Related papers: Explainable Deep Behavioral Sequence Clustering fo…

As financial services (FS) companies have experienced drastic technology driven changes, the availability of new data streams provides the opportunity for more comprehensive customer understanding. We propose Dynamic Customer Embeddings…

Credit card fraud causes significant financial losses and frequently occurs as fraud attack, defined as short-term sequence of fraudulent transactions associated with high transaction rates and amounts, business areas historically tied to…

Financial fraud cases are on the rise even with the current technological advancements. Due to the lack of inter-organization synergy and because of privacy concerns, authentic financial transaction data is rarely available. On the other…

Modern mobile devices are able to provide context-aware and personalized services to the users, by leveraging on their sensing capabilities to infer the activity and situation in which a person is currently involved. Current solutions for…

Clustering Transactions in sequence, temporal and time series databases is achieving an important attention from the database researchers and software industry. Significant research is carried out towards defining and validating the…

This paper presents a deep neural-network-based hierarchical graphical model for individual and group activity recognition in surveillance scenes. Deep networks are used to recognize the actions of individual people in a scene. Next, a…

In the last decade, the use of fast flux technique has become established as a common practice to organise botnets in Fast Flux Service Networks (FFSNs), which are platforms able to sustain illegal online services with very high…

In this paper we address the problem of human action recognition from video sequences. Inspired by the exemplary results obtained via automatic feature learning and deep learning approaches in computer vision, we focus our attention towards…

Due to the popularity of the Internet and smart mobile devices, more and more financial transactions and activities have been digitalized. Compared to traditional financial fraud detection strategies using credit-related features, customers…

Transaction flow networks are crucial in detecting illicit activities such as wash trading, credit card fraud, cashback arbitrage fraud, and money laundering. \revise{Our collaborator, Grab, a leader in digital payments in Southeast Asia,…

As the financial industry becomes more interconnected and reliant on digital systems, fraud detection systems must evolve to meet growing threats. Cloud-enabled Transformer models present a transformative opportunity to address these…

Over the past few years, there has been a growth in activity, public knowledge, and awareness of cryptocurrencies and related blockchain technology. As the industry has grown, there has also been an increase in scams looking to steal…

Analyzing and finding anomalies in multi-dimensional datasets is a cumbersome but vital task across different domains. In the context of financial fraud detection, analysts must quickly identify suspicious activity among transactional data.…

Imbalanced datasets pose a difficulty in fraud detection, as classifiers are often biased toward the majority class and perform poorly on rare fraudulent transactions. Synthetic data generation is therefore commonly used to mitigate this…

The need to increase accuracy in detecting sophisticated cyber attacks poses a great challenge not only to the research community but also to corporations. So far, many approaches have been proposed to cope with this threat. Among them,…

In this paper, we present an automated feature engineering based approach to dramatically reduce false positives in fraud prediction. False positives plague the fraud prediction industry. It is estimated that only 1 in 5 declared as fraud…

Clustering is crucial for many computer vision applications such as robust tracking, object detection and segmentation. This work presents a real-time clustering technique that takes advantage of the unique properties of event-based vision…

How can we track synchronized behavior in a stream of time-stamped tuples, such as mobile devices installing and uninstalling applications in the lockstep, to boost their ranks in the app store? We model such tuples as entries in a…

In the faceless world of the Internet,online fraud is one of the greatest reasons of loss for web merchants.Advanced solutions are needed to protect e businesses from the constant problems of fraud.Many popular fraud detection algorithms…

Fraud detection systems (FDS) mainly perform two tasks: (i) real-time detection while the payment is being processed and (ii) posterior detection to block the card retrospectively and avoid further frauds. Since human verification is often…