Denmark has during the last year been in the middle of an avalanche of whitewash scandals among others Danske Bank and Nordea, and more seems to follow. Without effective public control, the risk of money being lost by corruption and abuse is enormous. Citizens demand more transparency and openness about the process of awarding public contracts. Corruption also affects the poorest people in a country mostly by reducing access to important services such as health and education.
But with the growing digitalization, there are several opportunities to exploit available data to find the red flags that may indicate corruption and other integrity risks. With new technologies such as Artificial Intelligence and Machine Learning, one can avoid the former system that allowed bank fraudsters to continue to scam.
At the World Bank’s annual conference, the Anti-Corruption Collective Action Conference in November 2018, the agenda was to look at whether AI could promote transparency in all aspects of public administration. The World Bank’s initiative is part of a larger collaboration aimed at helping countries navigate in how technology can positively transform the public sector.
In addition to this, researchers from the University of Valladolid (Spain) have created a computer model based on artificial neural networks, that can predict cases of corruption in Spanish provinces with greater probability. The data collection and the analysis has been done with neural networks, which show the most predictive factors of corruption. With the use of AI techniques and databases of real cases, the research on corruption will be detected as soon as possible so that corrective and preventive measures can be taken. The Spanish researchers Félix J. López-Iturriaga and Iván Pastor Sanz, use the Artificial Neural Networks (ANN), that are computing systems vaguely inspired by the biological neural network that constitute animal and human brains. The neural network itself is not an algorithm, but rather a framework for many different machine learning algorithms to work together and process complex data inputs.
More specifically the Spanish researchers use a technique called Self-Organizing Maps (SOM), to predict public corruption based on economic and political factors. SOM is a type of ANN (artificial neural network) that is trained using unsupervised learning to produce a low-dimensional discretized representation of the input space of the training samples which is called a map.
SOM is therefore a method to reduce dimensions, where you can analyze patterns in large scale data sets. The SOM technique can therfore be a new tool for mapping criminal phenomena through processing of multivariate data, by identifying clusters and patterns in the data set.
Amongst other results the Spanish researchers can confirm, through this alert system, that when the same party stays in the government for years, the probabilities for corruption will increase, regardless of whether or not the party governs with majority.
As a concluding remark, it is off course important to combine ANN, SOM and other machine learning tools with behavioral and economic theory and analytics, to enhance anti-corruption compliance programs in the public as well as the private sector. Hopefully the new technologies will make the financial sector more transparent and will help the banks detect suspicious transactions in a much faster and effective way.
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