Bank and Credit Union Business Models in the United States
Bibliographic record
Abstract
This study comes in response to the continuing evolution of market structures and regulatory overhaul since the financial crisis of 2007-2009 in the US. The banking sector has largely suffered after the collapse of Lehman Brothers in September 2008. The initial context of decades of deregulation has been followed by the Dodd-Frank Act to respond to the overly damaging 2007-2009 global financial crisis, with the aim of safeguarding financial stability and putting an end to government bailouts. This first investigation into the bank and credit union business models for the United States, offers an extensive insight into 10,352 banks and 10,392 credit unions, which respectively account for almost all total banking assets of the country and more than 80% of the total assets of credit unions. Using regulatory data from 2000 to 2014, the two samples of 108,226 bank-year observations and 115,516 credit-union-year observations are each clustered into distinct bank and credit union business models, using a novel definition and applying a robust clustering methodology. The definition uses the activity and funding profiles of a bank or a credit union based on balance sheet indicators. Four bank business models and three credit union business models are identified. The study proceeds then by thorough assessments of the interaction between business models and size, as well as the migration, financial performance, contribution to the real economy, risk and response to regulation of US banks and credit unions, using a rich palette of indicators.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".