Kolmogorov-Arnold Networks-based GRU and LSTM for Loan Default Early Prediction

TL;DR

This paper introduces innovative GRU-KAN and LSTM-KAN models that significantly improve early prediction accuracy of loan defaults over existing methods, enabling financial institutions to act proactively.

Contribution

The study proposes novel architectures combining Kolmogorov-Arnold Networks with GRU and LSTM for enhanced early loan default prediction.

Findings

Achieves over 92% accuracy three months in advance

Outperforms baseline models in precision, recall, and F1 score

Effective for early detection up to eight months prior

Abstract

This study addresses a critical challenge in time series anomaly detection: enhancing the predictive capability of loan default models more than three months in advance to enable early identification of default events, helping financial institutions implement preventive measures before risk events materialize. Existing methods have significant drawbacks, such as their lack of accuracy in early predictions and their dependence on training and testing within the same year and specific time frames. These issues limit their practical use, particularly with out-of-time data. To address these, the study introduces two innovative architectures, GRU-KAN and LSTM-KAN, which merge Kolmogorov-Arnold Networks (KAN) with Gated Recurrent Units (GRU) and Long Short-Term Memory (LSTM) networks. The proposed models were evaluated against the baseline models (LSTM, GRU, LSTM-Attention, and LSTM-Transformer) in terms of accuracy, precision, recall, F1 and AUC in different lengths of feature window, sample sizes, and early prediction intervals. The results demonstrate that the proposed model achieves a prediction accuracy of over 92% three months in advance and over 88% eight months in advance, significantly outperforming existing baselines.