Geo-Banking Data

Information flow (both large and small), in dynamic interactions with local geographic conditions, can leave a strong imprint on the way customers access reliable financial information, eventually improving their daily lifestyles. Such a context is important in geographically and socio-economically challenged economies, such as Africa. The challenges are acute when the information flow is very large, as the increasing availability of big data in these economies requires resilient and need-based adaptive innovation solutions. In this paper, we propose a location-aware recommender algorithm called Geo-Banking which uses a Geographical Information System (GIS) approach. We created a series of questionnaires and distributed them using Google Forms to a purposively sampled group of respondents, totaling thirty-two, to assess our suggested Geo-Insurance technique. We verified the respondents' background information, including gender. Ten (10) men, or 31.25% of the participants, and twenty-two (22) women, or 68.75%. Before the assessment, the research participants in Table I were acquainted with the Geo-Insurance App and had utilized it. The technology was also made available to the participants throughout the pilot phase so they could use it directly.

信息流动（无论规模大小），在与本地地理条件的动态互动中，能够在客户获取可靠金融信息的方式上留下深刻的烙印，进而提升其日常生活品质。此类情境对于地理和社会经济挑战较大的经济体，如非洲地区，尤为重要。当信息流动规模巨大时，挑战尤为严峻，因为这些经济体大数据的日益丰富需要具备弹性且符合需求的适应性创新解决方案。在本研究中，我们提出了一种名为Geo-Banking的位置感知推荐算法，该算法采用地理信息系统（GIS）方法。我们设计了一系列问卷，并通过Google Forms向三十二位有意识地选取的受访者进行分发，以评估我们所提出的Geo-Insurance技术。我们验证了受访者的背景信息，包括性别。其中十名（10）男性，占参与者的31.25%，以及二十二名（22）女性，占68.75%。在评估之前，表I中的研究参与者已熟悉Geo-Insurance应用，并已使用过该应用。在试点阶段，该技术也对参与者开放，以便他们可以直接使用。