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

Overall Memory Impairment Identification with Mathematical Modeling of the CVLT-II Learning Curve in Multiple Sclerosis

The CVLT-II provides standardized scores for each of the List A five learning trials, so that the clinician can compare the patient's raw trials 1–5 scores with standardized ones. However, frequently, a patient's raw scores fluctuate making a proper interpretation difficult. The CVLT-II does not offer any other methods for classifying a patient's learning and memory status on the background of the learning curve. The main objective of this research is to illustrate that discriminant analysis provides an accurate assessment of the learning curve, if suitable predictor variables are selected. Normal controls were ninety-eight healthy volunteers (78 females and 20 males). A group of MS patients included 365 patients (266 females and 99 males) with clinically defined multiple sclerosis. We show that the best predictor variables are coefficients B3 and B4 of our mathematical model B3 ∗ exp(−B2  ∗  (X − 1)) + B4  ∗  (1 − exp(−B2  ∗  (X − 1))) because discriminant functions, calculated separately for B3 and B4, allow nearly 100% correct classification. These predictors allow identification of separate impairment of readiness to learn or ability to learn, or both

Visual-cognitive processing deficits in pediatric multiple sclerosis

Background: Children with multiple sclerosis (MS) can suffer significant cognitive deficits. This study investigates the sensitivity and validity in pediatric MS of two visual processing tests borrowed from the adult literature, the Brief Visuospatial Memory Test-Revised (BVMTR) and the Symbol Digit Modalities Test (SDMT). Objective: To test the hypothesis that visual processing is disproportionately impacted in pediatric MS by comparing performance with that of healthy controls on the BVMTR and SDMT. Methods: We studied 88 participants (43 MS, 45 controls) using a neuropsychological assessment battery including measures of intelligence, language, visual memory, and processing speed. Patients and demographically matched controls were compared to determine which tests are most sensitive in pediatric MS. Results: Statistically significant differences were found between the MS and control groups on BVMTR Total Learning ( t (84) = 4.04, p &lt; 0.001, d = 0.87), BVMTR Delayed Recall ( t (84) = 4.45, p &lt; 0.001, d = 0.96), and SDMT ( t (38) = 2.19, p = 0.035, d = 0.69). No significant differences were found between groups on confrontation naming or general intellectual ability. Validity coefficients exploring correlation between BVMTR, SDMT, and disease characteristics were consistent with the adult literature. Conclusions: This study found that BVMTR and SDMT may be useful in assessing children and adolescents with MS. </jats:p

Video surveillance : past, present, and now the future

Video surveillance is a part of our daily life, even though we may not necessarily realize it. We might be monitored on the street, on highways, at ATMs, in public transportation vehicles, inside private and public buildings, in the elevators, in front of our television screens, next to our baby?s cribs, and any spot one can set a camera