LPRO vs LU

Open Lending Corporation vs Lufax Holding Ltd — Valuation Comparison 2026

LPRO

Credit Services
Open Lending Corporation
Quality
7.2
out of 10
Value Trap
32
LOW
Price
$2.29
Last close
Models
13/13
Active
VS

LU

Credit Services
Lufax Holding Ltd
Quality
4.9
out of 10
Value Trap
8
SAFE
Price
$1.65
Last close
Models
8/13
Active

Model-by-Model Comparison

ModelType LPRO Fair ValueLPRO Upside LU Fair ValueLU Upside
Bayesian DCF Intrinsic $5.39 +135.4%
Earnings Power Value Intrinsic $7.75 +337.9%
EROIC Spread Intrinsic $•••.•• ••.•% $•••.•• ••.•%
First Chicago Scenario $1.08 -52.9% $4.28 +159.4%
Markov DDM Intrinsic $4.53 +97.8% $7.62 +361.8%
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 LPRO vs LU — including EROIC Spread, First Chicago, Markov DDM, PWERM, and 7 more.

Access Full Analysis — From $27/mo →

LPRO vs LU — Which Stock Is More Undervalued?

LPRO scores higher with a 7.2/10 quality rating vs LU's 4.9/10. Both stocks are analyzed daily using SEC EDGAR filings across 13 independent models.

Comparing Open Lending Corporation (LPRO) and Lufax Holding Ltd (LU) 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.

LPRO currently trades at $2.29 with a QOC of 7.2/10, while LU trades at $1.65 with a QOC of 4.9/10.

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).