Palgrave Macmillan Studies in Banking and Financial Institutions Fraud Corruption EU Funding

The authors analyse the specific situations, observe the risks and finally, propose an innovative method that allows predicting fraudulent acts, which will be of interest to both academics, researchers, and policy makers in financial services, public finance, and financial crime. This book provides systematic comparative research of antifraud laws and context at EU countries using a Artificial Neural Network (ANN) model to predict illegal activities in ERDF and CF. It also details a map of corruption risk with the goal of reducing corruption and fraud in the management of European Regional Development Funds and Cohesion Funds through the incorporation of adequate measures and strategies derived from the resulting of EUMODFRAUD EU Project. The authors analyse the specific situations, observe the risks and finally, propose an innovative method that allows predicting fraudulent acts, which will be of interest to both academics, researchers, and policy makers in financial services, public finance, and financial crime. David Blanco-Alcántara is a Senior Lecturer in Corporate Finance at the University of Burgos, Spain. Fernando García-Moreno Rodríguez is a Senior Lecturer in Administrative Law in the Faculty of Law, University of Burgos, Spain. Óscar López-de-Foronda Pérez is a Senior Lecturer in Corporate Finance and Accounting at the University of Burgos, Spain. This book provides systematic comparative research of antifraud laws and context at EU countries using a Artificial Neural Network (ANN) model to predict illegal activities in ERDF and CF. It also details a map of corruption risk with the goal of reducing corruption and fraud in the management of European Regional Development Funds and Cohesion Funds through the incorporation of adequate measures and strategies derived from the resulting of EUMODFRAUD EU Project. The authors analyse the specific situations, observe the risks and finally, propose an innovative method that allows predicting fraudulent acts, which will be of interest to both academics, researchers, and policy makers in financial services, public finance, and financial crime.