Geo-Insurance
Improving Big Data Challenges in the Context of Insurance Services Using a Geographical Information System (GIS)
Bibliographic Data
| ID | 23039629 |
|---|---|
| Authors | Nana Yaw Asabere (0000-0003-4908-8528, Accra Technical University, corresponding author), Isaac Ofori Asare (0000-0002-1503-1470, Accra Technical University), Gare Lawson (0009-0004-3834-0462, Ghana Communication Technology University), Fatoumata Balde (0009-0004-1643-9365, Université Alioune Diop de Bambey), Nana Yaw Duodu (0000-0002-6466-1241, Accra Technical University), Gifty Tsoekeku (Accra Technical University), Priscilla Owusu Afriyie (Accra Technical University), Abdul Razak Abdul Ganiu (Accra Technical University) |
| Editors | Saad G Yaseen (0000-0002-3079-9410) |
| Year | 2024 |
| Volume | 2024 |
| Issue | 1 |
| Publication date | 2024-01-01 |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Human Behavior and Emerging Technologies (JOURNAL) |
| Journal identifiers | ISSN: 2578-1863 • E-ISSN: 2578-1863 |
| Publisher | Wiley (PUBLISHER • GB) |
| DOI | 10.1155/2024/9015012 |
| OpenAlex | W4401543804 |
| Language | EN |
| References cited | 45 |
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 ( F 1), recall ( R ), precision ( P ), mean absolute error (MAE), and normalized MAE (NMAE).
Big data · Business · Context (archaeology) · Data mining · Data science · Geographic information system · Geography · Information system · Remote sensing · Computer Science · Customer churn and segmentation · Engineering · Insurance and Financial Risk Management · Technology Adoption and User Behaviour
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| Highly cited | No |
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