SALER: A Data Science Solution to Detect and Prevent Corruption in Public Administration

In this paper, we introduce SALER, an ongoing project developed by the Universitat Politècnica de València (Spain) which aims at detecting and preventing bad practices and fraud in public administration. The main contribution of the project is the development of a data science-based solution to systematically assist managing authorities to increase the effectiveness and efficiency when analysing fraud and corruption cases. The tool combines descriptive and predictive machine learning models with advanced statistics and visualisations. In this regard, we define a number of specific requirements in terms of questions and data analyses, as well as risk indicators and other anomaly patterns. Each of these requirements will materialize in specific visualisations, reports and dashboards included in the final solution. Several internal and external data sources are analysed and assessed to explore possible irregularities in budget and cash management, public service accounts, salaries, disbursement, grants, subsidies, etc. The project has already resulted in an initial prototype (SALER Analytics) successfully tested by the governing bodies of Valencia, in Spain.