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

• URL: http://arxiv.org/abs/2001.08427v1
• Date: Thu, 23 Jan 2020 10:02:02 GMT
• Title: Linking Bank Clients using Graph Neural Networks Powered by Rich Transactional Data
• Authors: Valentina Shumovskaia, Kirill Fedyanin, Ivan Sukharev, Dmitry Berestnev and Maxim Panov
• Abstract summary: We propose a new graph neural network model, which uses not only the topological structure of the network but rich time-series data available for the graph nodes and edges. The proposed model outperforms the existing approaches, with a significant gap in ROC AUC score on link prediction problem and also allows to improve the quality of credit scoring.
• Score: 2.1169216065483996
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Financial institutions obtain enormous amounts of data about user transactions and money transfers, which can be considered as a large graph dynamically changing in time. In this work, we focus on the task of predicting new interactions in the network of bank clients and treat it as a link prediction problem. We propose a new graph neural network model, which uses not only the topological structure of the network but rich time-series data available for the graph nodes and edges. We evaluate the developed method using the data provided by a large European bank for several years. The proposed model outperforms the existing approaches, including other neural network models, with a significant gap in ROC AUC score on link prediction problem and also allows to improve the quality of credit scoring.

Related papers

• Scalable Weibull Graph Attention Autoencoder for Modeling Document Networks [50.42343781348247]
  We develop a graph Poisson factor analysis (GPFA) which provides analytic conditional posteriors to improve the inference accuracy. We also extend GPFA to a multi-stochastic-layer version named graph Poisson gamma belief network (GPGBN) to capture the hierarchical document relationships at multiple semantic levels. Our models can extract high-quality hierarchical latent document representations and achieve promising performance on various graph analytic tasks.
  arXiv  Detail & Related papers  (2024-10-13T02:22:14Z)
• Applying Hybrid Graph Neural Networks to Strengthen Credit Risk Analysis [4.457653449326353]
  This paper presents a novel approach to credit risk prediction by employing Graph Convolutional Neural Networks (GCNNs) The proposed method addresses the challenges faced by traditional credit risk assessment models, particularly in handling imbalanced datasets. The study demonstrates the potential of GCNNs in improving the accuracy of credit risk prediction, offering a robust solution for financial institutions.
  arXiv  Detail & Related papers  (2024-10-05T20:49:05Z)
• A parameterised model for link prediction using node centrality and similarity measure based on graph embedding [5.507008181141738]
  Link prediction is a key aspect of graph machine learning. It involves predicting new links that may form between network nodes. Existing models have significant shortcomings. We present the Node Centrality and Similarity Based. Model (NCSM), a novel method for link prediction tasks.
  arXiv  Detail & Related papers  (2023-09-11T13:13:54Z)
• AoI-based Temporal Attention Graph Neural Network for Popularity Prediction and Content Caching [9.16219929722585]
  Information-centric network (ICN) aims to proactively keep limited popular content at the edge of network based on predicted results. In this paper, we leverage an effective dynamic graph neural network (DGNN) to jointly learn the structural and temporal patterns embedded in the bipartite graph. We also propose an age of information (AoI) based attention mechanism to extract valuable historical information.
  arXiv  Detail & Related papers  (2022-08-18T02:57:17Z)
• BScNets: Block Simplicial Complex Neural Networks [79.81654213581977]
  Simplicial neural networks (SNN) have recently emerged as the newest direction in graph learning. We present Block Simplicial Complex Neural Networks (BScNets) model for link prediction. BScNets outperforms state-of-the-art models by a significant margin while maintaining low costs.
  arXiv  Detail & Related papers  (2021-12-13T17:35:54Z)
• Temporal Graph Network Embedding with Causal Anonymous Walks Representations [54.05212871508062]
  We propose a novel approach for dynamic network representation learning based on Temporal Graph Network. For evaluation, we provide a benchmark pipeline for the evaluation of temporal network embeddings. We show the applicability and superior performance of our model in the real-world downstream graph machine learning task provided by one of the top European banks.
  arXiv  Detail & Related papers  (2021-08-19T15:39:52Z)
• Relational Graph Neural Networks for Fraud Detection in a Super-App environment [53.561797148529664]
  We propose a framework of relational graph convolutional networks methods for fraudulent behaviour prevention in the financial services of a Super-App. We use an interpretability algorithm for graph neural networks to determine the most important relations to the classification task of the users. Our results show that there is an added value when considering models that take advantage of the alternative data of the Super-App and the interactions found in their high connectivity.
  arXiv  Detail & Related papers  (2021-07-29T00:02:06Z)
• How effective are Graph Neural Networks in Fraud Detection for Network Data? [0.0]
  Graph-based Neural Networks (GNNs) are recent models created for learning representations of nodes (and graphs) Financial fraud stands out for its socioeconomic relevance and for presenting particular challenges, such as the extreme imbalance between the positive (fraud) and negative (legitimate transactions) classes. We conduct experiments to evaluate existing techniques for detecting network fraud, considering the two previous challenges.
  arXiv  Detail & Related papers  (2021-05-30T15:17:13Z)
• Graph-Based Neural Network Models with Multiple Self-Supervised Auxiliary Tasks [79.28094304325116]
  Graph Convolutional Networks are among the most promising approaches for capturing relationships among structured data points. We propose three novel self-supervised auxiliary tasks to train graph-based neural network models in a multi-task fashion.
  arXiv  Detail & Related papers  (2020-11-14T11:09:51Z)
• EWS-GCN: Edge Weight-Shared Graph Convolutional Network for Transactional Banking Data [2.1169216065483996]
  We show that information about connections between clients based on money transfers between them allows us to significantly improve the quality of credit scoring. We develop a new graph neural network model EWS-GCN that combines ideas of graph convolutional and recurrent neural networks via attention mechanism.
  arXiv  Detail & Related papers  (2020-09-30T12:09:28Z)
• Learning to Extrapolate Knowledge: Transductive Few-shot Out-of-Graph Link Prediction [69.1473775184952]
  We introduce a realistic problem of few-shot out-of-graph link prediction. We tackle this problem with a novel transductive meta-learning framework. We validate our model on multiple benchmark datasets for knowledge graph completion and drug-drug interaction prediction.
  arXiv  Detail & Related papers  (2020-06-11T17:42:46Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.