Insurance-Growth Nexus: Aggregation and Disaggregation

Kiwanuka A., Sibindi A.B.

PurposeThis study primarily aimed to explore how insurance literacy and perceived trust interact to affect insurance inclusion in Uganda. Through this, we aimed to determine whether perceived trust serves as a mediator in the relationship between insurance literacy and insurance inclusion.Design/methodology/approachThis research employed a correlational, cross-sectional and quantitative approach. A total of 400 voluntarily insured individuals in Uganda were sampled. Structured survey questionnaires were employed for data collection. PLS-SEM with bootstrapping was used to examine the hypothesized relationships.FindingsThe findings indicated a significant, positive correlation between insurance literacy with both insurance inclusion and perceived trust. Furthermore, perceived trust was identified as having a positive and significant impact on insurance inclusion in Uganda. Perceived trust was also established as a significant mediator in the connection between insurance literacy and insurance inclusion in the context of Uganda.Originality/valueThe contribution of this research resides in its explanation of how insurance literacy influences insurance inclusion in Uganda. For insurance literacy to influence insurance inclusion, a significant portion of insurance literacy is mediated through perceived trust of insurance providers.

Chand K., Chandel A., Tiwari R., Chauhan A.S.

Horvey S.S., Odei-Mensah J., Liebenberg A.P.

Singh D., Srivastava A.K., Malik G., Yadav A., Jain P.

AbstractAcademic interest in insurance and economic growth nexus has prospered in the last two decades. There needs to be more review‐based research in this area. We, therefore, reviewed the literature and presented future research directions helpful for the further development of the research field. This literature review seeks to enrich the discourse on insurance and economic growth through a comprehensive and detailed review of 126 articles covering 96 journals from 2004 to 2023. Using Theory, Context, Characteristics, and Methods (TCCM), a detailed analysis has been conducted on the prominent theories, research context, key variables, and the methodologies and analysis techniques employed in the literature over the past 19 years. Through content analysis, we present the findings across three knowledge dimensions related to insurance and economic growth: research focus, country focus, and insurance focus. Our research sheds light on under‐researched contexts, variables, and analytical techniques.

Kiwanuka A., Sibindi A.B.

The purpose of this study was to establish whether digital literacy and insurtech adoption influence insurance inclusion in Uganda. Principally, we sought to determine whether insurtech adoption mediates the nexus between digital literacy and insurance inclusion. This study adopted a cross-sectional and quantitative correlational approach. The study’s sample was 391 individuals who had used digital platforms such as mobile phones and computers to access insurance products and services in Uganda. Data were collected using structured survey questionnaires. Partial Least Squares Structural Equation Modelling (PLSEM) was employed to test the hypothesised relationships. The results demonstrated that both digital literacy and insurtech adoption significantly and positively influence insurance inclusion. We also found digital literacy to be a significant and positive determinant of insurtech adoption. Markedly, it was found that insurtech adoption mediates the association between digital literacy and insurance inclusion in Uganda. However, this study was conducted in a developing country with an underdeveloped insurance market and with low technological advancement. This may affect the generalisation of the study’s findings. This study’s novelty lies in establishing how digital literacy and insurtech adoption interact to influence insurance inclusion in Uganda. This is the first study to examine the effect of digital literacy and insurtech adoption on insurance inclusion.

Ajide F.M., Osinubi T.T., Ojeyinka T.A.

The study examines the impact of the insurance market on economic complexity in 28 OECD nations within a period of 1995–2020. The study also examines whether the impact of life insurance on economic complexity would be different from that of the non-life insurance sector within the insurance market. The results based on pooled mean group (PMG) estimators reveal that the insurance sector influences economic complexity positively. This finding is further substantiated after employing panel co-integrating regression and method of moment quantile regression (MM-QR). The study concludes that the insurance sector is a key instrument in upgrading the economic complexity of an economy. Since the distributional impact of economic complexity also depends on economic and financial risk, the insurance sector can assist in mitigating the risks and uphold the productive knowledge structure needed to enhance national product sophistication.

Velmurugan R., Kumar R., Saravanan D., Patnaik S., Ikkurthi S.K.

This research uses Artificial Neural Network (ANN) algorithms to forecast cloud computing and security issues. To forecast cloud security level performance, suggested Levenberg–Marquardt dependent Back Propagation (LMDBP) Equations. The precision of cloud security level prediction-based could computing also be approximated using LMSBP techniques. Artificial neural networks (ANNs) are a more effective methodology to improve efficiency and learn neural membership functions. The cloud vector analysis has been used to collect data dynamically for risk detection. Based on their experience with cloud computing in the banking industry, 40 bankers’ data were selected from both inside and outside Malaysian banking organizations for this study. The LMBP, on the other hand, is a non-linear optimization model for calculating the performance of a prediction model by evaluating the Mean Square Error (MSE). The output is acceptable if the MSE is minimal, which is less than theshold value i.e 0.45. This research was carried out on cloud banking developers and IT administrators’ teams. The optimal integrating strategy with ANNs algorithms forecasts and minimizes important security and cloud issues. Optimistic predictions of significant cloud security problems, on the other hand, would increase the cloud-based banking performance. The performance measures like accuracy 98.76%, sensitivity 97.34%, Recall 94.53%, and F measure 97.82% had been attained. These results outperform the methodology and compete with current techniques.

Ellili N., Nobanee H., Alodat A.Y., Dilshad M.N., Nuzhat S.

This study aims to identify the current trends in the literature on marine insurance by applying a bibliometric review of documents published in the Scopus database. This analysis was based on the most cited papers, most influential authors, countries, and organizations, as well as the most frequent keywords. In addition, qualitative content analysis was conducted. It reviewed 293 documents on marine insurance. Bibliometric analysis was performed using VOSviewer, and qualitative content analysis was performed using WordStat. The results identify three major clusters: (1) risk assessment, (2) marine insurance, and (3) the insurance industry. This paper also presents recommendations for future research in this field. The findings of this study have implications for marine insurance, such as new developments to be implemented in the insurance industry to enhance its efficiency. This study is the first to review marine insurance publications that can be largely used for insurance practices. This study provides an overview of how the literature on marine insurance research has developed, as well as a summary of the most influential authors, along with countries, organizations, and journal sources. This offers an opportunity for future research to focus on this topic.

Kiwanuka A., Sibindi A.B.

The study examined the impact of perceived value, insurance literacy and perceived trust on insurance inclusion in Uganda. The study employed a cross-sectional design to solicit responses from 400 individuals that voluntarily enrolled on an insurance programme. The study hypotheses were tested using Covariance-Based Structural Equation Modelling. The results showed that perceived value, insurance literacy and perceived trust have a significant and positive prediction of insurance inclusion in Uganda. However, perceived trust explained more of the variations in insurance inclusion than perceived value and insurance literacy. Overall, the predictor variables explained 63.2% of the variance in insurance inclusion. This study contributes to the limited nascent literature on insurance inclusion. The implication of this study is that insurance providers need to focus on trust and delivering value to customers in order to promote insurance inclusion. Further, the study proffers advice to policymakers to include insurance literacy in the national financial inclusion strategies to foster insurance inclusion.

Kiwanuka A., Sibindi A.B.

The aim of the study was to establish the significance of the individual components of insurance literacy—knowledge, skills, attitude and behaviour—in explaining insurance inclusion in Uganda. The study was correlational and cross-sectional by design. Hence, 400 responses were obtained from individuals who enrolled for insurance. A hierarchical multiple regression analysis was adopted to test the predictive power of the dimensions of insurance literacy on insurance inclusion in Uganda. Before performing correlational and regression analyses, the study variables were tested for parametric assumptions, convergent and discriminant validity, common method variance and exploratory factors. The results of the study revealed that knowledge, skills and attitude significantly and positively predicted insurance inclusion in Uganda. Contrary to prior studies, behaviour was found to have an insignificant positive influence on insurance inclusion in Uganda. Overall, the individual components of insurance literacy explained 38.5% of the variation in insurance inclusion in Uganda. Notably, the current study contributes to the nascent literature on insurance literacy and insurance inclusion. Earlier studies have ignored the insurance component of financial inclusion. The originality of this study lies in that it is the first to examine the significance of the individual dimensions of insurance literacy towards explaining insurance inclusion. The implication of this study is that policymakers should consider insurance literacy in national financial inclusion strategies and financial literacy programmes in order to foster insurance inclusion.

Mane D., Kaliyaperumal K., Khurram S., Regin R., Aarthi R., Gawish A.

Brain tumor detection is a challenging task in medical image processing, and a huge amount of research is happening in this context. Radiologist sets aside a lengthy timespan for identifying and classify brain disorders. Denoising techniques will lessen the noise present in the image. The precise denoising method can valuable in distinguish brain disorder location, grade, size etc. the feature extraction techniques used to extract the features of the image. These features of the image used efficiently identify the issues.

Hemavathi D., Kumar V.R., Regin R., Rajest S.S., Phasinam K., Singh S.

This project evaluates the effectiveness of some DM methods for predicting rainfall using India's historical weather data gathered from the IDM database. Including 10 attributes from the collected meteorological data, only 5 features are generally believed to be significant for precipitation forecasting. The following phases of research are used in the collected data: data cleaning, data selection, Data Transformation and classification. Many pre-processing responsibilities are concerned with determining missing values and eliminating clutter. Hence, two responsibilities are carrying out for finding missing value purpose, which is cleaning and normalization. In this work, six data mining classification methods, including MLR, K-NN, SVM, ANN, Random Forest (RF) and decision tree (DT), were investigated. The classification process divides objects into different classes. A classifier is often trained using a training set, where single or multiple experts assign labels to a set of objects. The proposed RF is a novel classification technique used in rainfall data to classify the region differently from normal rainfall or heavy rainfall. The completed accuracy of the planned RF classifier is 96.1%, which is advanced than other classifiers used in the Rainfall database.