WORKING PAPER SERIESFEDERAL RESERVE BANK of ATLANTA WORKING PAPER SERIES Technological Change, Financial Innovation, and Diffusion in Banking
Bibliographic record
Abstract
Abstract: This paper discusses the technological change and financial innovation that commercial banking has experienced during the past twenty-five years. The paper first describes the role of the financial system in economies and how technological change and financial innovation can improve social welfare. We then survey the literature relating to several specific financial innovations, which we define as new products or services, production processes, or organizational forms. We find that the past quarter century has been a period of substantial change in terms of banking products, services, and production technologies. Moreover, while much effort has been devoted to understanding the characteristics of users and adopters of financial innovations and the attendant welfare implications, we still know little about how and why financial innovations are initially developed. JEL classification: G21, O31, O33 Key words: technological change, financial innovation, banking The authors thank Pam Frisbee for research assistance. Helpful comments were provided by Allen Berger, Jose Negrin, and seminar participants at the Banco de México and Autonomous University of Mexico. The views expressed here are the authors ’ and not necessarily those of the Federal Reserve Bank of Atlanta or the Federal Reserve System. Any remaining errors are the authors’ responsibility.
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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 | 0.004 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.010 | 0.005 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.006 | 0.003 |
| Insufficient payload (model declined to judge) | 0.084 | 0.039 |
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".