scholarly journals Heterogeneous Data Analysis in Intelligent Fraud Detection Systems

2019 ◽  
Vol 143 (2) ◽  
pp. 78-90 ◽  
Author(s):  
T.D. Polhul ◽  
◽  
A.A. Yarovyi ◽  
Keyword(s):  
Data Analysis ◽  
Fraud Detection ◽  
Heterogeneous Data ◽  
Detection Systems

Architecture of a distributed system for processing heterogeneous data from social networks

2020 ◽  
Vol 4 ◽  
pp. 97-100
Author(s):  
A.P. Pronichev ◽  
Keyword(s):  
Social Networks ◽  
Data Analysis ◽  
Distributed System ◽  
Heterogeneous Data ◽  
Analysis Process ◽  
Support Costs

The article discusses the architecture of a system for collecting and analyzing heterogeneous data from social networks. This architecture is a distributed system of subsystem modules, each of which is responsible for a separate task. The system also allows you to use external systems for data analysis, providing the necessary interface abstraction for connection. This allows for more flexible customization of the data analysis process and reduces development, implementation and support costs.

scholarly journals INTERNATIONAL CONFERENCE ON SPECTACULAR PROGRESSIVE IMPROVEMENTS IN ENGINEERING SCIENCES & COMPUTING (ICSPEC 2021)INTERNATIONAL CONFERENCE ON SPECTACULAR PROGRESSIVE IMPROVEMENTS IN ENGINEERING SCIENCES & COMPUTING (ICSPEC 2021)

2021 ◽  
Author(s):  
Dr. Sharath Chandra I ◽  
Dr. Srikanth N ◽  
Dr. Senthil Kumar S k ◽  
Dr. Venkatessulu S ◽  
Dr. Anjaiah A ◽  
...  
Keyword(s):  
Credit Card ◽  
Fraud Detection ◽  
Credit Card Fraud ◽  
International Conference ◽  
Detection Systems ◽  
Online Purchases ◽  
Engineering Sciences ◽  
Business Establishment ◽  
Substantial Rise

The exceptional growth in the number of credit card transactions, especially for online purchases, has recently led to a substantial rise in fraudulent activities. Credit card security is a major concern for any business establishment. With that in mind, it is hard to identify the credit card fraud. Implementation of efficient fraud detection systems has thus become imperative for all credit card issuing banks to minimize their losses.

Parameters of automated fraud detection techniques during online transactions

2018 ◽  
Vol 25 (3) ◽  
pp. 702-720 ◽  
Author(s):  
Vipin Khattri ◽  
Deepak Kumar Singh
Keyword(s):  
Detection System ◽  
Fraud Detection ◽  
Security System ◽  
Research Papers ◽  
Content Type ◽  
Detection Techniques ◽  
Online Transactions ◽  
Detection Systems ◽  
Different Types ◽  
Depth Study

Purpose This paper aims to provide information of parameters and techniques used in the automated fraud detection system during online transaction. With the increase in the use of online transactions, the concerns regarding data security have also increased. To tackle the frauds, lot of research has been done and plethora of papers are available on the related topics. The purpose of this paper is to provide the clear pathway for researchers to move in the direction of development of automated fraud detection system to prevent the fraud during online transaction. Design/methodology/approach This literature review analyses and compares the different types of techniques for detecting fraud during online transaction. An in-depth study of the most prominent journals has been done and the core methodology of the papers has been presented. This article also shed some light on different types of parameters used in fraud detection techniques during online transaction. Findings There are vast varieties of various fraud detection techniques, and every technique has completed task in its own way. After studying approximately 41 research papers, 14 books and four reports, in total 30 parameters have been identified and a detailed study of the parameters has been presented. The parameters are also listed with their details that how these parameters are used in the security system for detecting online transaction fraud. Research limitations/implications This paper provides empirical insight about the parameters and their prominence in the development of automated fraud detection security system of online transaction. This paper encourages the researchers to development of improved fraud detection system. Practical implications This paper will pave the way for researchers to do a focused research on the fraud detection methodologies. The analysis will help in zeroing down the most prevalent topic of research in this field. The researchers will be able to understand the internal details of parameters and techniques used in the fraud detection systems. This literature also helps the research to think in a variety of ways that how these parameters will be used in the development of fraud detection system. Originality/value This paper is one of the most comprehensive reviews in its field. It tries and attempts to fill a void created because of lack of compilation of the laid fraud detection parameters.

Big Data Analytics for Tourism Destinations

2018 ◽  
pp. 349-363 ◽  
Author(s):  
Wolfram Höpken ◽  
Matthias Fuchs ◽  
Maria Lexhagen
Keyword(s):  
Data Analysis ◽  
Big Data Analytics ◽  
Heterogeneous Data ◽  
Machine Learning Techniques ◽  
Implicit Information ◽  
Heterogeneous Data Sources ◽  
Learning Techniques ◽  
Exploratory Data ◽  
Tourism Destinations ◽  
User Ratings

The objective of this chapter is to address the above deficiencies in tourism by presenting the concept of the tourism knowledge destination – a specific knowledge management architecture that supports value creation through enhanced supplier interaction and decision making. Information from heterogeneous data sources categorized into explicit feedback (e.g. tourist surveys, user ratings) and implicit information traces (navigation, transaction and tracking data) is extracted by applying semantic mapping, wrappers or text mining (Lau et al., 2005). Extracted data are stored in a central data warehouse enabling a destination-wide and all-stakeholder-encompassing data analysis approach. By using machine learning techniques interesting patterns are detected and knowledge is generated in the form of validated models (e.g. decision trees, neural networks, association rules, clustering models). These models, together with the underlying data (in the case of exploratory data analysis) are interactively visualized and made accessible to destination stakeholders.

PENGARUH WHISTLEBLOWING SYSTEM, KEMAMPUAN AUDITOR, SKEPTISME AUDITOR, DAN INDEPENDENSI TERHADAP PENDETEKSIAN KECURANGAN

2019 ◽  
Vol 2 (2) ◽  
pp. 39-48
Author(s):  
Nur Muchlisiah Utami Magister Akuntansi Pascasarjana, U
Keyword(s):  
Data Analysis ◽  
Fraud Detection ◽  
Primary Data ◽  
Professional Skepticism ◽  
South Sulawesi ◽  
Spss Software

This research was conducted by aiming to determine the effect ofwhistleblowing systems, auditor capabilities, auditor professional skepticism andindependence of fraud detection. This study uses primary data using a questionnaire of33 auditors as a sample of all auditor numbers as many as 48 auditors working at theInspectorate Office of South Sulawesi Province. Data analysis using multiple regressionwith the help of SPSS software. The results of this study indicate that: (1) Whistleblowingsystems have a positive and significant effect on fraud detection: (2) The auditor's abilityhas a positive and significant effect on fraud detection: (3) The auditor's professionalskepticism has a positive and significant effect on fraud detection: (4) Independencepositive and significant effect on fraud detection.

The Evolution of Fraud Intelligence

2011 ◽  
pp. 261-283
Author(s):  
David Porter
Keyword(s):  
Financial Institutions ◽  
Financial Services ◽  
Information Assurance ◽  
Fraud Detection ◽  
High Quality ◽  
Services Sector ◽  
Detection Systems ◽  
Financial Services Sector

This chapter discusses the latest innovations in fraud detection, with a particular focus on insider fraud and organized fraud. It argues that as fraud continues to grow at an alarming rate across the financial services sector, the constant evolution in fraudster behavior means that financial institutions need to keep their technology-based countermeasures constantly updated, particularly given the increasing involvement of serious organized criminals. In addition to upgrading their current operational detection systems, this chapter aims to encourage organizations to improve current levels of data and information assurance in order to ensure the generation of high quality intelligence on the enemy, and to adopt a structured framework for better understanding and describing exactly what we mean by “intelligence.”

scholarly journals Vertical and horizontal integration of multi-omics data with miodin

2019 ◽  
Vol 20 (1) ◽  
Author(s):  
Benjamin Ulfenborg
Keyword(s):  
Data Analysis ◽  
R Package ◽  
Heterogeneous Data ◽  
Omics Data ◽  
Technical Expertise ◽  
Horizontal Integration ◽  
Level Data ◽  
Genomics And Proteomics ◽  
Health And Disease ◽  
Molecular Layers

Abstract Background Studies on multiple modalities of omics data such as transcriptomics, genomics and proteomics are growing in popularity, since they allow us to investigate complex mechanisms across molecular layers. It is widely recognized that integrative omics analysis holds the promise to unlock novel and actionable biological insights into health and disease. Integration of multi-omics data remains challenging, however, and requires combination of several software tools and extensive technical expertise to account for the properties of heterogeneous data. Results This paper presents the miodin R package, which provides a streamlined workflow-based syntax for multi-omics data analysis. The package allows users to perform analysis of omics data either across experiments on the same samples (vertical integration), or across studies on the same variables (horizontal integration). Workflows have been designed to promote transparent data analysis and reduce the technical expertise required to perform low-level data import and processing. Conclusions The miodin package is implemented in R and is freely available for use and extension under the GPL-3 license. Package source, reference documentation and user manual are available at https://gitlab.com/algoromics/miodin.

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