NSIT vs SCSC

Insight Enterprises, Inc. vs ScanSource, Inc. — Valuation Comparison 2026

NSIT

Electronics & Computer Distribution
Insight Enterprises, Inc.
Quality
7.5
out of 10
Value Trap
18
SAFE
Price
$103.38
Last close
Models
12/13
Active
VS

SCSC

Electronics & Computer Distribution
ScanSource, Inc.
Quality
6.8
out of 10
Value Trap
12
SAFE
Price
$45.82
Last close
Models
12/13
Active

Model-by-Model Comparison

ModelType NSIT Fair ValueNSIT Upside SCSC Fair ValueSCSC Upside
Bayesian DCF Intrinsic $33.31 -67.8% $14.55 -68.3%
Earnings Power Value Intrinsic $38.90 -62.4% $24.26 -47.1%
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 NSIT vs SCSC — including EROIC Spread, First Chicago, Markov DDM, PWERM, and 7 more.

Access Full Analysis — From $27/mo →

NSIT vs SCSC — Which Stock Is More Undervalued?

NSIT scores higher with a 7.5/10 quality rating vs SCSC's 6.8/10. Both stocks are analyzed daily using SEC EDGAR filings across 13 independent models.

Comparing Insight Enterprises, Inc. (NSIT) and ScanSource, Inc. (SCSC) 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.

NSIT currently trades at $103.38 with a QOC of 7.5/10, while SCSC trades at $45.82 with a QOC of 6.8/10.

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).