Abstract We contend that corruption must be detected as soon as possible so that corrective and preventive measures may be taken. Thus, we develop an early warning system based on a neural network approach, specifically self-organizing maps, to predict public corruption based on economic and political factors. Unlike previous research, which is based on the perception of corruption, we use data on actual cases of corruption. We apply the model to Spanish provinces in which actual cases of corruption were reported by the media or went to court between 2000 and 2012. We find that the taxation of real estate, economic growth, the increase in real estate prices, the growing number of deposit institutions and non-financial firms, and the same political party remaining in power for long periods seem to induce public corruption. Our model provides different profiles of corruption risk depending on the economic conditions of a region conditional on the timing of the prediction. Our model also provides different time frameworks to predict corruption up to 3 years before cases are detected.
Concluding Remarks We develop a model of neural networks to predict public corruption based on economic and political factors. We apply this model to the Spanish provinces in which corrupt cases have been uncovered by the media or have gone to trial. Unlike previous research, which is based on the perception of corruption, we use data on actual cases of corruption. The output of our model is a set of SOMs, which allow us to predict corruption in different time scenarios before corruption cases are detected. Our model provides two main insights. First, we identify some underlying economic and political factors that can result in public corruption. Taxation of real estate, economic growth, and an increase in real estate prices, in the number of deposit institutions, and the same party remaining in office for a long time seem to induce public corruption. Second, our model provides different time frameworks to predict corruption. In some regions, we are able to detect latent corruption long before it emerges (up to 3 years), and in other regions our model provides short-term alerts, and suggests the need to take urgent preventive or corrective measures. Given the connection we find between economic and political factors and public corruption, some caveats must be applied to our results. Our model does not mean that economic growth or a given party remaining in power causes public corruption but that the fastest growing regions or the ones ruled by the same party for a long time are the most likely to be involved in corruption cases. Economic growth per se is not a sign of corruption, but rather it increases the interactions between economic agents and public officers. Similarly, being in office too long might prove to be an incentive for creating a network of unfair relations between politicians and economic agents. In addition, more competitive markets may induce some agents to pay bribes in order to obtain public concessions or a better competitive position. These results are consistent with some research exploring the relation between economic growth and corruption (Kuo et al. 2002; Kaufman and Rousseeuw 2009; Chen et al. 2002). Since corruption remains a widespread global concern, a key issue in our research is the generalizability of our model and the proposed actions. We have used fairly common macroeconomic and political variables that are widely available from public sources in many countries. In turn, our model can be applied to other regions and countries as well. Of course, the model could be improved if a country or region-specific factors were taken into account. Our approach is interesting both for academia and public authorities. For academia, we provide an innovative way to predict public corruption using neural networks. These methods have often been used to predict corporate financial distress and other economic events, but, as far as we are aware, no studies have yet attempted to use neural networks to predict public corruption. Consequently, we extend the domain of neural network application. For public authorities, we provide a model that improves the efficiency of the measures aimed at fighting corruption. Because the resources available to combat corruption are limited, authorities can use the early corruption warning system, which categorizes each province according to its corruption profile, in order to narrow their focus and better implement preventive and corrective policies. In addition, our model predicts corruption cases long before they are discovered, which enhances anticipatory measures. Our model can be especially relevant in countries suffering the severest corruption problems. In fact, European Union authorities are highly concerned about widespread corruption in certain countries. The study of new methodologies based on neural networks is a fertile field to be applied to a number of legal and economic issues. One possible direction for future research is to extend our model to the international framework and to take into account country-specific factors. Another application may be the detection of patterns of corruption and money laundering across different countries in the European Union.