Internet of Things and Blockchain-Based Smart Contracts: Enabling Continuous Risk Monitoring and Assessment in Peer-to-Peer Lending
Bibliographic record
Abstract
ABSTRACT Peer-to-peer (P2P) lending enables individuals and small companies to finance and invest without the intermediation of financial institutions. However, this business model is also associated with high delinquency risk and a lack of risk monitoring and control capabilities. This paper explores the potential of the Internet of Things (IoT), blockchain, smart contract technologies, and the Continuous Risk Monitoring and Assessment (CRMA) framework to re-engineer risk monitoring and control for P2P lending. We conducted a case study of a large Chinese P2P lending company to identify problems in its current risk monitoring and control processes and to design an IoT-smart contract CRMA system to continuously monitor and respond to delinquency risk via real-time data collection, automatic loan settlement, and in-time risk disclosure. Data Availability: Data are available from the public sources cited in the text. JEL Classifications: M40; M41; M49.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".