Сбор и анализ данных банковских систем и неструктурированных данных для обнаружения мошенничества

More and more banks use the analysis of structured information to obtain up-to-date information about the state of the business and making operational decisions. The use of data mining technology allows Bank to more effectively assess possible risks and increase the number of cases of fraud detection. A deep knowledge of the domestic specifics combined with the use of international experience in countering fraud, as well as the use of advanced technologies, make it possible to create an effective analytical platform for countering fraud that meets the requirements of management and owners of modern banks. The development of technologies for processing big data can play a key role in fraud prevention and detection. The area of the master thesis research is the fraud detection in bank based on all available data. It can be well-structured database or heap of files stored in data warehouse. One of the critical problems there is how to extract data, which should be analyzed, from different bank systems, define entities which should be investigated and use for the analytical model and how to manage these analytical processes in a real time. The aim of the thesis is to analyze the existing methods of protection and to model a fraud detection solution based on an analysis of all available data.