Trade Credit Insurance: Operational Value and Contract Choice
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
Trade credit insurance (TCI) is a risk management tool commonly used by suppliers to guarantee against payment default by credit buyers. TCI contracts can be either cancelable (the insurer has the discretion to cancel this guarantee during the insured period) or noncancelable (the terms cannot be renegotiated within the insured period). This paper identifies two roles of TCI: the (cash flow) smoothing role (smoothing the supplier’s cash flows) and the monitoring role (tracking the buyer’s continued creditworthiness after contracting, which enables the supplier to make efficient operational decisions regarding whether to ship goods to the credit buyer). We further explore which contracts better facilitate these two roles of TCI by modeling the strategic interaction between the insurer and the supplier. Noncancelable contracts rely on the deductible to implement both roles, which may result in a conflict: a high deductible inhibits the smoothing role, whereas a low deductible weakens the monitoring role. Under cancelable contracts, the insurer’s cancelation action ensures that the information acquired is reflected in the supplier’s shipping decision. Thus, the insurer has adequate incentives to perform its monitoring function without resorting to a high deductible. Despite this advantage, we find that the insurer may exercise the cancelation option too aggressively; this thereby restores a preference for noncancelable contracts, especially when the supplier’s outside option is unattractive and the insurer’s monitoring cost is low. Noncancelable contracts are also relatively more attractive when the acquired information is verifiable than when it is unverifiable. This paper was accepted by Vishal Gaur, operations management.
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 | 0.000 | 0.002 |
| 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