Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: A Time Attention based Fraud Transaction Detection Framework   With online payment platforms being ubiquitous and important, fraud
transaction detection has become the key for such platforms, to ensure user
account safety and platform security. In this work, we present a novel method
for detecting fraud transactions by leveraging patterns from both users' static
profiles and users' dynamic behaviors in a unified framework. To address and
explore the information of users' behaviors in continuous time spaces, we
propose to use \emph{time attention based recurrent layers} to embed the
detailed information of the time interval, such as the durations of specific
actions, time differences between different actions and sequential behavior
patterns,etc., in the same latent space. We further combine the learned
embeddings and users' static profiles altogether in a unified framework.
Extensive experiments validate the effectiveness of our proposed methods over
state-of-the-art methods on various evaluation metrics, especially on
\emph{recall at top percent} which is an important metric for measuring the
balance between service experiences and risk of potential losses.

4
The categories of this paper are: cond-mat
25
The categories of this paper are: patt-sol
7
The categories of this paper are: hep-th
15
The categories of this paper are: nucl-ex
36
The categories of this paper are: cmp-lg
22
The categories of this paper are: chao-dyn
20
The categories of this paper are: acc-phys
35
The categories of this paper are: mtrl-th
10
The categories of this paper are: q-bio
26
The categories of this paper are: bayes-an
28
The categories of this paper are: alg-geom
18
The categories of this paper are: econ
8
The categories of this paper are: hep-ex
3
The categories of this paper are: physics
34
The categories of this paper are: chem-ph
0
The categories of this paper are: hep-ph