LPRO's 7.2/10. Both stocks are analyzed daily using SEC EDGAR filings across 13 independent models.

Comparing Bread Financial Holdings, Inc. (BFH) and Open Lending Corporation (LPRO) across 13 institutional-grade valuation models reveals how each company's intrinsic value stacks up against its market price. CirclFi's engine processes SEC EDGAR 10-K and 10-Q filings, FRED macroeconomic data, and GDELT news sentiment to generate independent fair value estimates daily.

BFH currently trades at $89.07 with a QOC of 8.0/10, while LPRO trades at $2.28 with a QOC of 7.2/10.

Both companies are analyzed with models spanning intrinsic (Bayesian DCF, EPV), scenario-based (First Chicago), regime-switching (Markov DDM, RCMH-DCF), machine learning (ML-RIV, FTNN Topology), and ensemble methods (CUCE).