# Sequential Behavioral Data Processing Using Deep Learning and the Markov Transition Field in Online Fraud Detection

arXiv: https://arxiv.org/abs/1808.05329 · HTML: https://mlsanity.com/paper/1808.05329/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Aug 2018.

## Built on by (most cited first, 5 of 5)

- [Modeling Users' Behavior Sequences with Hierarchical Explainable Network for Cross-domain Fraud Detection](https://mlsanity.com/paper/2201.01004/) (2022)
- [Explainable Deep Behavioral Sequence Clustering for Transaction Fraud Detection](https://mlsanity.com/paper/2101.04285/) (2021)
- [Behavioral graph fraud detection in E-commerce](https://mlsanity.com/paper/2210.06968/) (2022)
- [Behavior Structformer: Learning Players Representations with Structured Tokenization](https://mlsanity.com/paper/2406.05274/) (2024)
- [Multi-task CNN Behavioral Embedding Model For Transaction Fraud Detection](https://mlsanity.com/paper/2411.19457/) (2024)

## Abstract (arXiv)

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 are generating a large amount of unstructured behavioral data every second. In this paper, we propose an Recurrent Neural Netword (RNN) based deep-learning structure integrated with Markov Transition Field (MTF) for predicting online fraud behaviors using customer's interactions with websites or smart-phone apps as a series of states. In practice, we tested and proved that the proposed network structure for processing sequential behavioral data could significantly boost fraud predictive ability comparing with the multilayer perceptron network and distance based classifier with Dynamic Time Warping(DTW) as distance metric.