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The detection of anomalous events in large multivariate data is sought in many domains. Analysis of data is an important fraud detection procedure in detecting suspicious events and prevent attempts to defraud. While now the data is becoming more complicated and difficult as data scales and complexities increase than ever before, the rich insights within the data may be difficult to identify by traditional means and often remain hidden. People require powerful tools to extract valid conclusions from the data while maintaining trustworthy and interpretable results. Hence, various fraud detection approaches have started to exploit Visual Analytics (VA) techniques to reveal the hidden knowledge in such fraudulent activities. Interactive data visualization tools have substantial potential for making the detection of fraudulent transactions more efficient and effective by allowing the investigator to change the representation of data from text and numeric into graphics and filter out subsets of transactions for further fraud investigation. However, little research to date has directly examined the efficacy of data visualization techniques for fraud detection especially telecommunication fraud. In this paper, we present an overview of several fraud detection solutions that use data visualization techniques to detect fraudulent transactions in the telecommunication domain. The paper concludes by discussing how academic research might proceed in investigating the efficacy of interactive data visualization tools for fraud detection.

In this paper, we investigated the influence of resetting weights in what we refer to as safely satisfied sub areas within the search space. Our work is divided into two main tracks; track one is to search for sub areas within the search space where a group of connected clauses are all satisfied. In track two, a Weight Reset mechanism is designed and implemented within the Multi-Level Weight Distribution (mulLWD) algorithm, which produced a new algorithm known as mulLWD+WR. The impact of our new strategy, the Weight Reset mechanism, is illustrated via an extensive experimental range of evaluation on benchmarks obtained from the DIMACS and the SAT Competition 2017 problem sets. Our investigation and experimental evaluation shows that the Weight Reset mechanism, when compared to the state-of-the-art solving algorithms, can significantly improves the process of searching for solutions when solving hard Boolean satisfiability (SAT), Planning, scheduling, and many other hard combinatorial problems. Furthermore, the weight reset could be generalized to be employed by any Dynamic Local Search approach.