3,442 research outputs found
HitFraud: A Broad Learning Approach for Collective Fraud Detection in Heterogeneous Information Networks
On electronic game platforms, different payment transactions have different
levels of risk. Risk is generally higher for digital goods in e-commerce.
However, it differs based on product and its popularity, the offer type
(packaged game, virtual currency to a game or subscription service), storefront
and geography. Existing fraud policies and models make decisions independently
for each transaction based on transaction attributes, payment velocities, user
characteristics, and other relevant information. However, suspicious
transactions may still evade detection and hence we propose a broad learning
approach leveraging a graph based perspective to uncover relationships among
suspicious transactions, i.e., inter-transaction dependency. Our focus is to
detect suspicious transactions by capturing common fraudulent behaviors that
would not be considered suspicious when being considered in isolation. In this
paper, we present HitFraud that leverages heterogeneous information networks
for collective fraud detection by exploring correlated and fast evolving
fraudulent behaviors. First, a heterogeneous information network is designed to
link entities of interest in the transaction database via different semantics.
Then, graph based features are efficiently discovered from the network
exploiting the concept of meta-paths, and decisions on frauds are made
collectively on test instances. Experiments on real-world payment transaction
data from Electronic Arts demonstrate that the prediction performance is
effectively boosted by HitFraud with fast convergence where the computation of
meta-path based features is largely optimized. Notably, recall can be improved
up to 7.93% and F-score 4.62% compared to baselines.Comment: ICDM 201
Occupational Fraud Detection Through Visualization
Occupational fraud affects many companies worldwide causing them economic
loss and liability issues towards their customers and other involved entities.
Detecting internal fraud in a company requires significant effort and,
unfortunately cannot be entirely prevented. The internal auditors have to
process a huge amount of data produced by diverse systems, which are in most
cases in textual form, with little automated support. In this paper, we exploit
the advantages of information visualization and present a system that aims to
detect occupational fraud in systems which involve a pair of entities (e.g., an
employee and a client) and periodic activity. The main visualization is based
on a spiral system on which the events are drawn appropriately according to
their time-stamp. Suspicious events are considered those which appear along the
same radius or on close radii of the spiral. Before producing the
visualization, the system ranks both involved entities according to the
specifications of the internal auditor and generates a video file of the
activity such that events with strong evidence of fraud appear first in the
video. The system is also equipped with several different visualizations and
mechanisms in order to meet the requirements of an internal fraud detection
system
TaxThemis: Interactive mining and exploration of suspicious tax evasion group
Tax evasion is a serious economic problem for many countries, as it can
undermine the government' s tax system and lead to an unfair business
competition environment. Recent research has applied data analytics techniques
to analyze and detect tax evasion behaviors of individual taxpayers. However,
they failed to support the analysis and exploration of the uprising related
party transaction tax evasion (RPTTE) behaviors (e.g., transfer pricing), where
a group of taxpayers is involved. In this paper, we present TaxThemis, an
interactive visual analytics system to help tax officers mine and explore
suspicious tax evasion groups through analyzing heterogeneous tax-related data.
A taxpayer network is constructed and fused with the trade network to detect
suspicious RPTTE groups. Rich visualizations are designed to facilitate the
exploration and investigation of suspicious transactions between related
taxpayers with profit and topological data analysis. Specifically, we propose a
calendar heatmap with a carefully-designed encoding scheme to intuitively show
the evidence of transferring revenue through related party transactions. We
demonstrate the usefulness and effectiveness of TaxThemis through two case
studies on real-world tax-related data, and interviews with domain experts.Comment: 11 pages, 7 figure
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