Credit Risk Modelling Using RNN-LSTM Hybrid Model for Digital Financial Institutions

Gabriel Musyoka, Antony Waititu, Herbert Imboga
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Abstract

In response to the rapidly evolving financial market and the escalating concern surrounding credit risk in digital financial institutions, this project addresses the urgency for accurate credit risk prediction models. Traditional methods such as Neural network models, kernel-based virtual machines, Z-score, and Logit (logistic regression model) have all been used, but their results have proven less than satisfactory. The project focuses on developing a credit scoring model specifically tailored for digital financial institutions, by leveraging a hybrid model that combines long short-term memory (LSTM) networks with recurrent neural networks (RNN). This innovative approach capitalizes on the strengths of the Long-Short Term Memory (LSTM) for long-term predictions and Recurrent Neural Network (RNN) for its recurrent neural network capabilities. A key component of the approach is feature selection, which entails extracting a subset of pertinent features from the credit risk data using RNN in order to help classify loan applications. The researcher chose to use data from Kaggle to study and compare the efficacy of different models. The findings reveal that the RNN-LSTM hybrid model outperforms other RNNs, LSTMs, and traditional models. Specifically, the hybrid model demonstrated distinct advantages, showcasing higher accuracy and a superior Area Under the Curve (AUC) compared to individual RNN and LSTM models. While RNN and LSTM models exhibited slightly lower accuracy individually, their combination in the hybrid model proved to be the optimal choice. In summary, the RNN-LSTM hybrid model developed stands out as the most effective solution for predicting credit risk in digital financial institutions, surpassing the performance of standalone RNN and LSTM models as well as traditional methodologies. This research contributes valuable insights for banks, regulators, and investors seeking robust credit risk assessment tools in the dynamic landscape of digital finance.
利用 RNN-LSTM 混合模型为数字金融机构建立信用风险模型
为应对快速发展的金融市场和对数字金融机构信用风险不断升级的担忧,该项目急需准确的信用风险预测模型。传统的方法,如神经网络模型、基于核的虚拟机、Z-score 和 Logit(逻辑回归模型)都曾被使用过,但结果都不尽如人意。本项目的重点是利用长短期记忆(LSTM)网络与递归神经网络(RNN)相结合的混合模型,开发专为数字金融机构量身定制的信用评分模型。这种创新方法充分利用了长短期记忆(LSTM)的长期预测能力和递归神经网络(RNN)的递归神经网络能力。该方法的一个关键组成部分是特征选择,即使用 RNN 从信贷风险数据中提取相关特征的子集,以帮助对贷款申请进行分类。研究人员选择使用 Kaggle 的数据来研究和比较不同模型的功效。研究结果表明,RNN-LSTM 混合模型优于其他 RNN、LSTM 和传统模型。具体来说,与单独的 RNN 和 LSTM 模型相比,混合模型具有更高的准确率和更优越的曲线下面积(AUC),表现出明显的优势。虽然单独的 RNN 和 LSTM 模型的准确率略低,但事实证明,将它们组合成混合模型是最佳选择。总之,所开发的 RNN-LSTM 混合模型是预测数字金融机构信用风险的最有效解决方案,其性能超过了独立的 RNN 和 LSTM 模型以及传统方法。这项研究为银行、监管机构和投资者在数字金融的动态环境中寻求稳健的信用风险评估工具提供了宝贵的见解。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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