Geographic information may be used in audit tasks, such as identifying high-risk cases involving suspicious entities usually located close to each other. However, the existing approach of text string analysis on addresses may only be able to match companies located in the same city or street. Following a design science approach, we propose using the geographic proximity of two locations to address how utilizing different levels of geographic information could improve the effectiveness and efficiency in auditing and other business tasks. As a proof of concept, we used Python and Google API to build Geographic Information in Audit Analytics (GIAA), a tool for automatically collecting, generating, and outputting spherical distance information indicating geographic proximity. We used a bid-rigging case to demonstrate GIAA and perform qualitative and quantitative evaluations. This study addresses how auditors and others can benefit from more advanced levels of geographic information, supporting better judgment and decision making.

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