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Improving Operational Efficiency of Government using Artificial Intelligence
| Content Provider | Scilit |
|---|---|
| Author | Al-Witwit, Saif Salam Ibrahim Ibrahim, Abdullahi Abdu |
| Copyright Year | 2020 |
| Description | Journal: Iop Conference Series: Materials Science and Engineering In this paper we developed a technique for Improving the operational efficiency of the government using Artificial Intelligence, we discussed the concept of insurance and how the concept of insurance will change and Artificial Intelligence (AI) is already disrupting the state of this industry. Insurers worldwide are using AI to automatize processes and tasks, such as fraud detection, underwriting, and claims processing. Additionally, there has been a rise of new competitors in the market, such as InsurTechs, that are bringing innovative solutions for insurance using Artificial neural network (ANN), responding to the new trends in customers' lifestyles and behaviors, that are more demanding for services directed for their needs. This study aims to understand how personalization of insurance policies, created with Artificial Intelligence and how its efficiency can be improved, and how it will disrupt this industry in the future and what will be the impact on the government's operational efficiency. We have chosen worldwide Governance indicator dataset which is publically available the personalization of an insurance policy with AI would encompass the definition of the coverages and premiums more appropriate for an individual customer and do the risk evaluation, in a market of one strategy. This innovation would take advantage of the accrual of Big Data from customers for the optimization, as people are each time more connected and information about them is constantly being shared, allowing companies to use it to know consumers better and for the training, testing and validation a well-known MATLAB R2019a software was used for this purpose. We achieved an accuracy of 95.25% using 9-Fold cross-validation. |
| Related Links | https://iopscience.iop.org/article/10.1088/1757-899X/928/2/022014/pdf |
| ISSN | 17578981 |
| e-ISSN | 1757899X |
| DOI | 10.1088/1757-899x/928/2/022014 |
| Journal | Iop Conference Series: Materials Science and Engineering |
| Issue Number | 2 |
| Volume Number | 928 |
| Language | English |
| Publisher | IOP Publishing |
| Publisher Date | 2020-11-19 |
| Access Restriction | Open |
| Subject Keyword | Journal: Iop Conference Series: Materials Science and Engineering Artificial Intelligence Artificial Neural Network |
| Content Type | Text |
| Resource Type | Article |
