5 research outputs found
Effects of Underwriting and Claims Management on Performance of Property and Casualty Insurance Companies in East Africa
No full textThe insurance sector plays an important role in service economy of any country by underwriting of risks inherent in most sectors thus providing a sense of peace to most economic entities. Performance of general insurance companies is expected to be related to various factors, including optimal underwriting and prompt and efficient claims management functions. This study investigated the effect of underwriting and claims management practices on the performance of general insurance firms in East Africa. The study employed multiple linear regression analysis using primary and secondary data collected from 82 general insurers in Kenya, Uganda and Tanzania. The findings show that there is a significant positive relationship between underwriting and claims management practices employed by the firms and non-financial performance, but the relationship with financial performance was insignificant. The implication is that a profit oriented insurance firm should embrace a claims function that is closely related with the underwriting and pricing of the firm’s portfolio for meaningful results. It is recommended that general insurance companies focus on other important factors besides underwriting and claims management order to improve overall financial performance
Actuarial Risk Management Practices, Underwriting Risk and Performance of P & C Insurance Firms in East Africa
No full textThe purpose of the study was to establish the intervening effect of underwriting risk (loss ratio) on the relationship between actuarial risk management practices (ARMP) and performance of property and casualty (P & C) insurance underwriters in East Africa. Findings from primary and secondary data gathered from 82 general insurers from Kenya, Uganda and Tanzania show that there is a significant positive relationship between ARMP and non-financial performance and that loss ratio significantly mediates this relationship. The relationship with financial performance was however insignificant. The implication is that P & C insurance firms should keenly watch their loss ratios in order to improve their non-financial performance by correctly underwriting, pricing and reinsuring their risks in order to influence their claims ratio and also have a strategic claims management program in place that controls costs and leads to better firm reputation, which in turn will have ripple effect in increasing business volumes and performance. It is recommended that further empirical studies be carried out to establish other factors that especially influence financial performance
Mediating Effect of Strategy Implementation on the Relationship Between TMT Characteristics and Performance of Ugandan State Agencies
No full textThe aim of this study is to determine the mediating effect of strategy implementation of the relationship between TMT characteristics and performance of Ugandan state agencies. The study was anchored on the upper echelons’ theory and dynamic capabilities theory. The study adopted a descriptive cross-sectional research design. The target population of the study was the 201 state agencies in Uganda. The study adopted at least three members of the TMT depending on the number of TMT members of the 160 selected state agencies in Uganda to gather the required information. Primary data was gathered using a structured questionnaire that was administered online. Inferential statistics employed regression analysis to test the hypothesis and draw conclusions. Haye’s (2022) PROCESS 4 (model 4) was utilised to test the hypothesis of this study. Furthermore, strategy implementation partially mediates the relationship between TMT characteristics and performance (Indirect effect of strategy implementation, b=.385, p<0.05 and the direct effect, b = .267, p<0.05). From the findings of this study, the research concludes that that strategy implementation has a significant partial mediating effect on the relationship between TMT characteristics and the performance of Ugandan state agencies. In addition, the results imply that the specific mechanism by which the connection between TMT characteristics and the performance of Ugandan state agencies occurs is direct, strategy implementation contributes a part to the relationship. This study recommends that individuals that make the TMT should have significant expert capabilities that give relevance while formulating and executing strategies. The study also recommends that strategy implementation should have a framework that is not affected by politics and corruption. This study also recommends that state agencies in Uganda need to create a prize and acknowledgement framework for TMTs and personnel who succeed in strategy implementation so they can be persuaded. This is on the grounds that it is through strategy implementation that the state agencies in Uganda can follow through on their directives and further improve service delivery. Rewards give a chance to the TMTs and staff to contend among themselves and this would bring quality, efficiency, proficiency and adequacy in delivering services
NATURE OF FRAUD AND ITS EFFECTS IN THE MEDICAL INSURANCE SECTOR IN KENYA
No full textABSTRACT Insurance fraud is a major challenge facing the insurance industry both in thedeveloping and developed world. This vice has no doubt existed wherever insurance policies areunderwritten and takes different forms depending on the economic time and coverages available.However, the validity of this claim has hardly been established empirically in Kenya. It is importantthat the insurance players in Kenya understand the nature and effects of insurance fraud and alsocome up with strategies to counter the same. The study objective was to investigate the nature offraud and its effects in the medical insurance sector in Kenya and also establish possible solutionsin countering the vice. The study adopted a descriptive research design where each of the twentyeight registered medical insurance providers and twenty Insurance companies underwritingmedical insurance in Kenya formed the sample frame of forty eight firms. A questionnaire was themain research instrument. The study findings revealed that majority of the firms sampled hadexperienced different levels of fraud in the recent past with the fraud form ranging from overstatedmedical bills, concealment of medical history of the patient, fraudulent identity / impersonation,document theft fraud as well as perpetration of the insurance premium fraud. The extent of fraudwas found to depend on the existence and extent of automation that the firms had adopted with highfraud levels being associated with low IT Usage and/or automation. The effects of fraud include:increase in the cost of medical insurance and tarnishing the image of the insurance industry.Solutions suggested in manageing the level of fraud include: subjecting medical bills to extensiveaudit to determine their validity as well as high levels of automation of the processes, making itmandatory for clients to produce their smart-cards in any medical facility before receiving services,and maintaining a database of all insured within the organizations’ network. Other strategiesinclude restriction of unauthorized employees in accessing client information, educating the staff touphold ethical practices and offering a better remuneration and friendlier work environment. Thisstudy contributes to a partial understanding of the reasons for medical covers being expensive andthe negative image of the insurance industry
Soil erosion modelling at European scale by using high resolution input layers
No full textSoil erosion by water is one of the most widespread forms of soil degradation. Since soil erosion is difficult to measure at large scales, soil erosion models are a crucial estimation tool at regional, national and European levels. The high heterogeneity of soil erosion causal factors, combined with often poor data availability is an obstacle for the application of complex soil erosion models. Thus, the empirical Revised Universal Soil Loss Equation (RUSLE) (Renard et al., 1997), which predicts the average annual soil loss resulting from raindrop splash and runoff from field slopes, is still most frequently used at large spatial scales. The RUSLE is the simple multiplication of 5 soil erosion risk factors:
• Soil erodibility (K-factor)
• Rainfall erosivity (R-factor)
• Cover and management (C-factor)
• Support practices (P-factor)
• Slope length and Steepness (LS-factor)
The PhD study proposes a new soil erosion map of Europe (RUSLE2015) which has the following characteristics:
- It is based on peer review and high quality input factors
- The factors are composite layers: K-factor includes stoniness, C-factor includes Management Practices (tillage practices, cover crops, plant residues) and Vegetation fraction through remote sensing, R-factor includes high temporal resolution precipitation measurements of 1541 stations, P-factor includes support practices (contouring, stone walls, grass margins) and LS-factor is based on 25m Digital Elevation Model.
- has a very fine resolution of 100m
- allows land use and management scenarios and can be used by policy makers
- makes the data available in European Soil Data Centre (ESDAC)
- it is proposed in a transparent way and follows the literature principles
The first chapter makes a comparison of the pan-European soil erosion data collection (named EIONET-SOIL) with the modelled data from PESERA. This data collection concluded that almost all member states of the European Union are using (R)USLE models for the estimation of soil erosion. The paper identified the areas with high discrepancies between the two different soil erosion estimation approaches. By concluding this study, I have decided to use a RUSLE approach at European scale due to limitations of PESERA and data availability from Member states using RUSLE.
The second chapter includes the key parameter for modelling soil erosion which is the soil erodibility, expressed as the K-factor. The soil erodibility which expresses the susceptibility of a soil to erode, is related to soil properties such as organic matter content, soil texture, soil structure and permeability. With the Land Use/Cover Area frame Survey (LUCAS) soil survey in 2009 a pan-European soil dataset is available for the first time, consisting of around 20,000 points across 25 Member States of the European Union. The aim of this study is the generation of a harmonised high-resolution soil erodibility map (with a grid cell size of 500 m) for the 25 EU Member States.
The third chapter proposes a new soil erosion model named G2 which uses the empirical formulas of the Universal Soil Loss Equation (USLE). The difference is that G2 makes allows for the integrated spatio-temporal monitoring of soil erosion as the Rainfall erosivity (R-factor) and Vegetation retention (V-factor; known as C-factor in USLE) are proposed on a monthly temporal resolution. This study in Crete (Greece) allowed to deep the knowledge of each erosion factors which are to be modelled at European scale.
The fourth chapter assesses rainfall erosivity in Europe in the form of the RUSLE R-factor, based on the best available datasets. Data have been collected from 1,541 precipitation stations in all European Union (EU) Member States and Switzerland, with temporal resolutions of 5 to 60 minutes. The R-factor values calculated from precipitation data of different temporal resolutions were normalised to R-factor values with temporal resolutions of 30 minutes using linear regression functions.
The fifth chapter assesses rainfall erosivity in Greece on a monthly basis in the form of the RUSLE R-factor, based on 30-minutes data from 80 precipitation stations covering an average period of almost 30 years. The proposed R-factor spatio-temporal analysis shows a high intra-annual variability of rainfall erosivity which should also be investigated in whole Europe.
The sixth chapter presents the cover-management factor (C-factor) which is considered to be the most important because policy makers and farmers can intervene and, as a consequence, may reduce soil erosion rates. In arable lands, the C-factor was estimated using crop statistics (% of land per crop) and management practices data such as conservation tillage, plant residues and winter crops. The C-factor in non-arable lands was estimated by weighting the range of literature values found by fractional vegetation cover, which was estimated based on the remote sensing datasets.
The seventh chapter assesses support practice factor (P-factor) which is rarely taken into account in soil erosion risk modelling. The P-factor model considers the latest policy developments in the Common Agricultural Policy (contour farming) and the impact of stone walls and grass margins in reducing soil loss. The P-factor modelling tool can potentially be used by policy makers to run soil-erosion risk scenarios.
The eight chapter proposes an overview of the RUSLE2015 model and presents the final soil erosion map of Europe based on the input layers discussed in previous chapters. This concluding chapter makes an assessment of the soil erosion map per land use, region and per class of soil erosion. The verification of the map with other data sources has been satisfactory. Finally, this chapter proposes the use of soil erosion map for policy making in European Union and predicts the soil erosion trends based on land management and land use changes.
The first 4 chapters are published in peer review journals. The 5th chapter is under revision after initial acceptance, the 6th and 7th chapters have initially been accepted (under second revision) and editors have requested some changes. The concluding chapter (9th) has been submitted in February in a high impact factor peer review journal
