Classification of Financial Transactions using Lightweight Memory Networks

Sammanfattning

Various forms of fraud have substantially impacted our lives and caused considerable losses to some people. To reduce these losses, many researchers have devoted themselves to the study of fraud detection. After the development of fraud detection from expert-driven to data-driven systems, the scalability and accuracy of fraud detection have been improved considerably. However, most existing fraud detection methods focus on the feature extraction and classification of a certain transaction, ignoring the temporal and spatial long-term information from accounts. In this work, we propose to address these limitations by employing a lightweight memory network (LiMNet), which is a deep neural network that captures causal relations between temporal interactions. We evaluate our approach on two data sets, the Ether-Fraud dataset, and the Elliptic dataset. The former is a brand new dataset collected from Etherscan with data mining, and the latter is published by the homonymous company. As a set of raw collected data never used before, the Ether-Fraud dataset had some issues, such as huge variation among values and incomplete information. Therefore we have processed Ether-Fraud with data supplementation and normalization, which has solved these problems. A series of experiments were designed based on our analysis of the model and helped us to find the best hyper-parameter setting. Then, we compared the performance of the model with other baselines, and the results showed that Lightweight Memory Network (LiMNet) outperformed traditional algorithms on the Ether-Fraud dataset but was not good as the graph-based method on the Elliptic dataset. Finally, we summarized the experience of applying the model to fraud detection, the strengths and weaknesses of the model, and future directions for improvement.