New neural model can predict public corruption

Scientists have developed a neural network model that can predict public corruption based on the economic and political factors.
Researchers from the National Research University in Russia and University of Valladolid in Spain used self- organising maps (SOMs), a neural network approach, to predict corruption cases in different time horizons.
SOMs are a kind of artificial neural network that aim to mimic brain functions.
SOMs have the ability to extract patterns from large data sets without an explicit understanding of the underlying relationships, researchers said.
They convert non-linear relations among high dimensional data into simple geometric connections.
These properties have made SOMs a useful tool to detect patterns and obtain visual representations of large amounts of data, according to the researchers.

Predicting corruption is a field in which SOMs can become a powerful tool, they said.

"Our research develops a novel approach with three differential characteristics. First, unlike previous research, which is mainly based on the perception of corruption, we use data on actual cases of corruption," said Felix J Lopez- Iturriaga from National Research University.
"Second, we use the neural network approach, a particularly suitable method since it does not make assumptions about data distribution," said Lopez-Iturriaga.
The results, published in the journal Social Indicators Research, show that economic factors prove to be relevant predictors of corruption.
Researchers found that the taxation of real estate, economic growth, increased house prices, and the growing number of deposit institutions and non-financial firms may induce public corruption.

They also found that the same ruling party remaining in power too long is positively related to public corruption.
Depending on the characteristics of each region, the probability of corrupt cases emerging over a period of up three years can be estimated.
Then the different patterns of corruption antecedents were detected, researchers said.
Whereas in some cases, corruption cases can be predicted well before they occur and thus allow preventive measures to be implemented, in other cases the prediction period is much shorter and urgent corrective political measures are required.
The method consists of a sophisticated algorithm with multiple non-linear relations according to which the determinants of the propensity to corruption change throughout the time.