Linking Bank Clients using Graph Neural Networks Powered by Rich Transactional Data

EWS-GCN: Edge Weight-Shared Graph Convolutional Network for Transactional Banking Data

In this paper, we discuss how modern deep learning approaches can be applied to the credit scoring of bank clients. We show that information about connections between clients based on money transfers between them allows us to significantly improve the quality of credit scoring compared to the approaches using information about the target client solely. As a final solution, we develop a new graph neural network model EWS-GCN that combines ideas of graph convolutional and recurrent neural networks via attention mechanism. The resulting model allows for robust training and efficient processing of…

Are Graph Representation Learning Methods Robust to Graph Sparsity and Asymmetric Node Information?

The growing popularity of Graph Representation Learning (GRL) methods has resulted in the development of a large number of models applied to a miscellany of domains. Behind this diversity of domains, there is a strong heterogeneity of graphs, making it difficult to estimate the expected performance of a model on a new graph, especially when the graph has distinctive characteristics that have not been encountered in the benchmark yet. To address this, we have developed an experimental pipeline, to assess the impact of a given property on the models performances. In this paper, we use this pipel…

Supporting Financial Inclusion with Graph Machine Learning and Super-App Alternative Data

The presence of Super-Apps have changed the way we think about the interactions between users and commerce. It then comes as no surprise that it is also redefining the way banking is done. The paper investigates how different interactions between users within a Super-App provide a new source of information to predict borrower behavior. To this end, two experiments with different graph-based methodologies are proposed, the first uses graph based features as input in a classification model and the second uses graph neural networks. Our results show that variables of centrality, behavior of neigh…

反洗钱算法中检测不公平性的反事实方法

Counterfactual Methods for Detecting Unfairness in Anti-Money Laundering Algorithms

研究利用反事实方法，通过合成数据集构建账户国家及平均行为等特征，提升多种机器学习模型预测性能，并通过反事实路径特定效应分析评估潜在不公平性，揭示预测准确性与公平性的权衡。

Learning Filter Bank Sparsifying Transforms

Data is said to follow the transform (or analysis) sparsity model if it becomes sparse when acted on by a linear operator called a sparsifying transform. Several algorithms have been designed to learn such a transform directly from data, and data-adaptive sparsifying transforms have demonstrated excellent performance in signal restoration tasks. Sparsifying transforms are typically learned using small sub-regions of data called patches, but these algorithms often ignore redundant information shared between neighboring patches. We show that many existing transform and analysis sparse representa…