Senior officials report their assets to the relevant national anti-corruption institution (ACI) each year. For an ACI, is a complex problem, how to detect the abnormal (unusual) growth of the assets of senior public officials. The digitalization of the systems of asset declarations can help the ACIs to identify the public officials that have abnormal growth of assets by using machine learning methods on the data of the asset declarations. Most asset declaration systems are designed for gathering and managing data of asset declarations. The main aims of those systems are to compare and verify the data of asset declarations of senior officials against their supposed real state of properties. This paper presents an analysis of features, methodology, and workflows for the systems of asset declaration in five countries, such as Albania, France, Kosovo, North Macedonia, and Ukraine. In addition, the paper presents a comparative analysis of the electronic systems to see if they are using any algorithmic methods for automated analysis of the assets declared data to detect suspicious declarants. Our analysis shows the similarities in the workflow processes of ACIs for components such as attributes, submission, and verification of declarations. Also, the paper presents the differences between SDRP, EACIDS, E-DECLARATION, SIMIDAI, and ADEL electronic systems regarding online declaration, process automation, cross-validation, and full control. By implementing an automated risk analysis module based on the ’Red Flags’ algorithm, SIMIDAI appears as the optimized system for declaring assets.
Asset Declaration System · E-declaration · Indicators · Data Analysis.
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