This study aimed to identify financial and cash flow risks associated with SMEs and investigated how managers perceived these risks using the analytical hierarchical process (AHP). Accordingly, a three-level decision model was structured using two criteria, probability and consequences, and a list of six different types of risks as decision alternatives. Data were collected by a survey questionnaire from SME managers/owners and analyzed in accordance with the AHP method. The results show that the priority weight for risk criteria was 52% for probability and 48% for consequences. Further, with an average weight of 18.8%, the risk of an increase in bank charges ranked as the highest type of risk faced by SMEs. However, the risk of low or no profits was ranked as the lowest with an average weight of 13.4%. This study is one of the few, if not the first, to investigate SME managers’ perceptions using an AHP method and to provide insightful information on how SME managers/owners perceived various financial and cash flow risks. The study results may support the use of the AHP method in understanding managers’ perceptions and attitudes toward various types of risks associated with SMEs.

This paper introduces a new class of probability distributions, termed the generated log exponentiated polynomial (GLEP) family, designed to enhance flexibility in modeling complex real financial data. The proposed family is constructed through a novel cumulative distribution function that combines logarithmic and exponentiated polynomial structures, allowing for rich distributional shapes and tail behaviors. We present comprehensive mathematical properties, including useful series expansions for the density, cumulative, and quantile functions, which facilitate the derivation of moments, generating functions, and order statistics. Characterization results based on the reverse hazard function and conditional expectations are established. The model parameters are estimated using various frequentist methods, including Maximum Likelihood Estimation (MLE), Cramer–von Mises (CVM), Anderson–Darling (ADE), Right Tail Anderson–Darling (RTADE), and Left Tail Anderson–Darling (LEADE), with a comparative simulation study assessing their performance. Risk analysis is conducted using actuarial key risk indicators (KRIs) such as Value-at-Risk (VaR), Tail Value-at-Risk (TVaR), Tail Variance (TV), Tail Mean Variance (TMV), and excess function (EL), demonstrating the model’s applicability in financial and insurance contexts. The practical utility of the GLEP family is illustrated through applications to real and simulated datasets, including house price dynamics and insurance claim sizes. Peaks Over Random Threshold Value-at-Risk (PORT-VaR) analysis is applied to U.K. motor insurance claims and U.S. house prices datasets. Some recommendations are provided. Finally, a comparative study is presented to prove the superiority of the new family.

This research paper explores the electronic government adoption and implementation level by local government entities in Sudan. It examines how local governments have embraced and integrated digital technologies, platforms, and services using Layne and Lee four stages model. The study employed a content analysis method to collect data from 18 official websites of Sudanese local government entities. The findings suggest that while local e-government initiatives are still in the informative stages, many governments plan to develop more sophisticated e-government offerings. The key barriers and challenges to adopt and implement e-government identified in the research include technological barriers, lack of resources, digital divide, and legal and regulatory obstacles.