Intelligent Financial Fraud Detection Practices: An Investigation

Financial fraud is an issue with far reaching consequences in the finance industry, government, corporate sectors, and for ordinary consumers. Increasing dependence on new technologies such as cloud and mobile computing in recent years has compounded the problem. Traditional methods of detection involve extensive use of auditing, where a trained individual manually observes reports or transactions in an attempt to discover fraudulent behaviour. This method is not only time consuming, expensive and inaccurate, but in the age of big data it is also impractical. Not surprisingly, financial institutions have turned to automated processes using statistical and computational methods. This paper presents a comprehensive investigation on financial fraud detection practices using such data mining methods, with a particular focus on computational intelligence-based techniques. Classification of the practices based on key aspects such as detection algorithm used, fraud type investigated, and success rate have been covered. Issues and challenges associated with the current practices and potential future direction of research have also been identified.Comment: Proceedings of the 10th International Conference on Security and Privacy in Communication Networks (SecureComm 2014

HIPAD - A Hybrid Interior-Point Alternating Direction algorithm for knowledge-based SVM and feature selection

We consider classification tasks in the regime of scarce labeled training data in high dimensional feature space, where specific expert knowledge is also available. We propose a new hybrid optimization algorithm that solves the elastic-net support vector machine (SVM) through an alternating direction method of multipliers in the first phase, followed by an interior-point method for the classical SVM in the second phase. Both SVM formulations are adapted to knowledge incorporation. Our proposed algorithm addresses the challenges of automatic feature selection, high optimization accuracy, and algorithmic flexibility for taking advantage of prior knowledge. We demonstrate the effectiveness and efficiency of our algorithm and compare it with existing methods on a collection of synthetic and real-world data.Comment: Proceedings of 8th Learning and Intelligent OptimizatioN (LION8) Conference, 201

StackInsights: Cognitive Learning for Hybrid Cloud Readiness

Hybrid cloud is an integrated cloud computing environment utilizing a mix of public cloud, private cloud, and on-premise traditional IT infrastructures. Workload awareness, defined as a detailed full range understanding of each individual workload, is essential in implementing the hybrid cloud. While it is critical to perform an accurate analysis to determine which workloads are appropriate for on-premise deployment versus which workloads can be migrated to a cloud off-premise, the assessment is mainly performed by rule or policy based approaches. In this paper, we introduce StackInsights, a novel cognitive system to automatically analyze and predict the cloud readiness of workloads for an enterprise. Our system harnesses the critical metrics across the entire stack: 1) infrastructure metrics, 2) data relevance metrics, and 3) application taxonomy, to identify workloads that have characteristics of a) low sensitivity with respect to business security, criticality and compliance, and b) low response time requirements and access patterns. Since the capture of the data relevance metrics involves an intrusive and in-depth scanning of the content of storage objects, a machine learning model is applied to perform the business relevance classification by learning from the meta level metrics harnessed across stack. In contrast to traditional methods, StackInsights significantly reduces the total time for hybrid cloud readiness assessment by orders of magnitude

Classification of Encouragement (Targhib) And Warning (Tarhib) Using Sentiment Analysis on Classical Arabic

The Holy Qur’an is the main religious text of Islam. The Qur’an has its own methods of Targhib (encouragement) and Tarhib (warning), which are important features of the Qur’an. Most of the Quranic verses would urge and encourage people to do right and good deeds, and also warn them from committing evil and bad deeds. The method of classifying a text into two opposing opinions has been applied previously in solving the problem of sentiment analysis. Currently, it is applied in identifying between Targhib (encouragement) and Tarhib (warning) verses in the Qur’an. Each verse of the Qur’an can be treated as either an encouragement, warning or neutral. The language of the Holy Qur’an is one of the most challenging natural languages in sentiment analysis.  The aim of this work is to classify the verses of encouragement and warning using sentiment analysis and NLP techniques. Several approaches are used in the Sentiment Analysis classification, such as the machine learning approach, the lexicon-based approach and the hybrid approach. In carrying out this aim, the applied machine learning approach was used, where the impact of the use of different techniques such as POS tagging, N-Gram and Feature selection with correlation based were evaluated and investigated. 95.6% accuracy was achieved using Naïve Bayes (NB) and 91.5% accuracy was achieved using the Support Vector Machines (SVM). This study is a significant study in extracting information and knowledge from the Holy Qur’an. It is significant for both researchers in the field of Islamic studies as well as non-specialized researchers