Internet of Things and Blockchain-Based Smart Contracts: Enabling Continuous Risk Monitoring and Assessment in Peer-to-Peer Lending
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it