Risk Management Practices: A Survey of Micro-Insurance Service Providers in Kenya
Bibliographic record
Abstract
In the recent past, concerted efforts have been made to encourage financial service access to the poor starting with microfinance and subsequently micro-insurance. With complexity of insurance and the vulnerability of the target market, there are inherent risks that insurance companies face in serving the low-end market. This study documents these risks, discusses the strategies that Kenyan insurance companies are using to mitigate the risks and discerns creative strategies to minimize them. Purposive sampling was used to select 8 companies that offer micro-insurance products in Kenya, from which 49 key informants responded to the survey. Visual binning approach was used to describe the data, while statistical tests of correlation and association were carried out by use of Pearson Correlations and Chi-Square tests. The study singled out the most ubiquitous risks facing micro-insurance providers as; diseconomies of scale resulting from low penetration, limited distribution channels, correlation risks and rigid regulatory framework. The strategies being used to counter the risks include; use of technology to lower administration costs, control of moral hazard and adverse selection, thorough scrutiny of claims, development of risk measurement models and continuous monitoring of the clients. Micro-insurance service providers are advised to invest in research and actuarial services to improve pricing of the products, develop innovative distribution channels, adopt technology conscious partnerships and devise flexible premium payment terms to enhance control of micro-insurance risks. The industry regulator (Insurance Regulatory Authority) is further advised to ensure that micro-insurance policies are drafted in simple language understandable by the clients.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".