Finance / Completed / Apr 2026 - May 2026

U.S. Bank Peer Profitability, FTP & Scenario Analytics

Decision-support system for finance and strategy teams to compare peer economics, explain profitability drivers, and test earnings sensitivity under transparent assumptions.

Role
Independent Analyst / End-to-end owner
Scope
SEC/FRED pipeline, FTP methodology, scenarios, DAX, report UX
Coverage
2018-2025 reported / through 2028 Q1 modeled
6
banks analyzed
2,402
SEC financial records
13,121
market-rate observations
4
modeled scenarios
Six-bank public-data modelPipeline, peer profitability, efficiency, FTP, and scenario sensitivity

Interactive Power BI

Written analysis

U.S. Bank Peer Profitability, FTP & Scenario Analytics

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The question

Comparing large banks requires more than ranking net income. The analysis must separate scale from profitability, connect funding costs to loan and deposit economics, and show how earnings respond when rates, expenses, credit losses, and capital assumptions change.

PythonpandasPower BIDAXSEC EDGARFRED

Model inputs

Scenario assumptions

Base

Rate shock
0 bp
Deposit beta
0.35
Loan yield beta
0.55
Expense growth
3.00%
Credit loss
0.60%
Capital ratio
9.50%

Rate Up 100bp

Rate shock
+100 bp
Deposit beta
0.45
Loan yield beta
0.65
Expense growth
3.30%
Credit loss
0.70%
Capital ratio
10.00%

Rate Down 100bp

Rate shock
-100 bp
Deposit beta
0.25
Loan yield beta
0.45
Expense growth
2.80%
Credit loss
0.60%
Capital ratio
9.50%

Credit Stress

Rate shock
+50 bp
Deposit beta
0.50
Loan yield beta
0.50
Expense growth
4.50%
Credit loss
1.80%
Capital ratio
11.00%

Modeled estimates calibrated from public SEC EDGAR and FRED data, not internal bank forecasts or regulatory stress-test results.