Crime and Fraud Detection Using Clustering Techniques

Abstract

Criminal attacks have drastically increased over the years which make its detection increasingly vital. Fraud detection is a technique of identifying fraudulent activities. We intend to apply clustering techniques in order to analyze and detect fraud or crime patterns from a large set of data. By using various clustering techniques, distinct areas or clusters can be generated by mapping crime instances (i.e., by identifying the factors that lead to fraud). These are areas which have high probability of criminal occurrences which are derived based on historical crime records. Thus, with the help of results obtained based on clustering analysis of the crime data, crime trends today can be identified. Crime can be divided into different types such as location-based crimes, theft, murder, kidnap, fraud, etc., and slums, residential areas, commercial areas, etc., are different types of areas where criminal activities may occur. Primary database is collected based on the types of crimes, the location, and the physical description of the suspects and also the time period in which felony has taken place including the other available data relevant to the analysis. The available data is then processed and clustered, thereby revealing a general crime pattern which in turn helps detect frauds.