scholarly journals Graph Computing for Financial Crime and Fraud Detection: Trends, Challenges and Outlook

2020 ◽  
Vol 14 (04) ◽  
pp. 565-589
Author(s):  
Eren Kurshan ◽  
Hongda Shen
Keyword(s):  
Fraud Detection ◽  
Transaction Processing ◽  
Financial Crime ◽  
Numerous Application ◽  
Detection Techniques ◽  
Significant Interest ◽  
Crime Detection ◽  
Digital Payments ◽  
Graph Computing ◽  
Transaction Processing Systems

The rise of digital payments has caused consequential changes in the financial crime landscape. As a result, traditional fraud detection approaches such as rule-based systems have largely become ineffective. Artificial intelligence (AI) and machine learning solutions using graph computing principles have gained significant interest in recent years. Graph-based techniques provide unique solution opportunities for financial crime detection. However, implementing such solutions at industrial-scale in real-time financial transaction processing systems has brought numerous application challenges to light. In this paper, we discuss the implementation difficulties current and next-generation graph solutions face. Furthermore, financial crime and digital payments trends indicate emerging challenges in the continued effectiveness of the detection techniques. We analyze the threat landscape and argue that it provides key insights for developing graph-based solutions.

scholarly journals An approach to benchmark fraud detection algorithms in the COVID-19 era

2021 ◽  
Author(s):  
Miroslawa Alunowska Figueroa ◽  
Daniel Turner-Szymkiewicz ◽  
Edgar Alonso Lopez-Rojas ◽  
Juan Sebastián Cárdenas-Rodriguez ◽  
Ulf Norinder
Keyword(s):  
Machine Learning ◽  
Data Analysis ◽  
Financial Institutions ◽  
Detection System ◽  
Synthetic Data ◽  
Fraud Detection ◽  
Financial Crime ◽  
Innocent People ◽  
Detection Algorithms ◽  
Digital Payments

To address the challenges in the fight against financial crime, particularly in the COVID-19 pandemic context, this paper focuses on financial synthetic data and the use of a reliable benchmark tool to test fraud detection algorithms. Compliance departments at financial institutions face the challenge of reducing the number of innocent people erroneously accused of fraud. To cope with this problem financial institutions are exploring the application of machine learning fraud detection algorithms and data analysis technologies to develop a more accurate and precise fraud detection system. However, approaches to streamlining and automating banks’ monitoring and testing processes is challenging as there is no consensus on a benchmark. We explore the relevance of measuring the applicability of a financial crime benchmark in the presence of a growing digital financial sector, such as in the case of Mexico. This study is particularly important due to serious threats that are faced by a rapidly developing financial system (2019 Mexican Central Bank Report). These risks have been further exacerbated as a result of the COVID-19 pandemic accelerating the shift towards digital payments.

scholarly journals Fraudulent credit card transaction detection using soft computing techniques

2021 ◽  
Vol 23 (3) ◽  
pp. 1634
Author(s):  
Aishwarya Priyadarshini ◽  
Sanhita Mishra ◽  
Debani Prasad Mishra ◽  
Surender Reddy Salkuti ◽  
Ramakanta Mohanty
Keyword(s):  
Soft Computing ◽  
Credit Card ◽  
Nearest Neighbor ◽  
Detection System ◽  
Fraud Detection ◽  
K Nearest Neighbor ◽  
Computationally Efficient ◽  
Detection Techniques ◽  
Government Organizations ◽  
Soft Computing Techniques

<p>Nowadays, fraudulent or deceitful activities associated with financial transactions, predominantly using credit cards have been increasing at an alarming rate and are one of the most prevalent activities in finance industries, corporate companies, and other government organizations. It is therefore essential to incorporate a fraud detection system that mainly consists of intelligent fraud detection techniques to keep in view the consumer and clients’ welfare alike. Numerous fraud detection procedures, techniques, and systems in literature have been implemented by employing a myriad of intelligent techniques including algorithms and frameworks to detect fraudulent and deceitful transactions. This paper initially analyses the data through exploratory data analysis and then proposes various classification models that are implemented using intelligent soft computing techniques to predictively classify fraudulent credit card transactions. Classification algorithms such as K-Nearest neighbor (K-NN), decision tree, random forest (RF), and logistic regression (LR) have been implemented to critically evaluate their performances. The proposed model is computationally efficient, light-weight and can be used for credit card fraudulent transaction detection with better accuracy.</p>

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.

Mobile Transaction Processing System

2017 ◽  
pp. 64-112
Keyword(s):  
Mobile Computing ◽  
Resource Constraints ◽  
Processing System ◽  
Database Systems ◽  
Transaction Processing ◽  
Distributed Environments ◽  
Mobile Transaction ◽  
Basic Concepts ◽  
Database Transactions ◽  
Transaction Processing Systems

In this chapter, we first revisit the basic concepts of database transactions, and discuss how these concepts are achieved in practical systems. Next, we briefly go through the architecture of transaction processing systems in the centralized and the distributed environments. This chapter we have reviewed the basic concepts of database systems and database transactions, and discussed the architecture of transaction processing systems in distributed environments. We will shift our focus to transactions and transaction processing in mobile environments, which possess some unique characteristics such as the mobility of mobile computing hosts, the limitations of wireless communications and the resource constraints of mobile computing devices.

Security Issues in Distributed Transaction Processing Systems

2008 ◽  
pp. 3572-3576
Author(s):  
R. A. Haraty
Keyword(s):  
Computer Network ◽  
Processing System ◽  
Data Replication ◽  
Transaction Processing ◽  
Substantial Effect ◽  
Security Issues ◽  
Distributed Transaction Processing ◽  
Distributed Transaction ◽  
Research Stage ◽  
Transaction Processing Systems

Transaction-processing systems (TPS) are becoming increasingly more available as commercial products. However, the approaches to the issues associated with using TPS in multilevel secure environments are still in the research stage. In this article, we address the issues of multilevel security in distributed transaction-processing systems. A distributed transaction-processing system (DTPS) is a collection of a finite number of centralized transaction-processing systems connected by a computer network. Each of these transaction-processing systems is controlled by a software layer and can be accessed both remotely and locally. Properties of a DTPS, such as data replication, may have a substantial effect on the security of the system. The security policies and integrity constraints adopted at each site may result in global security having inconsistent states. We address the issues of achieving a multilevel secure DTPS, and discuss the security constraints and data replication.

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