One of the main effects of the Fourth Industrial Revolution is increased human productivity and creativity. Over the past year we have seen companies continue to be exposed to massive fraud, corruption and cyber security attacks. To continuously outsmart the fraudsters, who keep on perfecting their tactics due to new emerging technology, organisations need new, efficient and effective tools such as visualized big data analytics. Although data is available in abundance and it provide enormous potential to deploy analytics and uncover what mechanisms fraudsters use, fraud analytical models should satisfy various requirements. The amount of data produced by any large organisation is growing at a rate of 40% to 60% per year. Simply storing this huge amount of data is not going to be useful if it is not used smartly.
By using data smartly, using intelligence analysis to combine datasets and detect individual’s relationships, connections and time-lines using visualization tools the initial fuzzy dots can respond to the data evolution by means of cost-effective and efficient tools, multidisciplinary insight, re-engineered systems and processes, response and prevention (data-to-decision process) of fraud. By layering data modelling onto various forms of customer data, employee data, exception reporting, financial trends, geospatial data, service provider data, historic fraud data, etc, it becomes possible to develop new fraud prevention strategies based on machine learning models. To make sure that the models continuously performs at its best, they should be fed with the most accurate and relevant data available.