Linking bank clients using graph neural networks powered by rich transactional data

Shumovskaia, Valentina; Fedyanin, Kirill; Sukharev, Ivan; Berestnev, Dmitry; Panov, Maxim

doi:10.1007/s41060-021-00247-3

Cited by 14 publications

(6 citation statements)

References 25 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…For instance, the edges can contain information such as transaction amount and frequency while nodes themselves can contain rich features such as FICO score, income, and account balance. This structure lends itself to the application of Graph Neural Networks as shown previously in [1] and [2].…”

Section: Introductionmentioning

confidence: 72%

“…Graph representation learning is often used as a generalized approach to feature generation from a graph structure that can be used in a number of down stream applications. In financial services these include fraud detection and credit decisions [2], [1]. Many of the traditional representation learning techniques assume stationarity in the underlying structures that they embed.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Navigating the Dynamics of Financial Embeddings over Time

Gogoglou¹,

Nguyen²,

Salimov³

et al. 2020

Preprint

View full text Add to dashboard Cite

Financial transactions constitute connections between entities and through these connections a large scale heterogeneous weighted graph is formulated. In this labyrinth of interactions that are continuously updated, there exists a variety of similarity-based patterns that can provide insights into the dynamics of the financial system. With the current work, we propose the application of Graph Representation Learning in a scalable dynamic setting as a means of capturing these patterns in a meaningful and robust way. We proceed to perform a rigorous qualitative analysis of the latent trajectories to extract real world insights from the proposed representations and their evolution over time that is to our knowledge the first of its kind in the financial sector. Shifts in the latent space are associated with known economic events and in particular the impact of the recent Covid-19 pandemic to consumer patterns. Capturing such patterns indicates the value added to financial modeling through the incorporation of latent graph representations.

show abstract

Section: Introductionmentioning

confidence: 72%

Section: Introductionmentioning

confidence: 99%

Navigating the Dynamics of Financial Embeddings over Time

Gogoglou¹,

Nguyen²,

Salimov³

et al. 2020

Preprint

View full text Add to dashboard Cite

show abstract

“…Other works have used a graph convolutional network-based approach for this purpose. Shumovskaia et al (2020) present one of the first empirical works with massive graphs created from transactions between clients of a large Russian bank. They propose a framework to estimate links using SEAL (Zhang and Chen, 2018) and Recurrent Neural Networks, the SEAL-RNN framework.…”

Section: Credit Risk and Social Networkmentioning

confidence: 99%

On the combination of graph data for assessing thin-file borrowers' creditworthiness

Muñoz-Cancino,

Bravo,

Ríos

et al. 2021

Preprint

View full text Add to dashboard Cite

The thin-file borrowers are customers for whom a creditworthiness assessment is uncertain due to their lack of credit history; many researchers have used borrowers' relationships and interactions networks in the form of graphs as an alternative data source to address this. Incorporating network data is traditionally made by hand-crafted feature engineering, and lately, the graph neural network has emerged as an alternative, but it still does not improve over the traditional method's performance. Here we introduce a framework to improve credit scoring models by blending several Graph Representation Learning methods: feature engineering, graph embeddings, and graph neural networks. In this approach, we stacked their outputs and trained a gradient boosting classifier to produce a single score. We validated this framework using a unique multi-source dataset that characterizes the relationships, interactions, and credit history for the entire population of a Latin American country, applying it to credit risk models, application and behaviour, targeting both individuals and companies. Our results show that the graph representation learning methods should be used as complements, and these should not be seen as self-sufficient methods as is currently done. In terms of AUC and KS, we enhance the creditworthiness assessment statistical performance, outperforming traditional methods to incorporate graph data. In Corporate lending, where the gain is much higher, it confirms that evaluating an unbanked company cannot solely consider its characteristics. The business ecosystem where these companies interact with their owners, suppliers, customers, and other companies provides novel knowledge that enables financial institutions to enhance their creditworthiness assessment. Our results let us know when and on which population to use graph data and what effects on performance to expect. They also show the enormous value of graph data on the unbanked credit scoring problem, principally to help companies' banking.

show abstract

“…Recently, the idea to analyse bank clients as a part of a network was introduced in fraud detection problem [10] and in the task of embeddings construction [11], while in [12] authors solve a problem of anti-money laundering detection. Our recent work [13] also considers bank clients as a network but focuses on a different task of finding stable connections between clients which is treated as a link prediction problem.…”

Section: Related Workmentioning

confidence: 99%

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

Sukharev¹,

Shumovskaia²,

Fedyanin³

et al. 2020

Preprint

Self Cite

View full text Add to dashboard Cite

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 large-scale data. We also demonstrate that our model outperforms the stateof-the-art graph neural networks achieving excellent results.

show abstract

Linking bank clients using graph neural networks powered by rich transactional data

Cited by 14 publications

References 25 publications

Navigating the Dynamics of Financial Embeddings over Time

Navigating the Dynamics of Financial Embeddings over Time

On the combination of graph data for assessing thin-file borrowers' creditworthiness

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

Contact Info

Product

Resources

About