Financial Behavior Analysis

Discovering patterns and estimating future behaviors by mining financial behavioral data with time series-based and graph-based methods. 

 Pick and Choose: A GNN-based Imbalanced Learning Approach for Fraud Detection (WWW 2021) 

Yang Liu, Xiang Ao, Zidi Qin, Jianfeng Chi , Jinghua Feng, Hao Yang, Qing He

Mobirise

To remedy the class imbalance problem of graph-based fraud detection, we propose a Pick and Choose Graph Neural Network (PC-GNN for short) for imbalanced supervised learning on graphs. First, nodes and edges are picked with a devised label-balanced sampler to construct sub-graphs for mini-batch training. Next, for each node in the sub-graph, the neighbor candidates are chosen by a proposed neighborhood sampler. Finally, information from the selected neighbors and different relations are aggregated to obtain the final representation of a target node. Experiments on both benchmark and real-world graph-based fraud detection tasks demonstrate that PC-GNN apparently outperforms SOTA baselines. [Paper]

Fraud Transactions Detection via Behavior Tree with Local Intention Calibration (KDD 2020)

Can Liu, Qiwei Zhong, Xiang Ao, Li Sun, Wangli Lin, Jinghua Feng, Qing He, Jiayu Tang

Mobirise

In this paper, we devise a tree-like structure named behavior tree to reorganize the user behavioral data, in which a group of successive sequential actions denoting a specific user intention are represented as a branch on the tree. We then propose a novel neural method coined LIC Tree-LSTM (Local Intention Calibrated Tree-LSTM) to utilize the behavior tree for fraud transactions detection. We investigate the effectiveness of LIC Tree-LSTM on a real-world dataset of Alibaba platform, and the experimental results show that our proposed algorithm outperforms state-of-the-art methods in both offline and online modes. [Paper]

Financial Defaulter Detection on Online Credit Payment via Multi-view Attributed Heterogeneous Information Network (WWW 2020)

Qiwei Zhong, Yang Liu, Xiang Ao, Binbin Hu, Jinghua Feng, Jiayu Tang, Qing He

Mobirise

In this paper, we propose a multi-view attributed heterogeneous information network based approach coined MAHINDER for defaulter detection. First, multiple views of user behaviors are adopted to learn personal profile due to the endogenous aspect of financial default. Second, local behavioral patterns are specifically modeled since financial default is adversarial and accumulated. The experimental resuts on real-world datasets on Alibaba platform exhibit the proposed approach is able to improve AUC over 2.8% and Recall@Precision=0.1 over 13.1% compared with the state-of-the-art methods. [Paper]

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Last Modified in Apr 20th, 2021

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