During the last 20 years the volume and variety of broadly available data has driven the science and business to start searching the ways for extracting valuable information and knowledge from the data.
Big data volumes have overcome well known frames of statistics and manual data analysis. At the same time, super-fast technology changes combined with the development of mathematical algorithms, customized to achieve the best performances in the new technological and big data environment, has created a new frame for data analyses.
Data science has grown up into the most favorite applied science in the business sector. Companies in almost every industry are focused on collecting and exploiting internal and external data for competitive advantage. TeleSign generates a tremendous amount of Call Detail Record data per day. The big data possession enables us to use data science tools to automatize process of fraud detection.
Automated fraud detection has become mandatory segment of our business after the fraud has evolved into a big business. Large profits, expansion of modern technology and the global superhighways of communication have justified the growth of well-organized and well-informed community of fraudsters, resulting in the huge financial loss worldwide each year. Fraud detection methods are continuously developed to defend criminals in adapting to their strategies. This presentation describes a few main types of data science techniques available for automated fraud detection.