Design and Implementation of Financial Smart Contract Services on Blockchain
Bibliographic record
Abstract
Blockchain technology is disrupting current business models and practices by making intermediary services obsolete and the term has become a buzzword worldwide. One particular area based on the distributed ledger technology that has grabbed the attention of many financial businesses is "Smart Contracts". A lot of research is going on to harness its full potential in many fronts from small to large financial businesses. Currently, the Collateral Smart Contract Services (CSCS) are manually processed in financial institutions. The main objective of this research is to help financial institutions in choosing the most appropriate financial instrument for CSCS and allow its automated trading using blockchain technology, which we have achieved on one of the prominent blockchain platforms. We have designed a Chaincode for CSCS using Hyperledger Fabric, maintaining a transparent distributed ledger for the CSCS between financial institutions. The clients in the network are permissioned and possess certificates that they utilize to interact with the Hyperledger Fabric network. Go programming language has been used to create contracts on Hyperledger Fabric. One of the main challenges in the implementation has been to identify different elements that are required to get the network and chaincode running on the Hyperledger. We have designed a network consisting of multiple organizations each one connecting with other organizations for the Hyperledger transactions, for achieving collateral services among these institutions. The experimental evaluation criteria designed for this study could be used as formal procedure for designing financial smart contracts on Hyperledger.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".