Automatically Detecting Peer-to-Peer Lending Intermediary Risk—Top Management Team Profile Textual Features Perspective
Bibliographic record
Abstract
Peer-to-Peer lending is developing quickly around the world as a new E-finance industry, especially in China. Yet fraudulence and business ceasing of Peer-to-Peer Lending Intermediaries (P2P-INTs) occur frequently, making P2P investors facing serious risk. This paper attempts to explore a bridge connecting managerial research with some most advanced natural language processing (NLP) technologies, and examines the risk assessing power of automatic learning text classifiers based on data of hazard status and top management team profile texts of the P2P-INTs. A risk evaluation model named MULTIPLE NLP Integrated Learning Text Classifier (MUN-LETCLA) based on five NLP techniques and meta-learning is proposed. Then risk classification power of the MUN-LETCLA and the single NLP models is assessed. The results show that the proposed model is effective in classifying low-risk and high-risk P2P-INTs. The NLP models can automatically detect the P2P-INTs risk from Top Management Team (TMT) members’ working experience, educational background, and TMT composition with a precision level of more than 75%.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".