As credit card becomes the most popular payment mode particularly in online sector, fraudu- lent activities using technologies are rapidly increasing as a result. The purpose of this work is to develop novel system for fraud detection based on sequential modeling data, attention mechanism Long Short Term Mem- ory(LSTM) deep Recurrent Neural Networks(RNN) and Synthetic Minority Oversampling Tech...

We propose a novel approach for credit card fraud detection, which combines evidences from current as well as past behavior. The fraud detection system (FDS) consists of four components, namely, rule-based filter, Dempster–Shafer adder, transaction history database and Bayesian learner. In the rule-based component, we determine the suspicion level of each incoming transaction based on the exten...

Many factors innuence a learning process and the performance of a learned classiier. In this paper we investigate the eeects of class distribution in the training set on performance. We also study diierent methods of measuring performance based on cost models and the performance eeects of training class distribution with respect to the diierent cost models. Observations from these eeects help u...

Many factors innuence a learning process and the performance of a learned classiier. In this paper we investigate the performance eeects of class distribution in the training set. We also study diierent methods of measuring performance based on cost models and the performance eeects of training class distribution with respect to the diierent cost models. Observations from these eeects help us d...

Now a day technology is increasing very rapidly, which can be used for good as well as for bad purposes. Ecommerce is part of all most every system where online transitions are performed through internet through which frauds can be easily done. Credit card system is most vulnerable for frauds. Hence it is very much essential to have fraud detecting system. Till date various approaches have been...

Neural networks have represented a serious barrier-to-entry in their application in automated fraud detection due to their black box and often proprietary nature which is overcome here by combining them with symbolic rule extraction. A Sparse Oracle-based Adaptive Rule extraction algorithm is used to produce comprehensible rules from a neural network to aid the detection of credit card fraud. I...

Credit card is a small plastic card issued by a bank, building society, etc., allowing the holder to purchase goods or services on credit. Debit card is a card allowing the holder to transfer money electronically from their bank account when making a purchase. The use of credit cards and debit cards are increasing day by day. People are relying more on both cards nowadays than in the previous d...

Adversaries achieved such intrusion via carefully crafted attacks of large magnitude that seek to wreak havoc on network infrastructures with a focus personal gains and rewards. Study proposes spectral-clustering hybrid genetic algorithm trained modular neural detect fraud in credit card transactions. The ensemble seeks equip credit-card users system whose knowledge will altruistically cards. R...

Insurance fraud costs the property and casualty insurance industry over 25 billion dollars (USD) annually. This paper addresses workers' compensation claim fraud. A data mining approach is adopted, and issues of data preparation are discussed. The focus is on building predictive models to score an open claim for a propensity to be fraudulent. A key component to modeling is the use of textual da...