Records management and risk management at Kenya Commercial Bank Limited, Nairobi
Bibliographic record
Abstract
Background: This paper reported empirical research findings of an MPhil in Information Sciences (Records and Archives Management) study conducted at Moi University in Eldoret, Kenya between September 2007 and July 2009.Objectives: The aim of the study was to investigate records management and risk management at Kenya Commercial Bank (KCB) Ltd, in the Nairobi area and propose recommendations to enhance the functions of records and risk management at KCB. The specific objectives of the study were to, (1) establish the nature and type of risks to which KCB is exposed, (2) conduct business process analysis and identify the records generated by KCB, (3) establish the extent to which records management is emphasised within KCB as a tool to managing risk, (4) identify which vital records of KCB need protection because of their nature and value to the bank and (5) make recommendations to enhance current records management practices to support the function of risk management in KCB.Method: The study was qualitative. Data were collected through face-to-face interviews. The theoretical framework of the study involved triangulation of the records continuum model by Frank Upward (1980) and the integrated risk management model by the Government of Canada (2000).Results: The key findings of the study were, (1) KCB is exposed to a wide range of risks by virtue of its business, (2) KCB generates a lot of records in the course of its business activities and (3) there are inadequate records management practices and systems, the lack of which undermines the risk management function.Conclusion: The findings of this study have revealed the need to strengthen records management as a critical success factor in risk mitigation within KCB and, by extension, the Kenyan banking industry. A records management model was proposed to guide the management of records within an enterprise-wide risk management framework in the bank.
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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.009 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".