Loan Default Risk Prediction Using Knowledge Graph

Abstract

Credit risk, also known as loan default risk, is one of the significant financial challenges in banking and financial institutions since it involves the uncertainty of the borrowers' ability to perform their contractual obligation. Banks and financial institutions rely on statistical and machine learning methods in predicting loan default to reduce the potential losses of issued loans. These machine learning applications may never achieve their full potential without the semantic context in the data. A knowledge graph is a collection of linked entities and objects that include semantic information to contextualize them. Knowledge graphs allow machines to incorporate human expertise into their decision-making and provide context to machine learning applications. Therefore, we proposed a loan default prediction model based on knowledge graph technology to improve the prediction model's accuracy and interpretability. The experimental results demonstrated that incorporating knowledge graph embedding as features can boost the performance of the conventional machine learning classifiers in predicting loan default risk.

Cite this work

No public copy is stored for this record, so only the metadata is cited.

Stored only when both papers are in the corpus — 15 citations exist upstream.

Papers that cite the same references — the relational fallback while embeddings are still backfilling.