A team of scientists from the University of Valladolid (Spain) has developed a computer model based on neural networks which predict in which Spanish provinces cases of corruption are more likely to appear, outlining the conditions that favor their appearance. This alert system observes that the corruption probabilities increase when the same party keeps power in government for many years.

A team made of two researchers from the University of Valladolid has developed a model with artificial neural networks to predict in which Spanish provinces corruption cases could appear with more probability, after one, two and up to three years. The study, published in Social Indicators Research, avoids mentioning the provinces most like to present corruption, in order to avoid the creation of controversy, explains one of the authors, Ivan Pastor, to Sinc, who addresses the decision explaining that “a greater propensity or high probability does not imply corruption will actually happen.”

The data suggest that the real estate tax (Impuesto de Bienes Inmuebles), the exaggerated increase in the price of housing, the opening of bank branches and the creation of new companies are some of the variables that seem to induce public corruption, and when they are added together in a region, it should be taken into account to carry out a more rigorous control of the public accounts.

“In addition, as might be expected, our model confirms that the increase in the number of years in the government of the same political party increases the chances of corruption, regardless of whether or not the party governs with majority,” says Pastor. “Anyway, fortunately – he adds -, for the next years this alert system predicts less indications of corruption in our country. This is mainly due to the greater public pressure on this issue and to the fact that the economic situation has worsened significantly during the crisis.”

For their study, the authors have taken in account and analysed all the cases of corruption that appeared in Spain between 2000 and 2012, such as the Mercasevilla case (in which the managers of this public company of the Seville City Council were charged) and the Baltar case (in which the president of the Diputación de Ourense was sentenced for more than a hundred contracts “that did not complied with the legal requirements”).

The collection and analysis of all this information has been done with neural networks, which show the factors most likely to predict corruption. “The use of this AI technique is novel, as well as that of a database of real cases, since until now more or less subjective indexes of perception of corruption were used, scorings assigned to each country by agencies such as Transparency International, based on surveys of businessmen and national analysts,” highlights Pastor.

The authors hope that this study will play a role in directing efforts to end corruption in an effective way, focusing the efforts on those areas where corruption is more likely to appear, whilst continuing to move forward to apply their model internationally.

Written by: Pietro Paolo Frigenti