2,725 research outputs found
Advanced analytical methods for fraud detection: a systematic literature review
The developments of the digital era demand new ways of producing goods and rendering
services. This fast-paced evolution in the companies implies a new approach from the
auditors, who must keep up with the constant transformation. With the dynamic
dimensions of data, it is important to seize the opportunity to add value to the companies.
The need to apply more robust methods to detect fraud is evident.
In this thesis the use of advanced analytical methods for fraud detection will be
investigated, through the analysis of the existent literature on this topic.
Both a systematic review of the literature and a bibliometric approach will be applied to
the most appropriate database to measure the scientific production and current trends.
This study intends to contribute to the academic research that have been conducted, in
order to centralize the existing information on this topic
The Jurisprudence of Willfulness: An Evolving Theory of Excusable Ignorance
Ignorantia legis non excusat-ignorance of the law does not excuse-is a centuries-old criminal law maxim familiar to lawyer and layperson alike. Under the doctrine, an accused finds little protection in the claim But, I did not know the law, for all are presumed either to be familiar with the law\u27s commands or to proceed in ignorance at their own peril. The ignorant must be punished along with the knowing, the maxim teaches, to achieve a better educated and more law-abiding populace and to avoid the easy-to-assert and difficult-to-dispute claim of ignorance that would otherwise flow from the lips of any person facing criminal punishment. Despite this country\u27s long-standing allegiance to the hoary maxim, over the last century, and in particular over the last decade, the courts have seriously eroded the ignorantia legis principle by frequently construing the mens rea term willfully to require proof of an accused\u27s knowledge of the law. The erosive effect that these constructions have had on the ignorantia legis maxim is referred to in this Article as the jurisprudence of willfulness. Professor Davies demonstrates that, contrary to the maxim, the number of federal criminal statutes that have been construed to impose such a heightened mens rea requirement is already quite large. The Article reveals that, if the courts continue to employ their current interpretive approach to the term willfully, at least 160 additional federal statutes containing the term are at risk of similar treatment. The author argues that contemporary constructions of the troublesome scienter term to impose a knowledge of the law element have been grounded on doubtful, unchallenged logic and have bequeathed a legacy of grave interpretive confusion. Professor Davies maintains that much of the jurisprudence of willfulness is inimical to congressional judgments and, therefore, violative of rule of law and separation of powers principles. The Article urges a return to the ignorantia legis principle in all cases in which a clear legislative intent to abandon the maxim when employing the term willfully is missing
GraphFC: Customs Fraud Detection with Label Scarcity
Custom officials across the world encounter huge volumes of transactions.
With increased connectivity and globalization, the customs transactions
continue to grow every year. Associated with customs transactions is the
customs fraud - the intentional manipulation of goods declarations to avoid the
taxes and duties. With limited manpower, the custom offices can only undertake
manual inspection of a limited number of declarations. This necessitates the
need for automating the customs fraud detection by machine learning (ML)
techniques. Due the limited manual inspection for labeling the new-incoming
declarations, the ML approach should have robust performance subject to the
scarcity of labeled data. However, current approaches for customs fraud
detection are not well suited and designed for this real-world setting. In this
work, we propose ( neural networks for
ustoms raud), a model-agnostic, domain-specific,
semi-supervised graph neural network based customs fraud detection algorithm
that has strong semi-supervised and inductive capabilities. With upto 252%
relative increase in recall over the present state-of-the-art, extensive
experimentation on real customs data from customs administrations of three
different countries demonstrate that GraphFC consistently outperforms various
baselines and the present state-of-art by a large margin
Journey of Artificial Intelligence Frontier: A Comprehensive Overview
The field of Artificial Intelligence AI is a transformational force with limitless promise in the age of fast technological growth This paper sets out on a thorough tour through the frontiers of AI providing a detailed understanding of its complex environment Starting with a historical context followed by the development of AI seeing its beginnings and growth On this journey fundamental ideas are explored looking at things like Machine Learning Neural Networks and Natural Language Processing Taking center stage are ethical issues and societal repercussions emphasising the significance of responsible AI application This voyage comes to a close by looking ahead to AI s potential for human-AI collaboration ground-breaking discoveries and the difficult obstacles that lie ahead This provides with a well-informed view on AI s past present and the unexplored regions it promises to explore by thoroughly navigating this terrai
Detecting disturbances in supply chains: the case of capacity constraints
Purpose – The ability to detect disturbances quickly as they arise in a supply chain helps to manage them efficiently and effectively. This paper is aimed at demonstrating the feasibility of automatically, and therefore quickly detecting a specific disturbance, which is constrained capacity at a supply chain echelon.
Design/Methodology/approach – Different supply chain echelons of a simulated four echelon supply chain were individually capacity constrained to assess their impacts on the profiles of system variables, and to develop a signature that related the profiles to the echelon location of the capacity constraint. A review of disturbance detection techniques across various domains formed the basis for considering the signature based technique.
Findings – The signature for detecting a capacity constrained echelon was found to be based on cluster profiles of shipping and net inventory variables for that echelon as well as other echelons in a supply chain, where the variables are represented as spectra.
Originality/value– Detection of disturbances in a supply chain including that of constrained capacity at an echelon has seen limited research where this study makes a contribution
Blending big data analytics : review on challenges and a recent study
With the collection of massive amounts of data every day, big data analytics has emerged as an important trend for many organizations. These collected data can contain important information that may be key to solving wide-ranging problems, such as cyber security, marketing, healthcare, and fraud. To analyze their large volumes of data for business analyses and decisions, large companies, such as Facebook and Google, adopt analytics. Such analyses and decisions impact existing and future technology. In this paper, we explore how big data analytics is utilized as a technique for solving problems of complex and unstructured data using such technologies as Hadoop, Spark, and MapReduce. We also discuss the data challenges introduced by big data according to the literature, including its six V's. Moreover, we investigate case studies of big data analytics on various techniques of such analytics, namely, text, voice, video, and network analytics. We conclude that big data analytics can bring positive changes in many fields, such as education, military, healthcare, politics, business, agriculture, banking, and marketing, in the future. © 2013 IEEE
- …