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

A comparison of fuzzy approaches to e-commerce review rating prediction

This paper presents a comparative analysis of the performance of fuzzy approaches on the task of predicting customer review ratings using a computational intelligence framework based on a genetic algorithm for data dimensionality reduction. The performance of the Fuzzy C-Means (FCM), a neurofuzzy approach combining FCM and the Adaptive Neuro Fuzzy Inference System (ANFIS), and the Simplified Fuzzy ARTMAP (SFAM) was compared on six datasets containing customer reviews. The results revealed that all computational intelligence predictors were suitable for the rating prediction problem, and that the genetic algorithm is effective in reducing the number of dimensions without affecting the prediction performance of each computational intelligence predictor

Adaptive Data Mining Approach for Pcb Defect Detection and Classification

Objective: To develop a model for PCB defect detection and classification with the help of soft computing technique. Methodology: To improve the performance of the prediction and classification we propose a hybrid approach for feature reduction and classification. The proposed approach is divided into three main stages: (i) data pre-processing (ii) feature selection and reduction and (iii) Classification. In this approach, pre-processing, feature selection and reduction is carried out by measuring of confidence with the adaptive genetic algorithm. Prediction and classification is carried out by using neural network classifier. A genetic algorithm is used for data preprocessing to achieve the feature reduction and confidence measurement. Findings: The system is implemented using MatLab 2013b. The resulting analysis shows that the proposed approach is capable of detecting and classifying defects in PCB board

Data mining in manufacturing: a review based on the kind of knowledge

In modern manufacturing environments, vast amounts of data are collected in database management systems and data warehouses from all involved areas, including product and process design, assembly, materials planning, quality control, scheduling, maintenance, fault detection etc. Data mining has emerged as an important tool for knowledge acquisition from the manufacturing databases. This paper reviews the literature dealing with knowledge discovery and data mining applications in the broad domain of manufacturing with a special emphasis on the type of functions to be performed on the data. The major data mining functions to be performed include characterization and description, association, classification, prediction, clustering and evolution analysis. The papers reviewed have therefore been categorized in these five categories. It has been shown that there is a rapid growth in the application of data mining in the context of manufacturing processes and enterprises in the last 3 years. This review reveals the progressive applications and existing gaps identified in the context of data mining in manufacturing. A novel text mining approach has also been used on the abstracts and keywords of 150 papers to identify the research gaps and find the linkages between knowledge area, knowledge type and the applied data mining tools and techniques

Brain image clustering by wavelet energy and CBSSO optimization algorithm

Previously, the diagnosis of brain abnormality was significantly important in the saving of social and hospital resources. Wavelet energy is known as an effective feature detection which has great efficiency in different utilities. This paper suggests a new method based on wavelet energy to automatically classify magnetic resonance imaging (MRI) brain images into two groups (normal and abnormal), utilizing support vector machine (SVM) classification based on chaotic binary shark smell optimization (CBSSO) to optimize the SVM weights. The results of the suggested CBSSO-based KSVM are compared favorably to several other methods in terms of better sensitivity and authenticity. The proposed CAD system can additionally be utilized to categorize the images with various pathological conditions, types, and illness modes