Fintech startups in Germany: firm failure, funding success, and innovation capacity
Bibliographic record
Abstract
Fintech startups have set out to revolutionize the financial world. However, little is known about how successful and innovative these firms actually are. This paper investigates firm failure, funding success, and innovation capacity using a hand-collected dataset of 892 German fintechs founded between 2000 and 2021. We find that founders with a business degree and entrepreneurial experience have a better chance of obtaining funding, while founder teams with science, technology, engineering, or mathematics backgrounds file more patents. Early third-party endorsements and foreign partnerships substantially increase firm survival. We also establish the following stylized facts: (1) fintechs focusing on business-to-business models and which position themselves as technical providers prove to be more effective; and (2) fintechs competing in segments traditionally reserved for banks are generally less successful and less innovative. These results have important implications for the early-stage success management of fintech firms and the investment decisions of venture capital funds and government startup programs.
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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 | 0.005 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".