Data and Code to reproduce results in paper "A Systematic Literature Review on Graph-Based Models in Credit Risk Assessment"

Data and Code to reproduce results in paper "A Systematic Literature Review on Graph-Based Models in Credit Risk Assessment" This repository contains the necessary codes to reproduce results in the paper: Baals, L. J., Liu, Y., Osterrieder, J., & Hadji-Misheva, B. (2025). A Systematic Literature Review on Graph-Based Models in Credit Risk Assessment. Target journal: International Review of Financial Analysis, revising. Abstract: This review provides a comprehensive analysis of graph-based models in credit risk assessment within the financial industry. It systematically categorizes and evaluates various models, including factorial network models, Graphical Gaussian Models (GGMs), Graph Neural Networks (GNNs), network centrality measures, community detection methods, dynamic multi-layer networks, and advanced techniques like graph attention networks and hypergraphs. The analysis highlights the comparative advantages of graph-based models over traditional approaches in capturing complex relationships and contagion within financial networks. Factorial network models and GGMs excel in understanding latent factors and systemic risks, while GNNs and network centrality measures enhance predictive accuracy and explainability. Community detection and dynamic multi-layer networks offer insights into risk transmission and systemic risk. Advanced techniques such as graph attention networks, hypergraphs, and knowledge graph models integrate diverse data sources for holistic credit risk assessment. Additionally, the review underscores the potential of graph-based models in handling imbalanced data, improving credit scoring for thin-file borrowers, and mitigating financial contagion. The findings emphasize the need for future research to integrate early warning systems into customer segmentation frameworks and extend the utility of graph-based models to identify positive financial behaviors and lending opportunities. Raw data: Source A: Use the following search query string to search in Scopus: TITLE-ABS-KEY(("graph" OR "network" OR "graphs" OR "networks" OR "network models" OR "network-based models" OR "graph models" OR "network-based modeling" OR "graph-based modeling") AND ("credit risk" OR "financial risk" OR "banking risk" OR "loan risk" OR "credit scoring" OR "risk assessment") AND ("financial" OR "banking" OR "loan" OR "P2P" OR "Peer-to-Peer")) AND PUBYEAR > 1993 AND DOCTYPE(ar) NOT TITLE-ABS-KEY("COVID-19" OR "epidemic" OR "infection" OR "disease" OR "virus") Source B: Use the following search query string to search in Web of Science: (TS=("graph" OR "network" OR "graphs" OR "networks" OR "network models" OR "network-based models" OR "graph models" OR "network-based modeling" OR "graph-based modeling") AND TS=("credit risk" OR "financial risk" OR "banking risk" OR "loan risk" OR "credit scoring" OR "risk assessment") AND TS=("financial" OR "banking" OR "loan" OR "P2P" OR "Peer-to-Peer") AND TS=("network" OR "graph" OR "network models" OR "graph models" OR "network-based modeling" OR "graph-based modeling")) NOT TS=("COVID-19" OR "epidemic" OR "infection" OR "disease" OR "virus") Source C: Use the following search query string to search in Web of Science: (TS=("graph" OR "network" OR "graphs" OR "networks" OR "network models" OR "network-based models" OR "graph models" OR "network-based modeling" OR "graph-based modeling") AND TS=("credit risk" OR "financial risk" OR "banking risk" OR "loan risk" OR "credit scoring" OR "risk assessment") AND TS=("financial" OR "banking" OR "loan" OR "P2P" OR "Peer-to-Peer") AND TS=("network" OR "graph" OR "network models" OR "graph models" OR "network-based modeling" OR "graph-based modeling")) Data files: 24.7.30 sourceA search 1.xlsx 24.7.30 sourceB search 1 v002_LB_sync.xlsx 24.7.26 sourceC extra papers.xlsx New_final_search.xlsx Codes to analyse the collected papers: Literature Review.qmd cluster.R