Geo-Insurance: Improving Big Data Challenges in the Context of Insurance Services Using a Geographical Information System (GIS)
Both large and small information flows can have a significant impact on how consumers obtain trustworthy financial information, ultimately leading to an improvement in their daily lives when they interact dynamically with local geographic conditions. In economies that face both geographical and soci...
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| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2024-01-01
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| Series: | Human Behavior and Emerging Technologies |
| Online Access: | http://dx.doi.org/10.1155/2024/9015012 |
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| _version_ | 1841564440360648704 |
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| author | Nana Yaw Asabere Isaac Ofori Asare Gare Lawson Fatoumata Balde Nana Yaw Duodu Gifty Tsoekeku Priscilla Owusu Afriyie Abdul Razak Abdul Ganiu |
| author_facet | Nana Yaw Asabere Isaac Ofori Asare Gare Lawson Fatoumata Balde Nana Yaw Duodu Gifty Tsoekeku Priscilla Owusu Afriyie Abdul Razak Abdul Ganiu |
| author_sort | Nana Yaw Asabere |
| collection | DOAJ |
| description | Both large and small information flows can have a significant impact on how consumers obtain trustworthy financial information, ultimately leading to an improvement in their daily lives when they interact dynamically with local geographic conditions. In economies that face both geographical and socioeconomic challenges, such as those in Africa, this kind of context is crucial. Large information flows provide significant issues such as big data challenges in the insurance sector, which calls for robust, demand-driven, and adaptive innovation solutions. In this paper, we present a geographic information system (GIS)–based location-aware recommender algorithm, called Geo-Insurance. Using some selected insurance companies in Accra, Ghana, as a point of view for location and customer data, our proposed Geo-Insurance solution addresses the big data challenges of customers finding the closest insurance companies with specific services through a web-based map created using a geodatabase file, ArcCatalog, and ArcGIS (among others). We conducted a series of benchmarking experiments. Our evaluation results show that Geo-Insurance performs better than other contemporary methods in terms of F-measure (F1), recall (R), precision (P), mean absolute error (MAE), and normalized MAE (NMAE). |
| format | Article |
| id | doaj-art-0429eb8296474d318177a89f1cb729d3 |
| institution | Kabale University |
| issn | 2578-1863 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Human Behavior and Emerging Technologies |
| spelling | doaj-art-0429eb8296474d318177a89f1cb729d32025-01-02T22:40:12ZengWileyHuman Behavior and Emerging Technologies2578-18632024-01-01202410.1155/2024/9015012Geo-Insurance: Improving Big Data Challenges in the Context of Insurance Services Using a Geographical Information System (GIS)Nana Yaw Asabere0Isaac Ofori Asare1Gare Lawson2Fatoumata Balde3Nana Yaw Duodu4Gifty Tsoekeku5Priscilla Owusu Afriyie6Abdul Razak Abdul Ganiu7Department of Computer ScienceDepartment of Applied Mathematics and StatisticsDepartment of Unified Communication/Contact Center (UC/CC)Departement Technologie de l’Information et de la CommunicationDepartment of Computer ScienceDepartment of Computer ScienceDepartment of Computer ScienceDepartment of Computer ScienceBoth large and small information flows can have a significant impact on how consumers obtain trustworthy financial information, ultimately leading to an improvement in their daily lives when they interact dynamically with local geographic conditions. In economies that face both geographical and socioeconomic challenges, such as those in Africa, this kind of context is crucial. Large information flows provide significant issues such as big data challenges in the insurance sector, which calls for robust, demand-driven, and adaptive innovation solutions. In this paper, we present a geographic information system (GIS)–based location-aware recommender algorithm, called Geo-Insurance. Using some selected insurance companies in Accra, Ghana, as a point of view for location and customer data, our proposed Geo-Insurance solution addresses the big data challenges of customers finding the closest insurance companies with specific services through a web-based map created using a geodatabase file, ArcCatalog, and ArcGIS (among others). We conducted a series of benchmarking experiments. Our evaluation results show that Geo-Insurance performs better than other contemporary methods in terms of F-measure (F1), recall (R), precision (P), mean absolute error (MAE), and normalized MAE (NMAE).http://dx.doi.org/10.1155/2024/9015012 |
| spellingShingle | Nana Yaw Asabere Isaac Ofori Asare Gare Lawson Fatoumata Balde Nana Yaw Duodu Gifty Tsoekeku Priscilla Owusu Afriyie Abdul Razak Abdul Ganiu Geo-Insurance: Improving Big Data Challenges in the Context of Insurance Services Using a Geographical Information System (GIS) Human Behavior and Emerging Technologies |
| title | Geo-Insurance: Improving Big Data Challenges in the Context of Insurance Services Using a Geographical Information System (GIS) |
| title_full | Geo-Insurance: Improving Big Data Challenges in the Context of Insurance Services Using a Geographical Information System (GIS) |
| title_fullStr | Geo-Insurance: Improving Big Data Challenges in the Context of Insurance Services Using a Geographical Information System (GIS) |
| title_full_unstemmed | Geo-Insurance: Improving Big Data Challenges in the Context of Insurance Services Using a Geographical Information System (GIS) |
| title_short | Geo-Insurance: Improving Big Data Challenges in the Context of Insurance Services Using a Geographical Information System (GIS) |
| title_sort | geo insurance improving big data challenges in the context of insurance services using a geographical information system gis |
| url | http://dx.doi.org/10.1155/2024/9015012 |
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