Bibliographic record
Abstract
As banks strive to find ways to increase profits against headwinds that include continued low interest rates and intensive regulation, they are compelled to look within all areas of the organisation for efficiencies that result in cost savings. Fierce competition from traditional competitors and the emergence of financial technology (Fin-Tech) in traditional banking areas are further pushing organisations to reduce costs, eliminate credit risk loses and optimise capital utilisation. For too long, commercial and corporate lending (‘banking book’) businesses have been hampered by manual processes, fragmented systems and multiple data sources. As a result, banking book lending is exposed to increased operational and credit risk and inefficient business decision making — impeding their ability to scale efficiently and therefore eroding profits. Loan collateral mitigates credit risk while also providing the opportunity to mitigate pressure on banking book performance through efficient use. To achieve these goals, however, automated and holistic collateral management operations are mandatory: to, specifically, reduce counterparty and operational risk; eliminate manual processes; improve capital efficiency and provide the accurate datasets required for capital reporting and management. Transformation to an automated and holistic banking book collateral management operation also enables the deployment of digital technologies including machine learning (artificial intelligence) and distributed ledger technology, adding further to the potential to improve risk management, operational efficiency and scalability across the banking book.
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.
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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.061 | 0.037 |
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".