Confidential Distributed Ledgers for Online Syndicated 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
Online syndicated lending offers quick and convenient financing support to individuals, while diversifying risks by pooling funds from multiple lenders into loan projects. It has experienced explosive growth, reaching a multibillion-dollar market. Establishing transparency is essential for constructing a trusted, fair, and regulation-compliant financial collaboration model. Meanwhile, confidentiality must be maintained to protect the sensitive financial information of individual lenders. Multi-party computation (MPC) can protect the input privacy of lenders, but it cannot safeguard the sensitive information revealed by the fund flow itself. To address these challenges, we propose a new collaborative financial ledger for online syndicated lending. It leverages homomorphic encryption/commitment to enable the reuse of intermediary states without compromising privacy throughout the entire lifecycle of a loan. This system also supports efficient regulation-compliant auditing. We streamline the framework design to optimize performance and develop a prototype system. Even with a large syndicate of 100 lenders, the system still achieves low-latency performance.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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