15,638 research outputs found
Spectrum-based deep neural networks for fraud detection
In this paper, we focus on fraud detection on a signed graph with only a
small set of labeled training data. We propose a novel framework that combines
deep neural networks and spectral graph analysis. In particular, we use the
node projection (called as spectral coordinate) in the low dimensional spectral
space of the graph's adjacency matrix as input of deep neural networks.
Spectral coordinates in the spectral space capture the most useful topology
information of the network. Due to the small dimension of spectral coordinates
(compared with the dimension of the adjacency matrix derived from a graph),
training deep neural networks becomes feasible. We develop and evaluate two
neural networks, deep autoencoder and convolutional neural network, in our
fraud detection framework. Experimental results on a real signed graph show
that our spectrum based deep neural networks are effective in fraud detection
Electronic fraud detection in the U.S. Medicaid Healthcare Program: lessons learned from other industries
It is estimated that between 850 billion annually is lost to fraud, waste, and abuse in the US healthcare system,with 175 billion of this due to fraudulent activity (Kelley 2009). Medicaid, a state-run, federally-matchedgovernment program which accounts for roughly one-quarter of all healthcare expenses in the US, has been particularlysusceptible targets for fraud in recent years. With escalating overall healthcare costs, payers, especially government-runprograms, must seek savings throughout the system to maintain reasonable quality of care standards. As such, the need foreffective fraud detection and prevention is critical. Electronic fraud detection systems are widely used in the insurance,telecommunications, and financial sectors. What lessons can be learned from these efforts and applied to improve frauddetection in the Medicaid health care program? In this paper, we conduct a systematic literature study to analyze theapplicability of existing electronic fraud detection techniques in similar industries to the US Medicaid program
SAFE: A Neural Survival Analysis Model for Fraud Early Detection
Many online platforms have deployed anti-fraud systems to detect and prevent
fraudulent activities. However, there is usually a gap between the time that a
user commits a fraudulent action and the time that the user is suspended by the
platform. How to detect fraudsters in time is a challenging problem. Most of
the existing approaches adopt classifiers to predict fraudsters given their
activity sequences along time. The main drawback of classification models is
that the prediction results between consecutive timestamps are often
inconsistent. In this paper, we propose a survival analysis based fraud early
detection model, SAFE, which maps dynamic user activities to survival
probabilities that are guaranteed to be monotonically decreasing along time.
SAFE adopts recurrent neural network (RNN) to handle user activity sequences
and directly outputs hazard values at each timestamp, and then, survival
probability derived from hazard values is deployed to achieve consistent
predictions. Because we only observe the user suspended time instead of the
fraudulent activity time in the training data, we revise the loss function of
the regular survival model to achieve fraud early detection. Experimental
results on two real world datasets demonstrate that SAFE outperforms both the
survival analysis model and recurrent neural network model alone as well as
state-of-the-art fraud early detection approaches.Comment: To appear in AAAI-201
Forging success : Soviet managers and accounting fraud, 1943 to 1962
Attempting to satisfy their political masters in a target-driven culture, Soviet managers had to
optimize on many margins simultaneously. One of these was the margin of truthfulness. False
accounting for the value of production was apparently widespread in some branches of the
economy and at some periods of time. A feature of accounting fraud was that cases commonly
involved the aggravating element of conspiracy. The paper provides new evidence on the nature
and extent of accounting fraud; the scale and optimal size of conspiratorial networks; the
authoritiesâ willingness to penalize it and the political and social factors that secured leniency;
and inefficiency in the socialist market where managers competed for political credit
- âŠ