scholarly journals Financial Crime & Fraud Detection Using Graph Computing: Application Considerations & Outlook

2020 ◽  
Author(s):  
Eren Kurshan ◽  
Hongda Shen ◽  
Haojie Yu
Keyword(s):  
Fraud Detection ◽  
Financial Crime ◽  
Graph Computing

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.

Concealment of Illegally Obtained Assets in Nigeria: Revisiting the Role of the Churches in Money Laundering

2020 ◽  
Vol 28 (1) ◽  
pp. 106-121
Author(s):  
Kato Gogo Kingston
Keyword(s):  
Economic Growth ◽  
Money Laundering ◽  
Financial Crime ◽  
The Past ◽  
Key Drivers ◽  
Nigerian Government

Financial crime in Nigeria – including money laundering – is ravaging Nigeria's economic growth. In the past few years, the Nigerian government has made efforts to tackle money laundering by enacting laws and setting up several agencies to enforce the laws. However, there are substantial loopholes in the regulatory and enforcement regimes. This article seeks to unravel the involvement of the churches as key drivers in money laundering crimes in Nigeria. It concludes that the permissive secrecy which enables churches to conceal the names of their financiers and donors breeds criminality on an unimaginable scale.

Corporate Fraud Prevention and Detection: Revisiting the Literature

2015 ◽  
Vol 4 (1) ◽  
Author(s):  
Deepa Mangala ◽  
Pooja Kumari
Keyword(s):  
Fraud Detection ◽  
Primary Objective ◽  
Future Research ◽  
Business Organization ◽  
Corporate Fraud ◽  
Top Executives ◽  
Red Flag ◽  
Worldwide Phenomenon ◽  
Research Gap ◽  
Prevention Methods

Fraud has become a worldwide phenomenon and prime issue of concern. It dwells in all countries and affects all types of organizations irrespective of their size, profitability or industry. The primary objective of this paper is to provide an in-depth understanding of literature related to corporate fraud in order to understand why fraud occurs and how to combat it. Research studies published during the period commencing from the year 1984 to 2014 have been reviewed. The study aims to provide an in-depth discussion on significant red flags that may exist before fraud occurrence. It, also, provides a comprehensive view about fraud detection and prevention methods. Findings reveal that red flag is an important mechanism to prevent fraud. Application of single fraud detection technique will not curb the fraud effectively. Also, the top executives were found to be responsible for implementing anti-fraud policies and techniques within business organization. Further, the present study tries to discern the research gap in existing literature and explore the area of future research.

Fraud Detection Protocol for Web-Based Research Among Men Who Have Sex With Men: Development and Descriptive Evaluation (Preprint)

2018 ◽  
Author(s):  
April M Ballard ◽  
Trey Cardwell ◽  
April M Young
Keyword(s):  
Survey Research ◽  
Hiv Transmission ◽  
Personal Information ◽  
Fraud Detection ◽  
Detection Algorithm ◽  
Cross Sectional ◽  
Web Based ◽  
Young Msm ◽  
Data Survey ◽  
Sex With Men

BACKGROUND Internet is becoming an increasingly common tool for survey research, particularly among “hidden” or vulnerable populations, such as men who have sex with men (MSM). Web-based research has many advantages for participants and researchers, but fraud can present a significant threat to data integrity. OBJECTIVE The purpose of this analysis was to evaluate fraud detection strategies in a Web-based survey of young MSM and describe new protocols to improve fraud detection in Web-based survey research. METHODS This study involved a cross-sectional Web-based survey that examined individual- and network-level risk factors for HIV transmission and substance use among young MSM residing in 15 counties in Central Kentucky. Each survey entry, which was at least 50% complete, was evaluated by the study staff for fraud using an algorithm involving 8 criteria based on a combination of geolocation data, survey data, and personal information. Entries were classified as fraudulent, potentially fraudulent, or valid. Descriptive analyses were performed to describe each fraud detection criterion among entries. RESULTS Of the 414 survey entries, the final categorization resulted in 119 (28.7%) entries identified as fraud, 42 (10.1%) as potential fraud, and 253 (61.1%) as valid. Geolocation outside of the study area (164/414, 39.6%) was the most frequently violated criterion. However, 33.3% (82/246) of the entries that had ineligible geolocations belonged to participants who were in eligible locations (as verified by their request to mail payment to an address within the study area or participation at a local event). The second most frequently violated criterion was an invalid phone number (94/414, 22.7%), followed by mismatching names within an entry (43/414, 10.4%) and unusual email addresses (37/414, 8.9%). Less than 5% (18/414) of the entries had some combination of personal information items matching that of a previous entry. CONCLUSIONS This study suggests that researchers conducting Web-based surveys of MSM should be vigilant about the potential for fraud. Researchers should have a fraud detection algorithm in place prior to data collection and should not rely on the Internet Protocol (IP) address or geolocation alone, but should rather use a combination of indicators.

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