Comparison of Machine Learning Algorithms for Predicting Financial Risk in Cash Flow Statements

Ecem Engi̇n, Damla İLTER FAKHOURI
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Abstract

Nowadays, making financial decisions and evaluating loan applications is a complex and sensitive process. Cash flow data, which shows the financial risk status of businesses, plays a key role in the evaluation of loan applications. A detailed analysis with machine learning algorithms evaluates the performance of different models in the loan classification process and highlights the role of cash flow data in the process. The study includes data from 282 companies for the quarterly periods between 2018 and 2022. It is observed that there are limited studies on loan classification with cash flow statement in the literature. Considering the suitability of the data used in the study to the data structure, the creation of effective algorithms and the evaluation of these algorithms with information criteria aimed to provide a unique approach in the field. The model for the 2nd quarter of 2019 was selected as the best model with 99% accuracy and 99% F1 value. It is also determined that variable selection with high accuracy rates in the models established for each quarter is important for predicting financial risk.
预测现金流量表中财务风险的机器学习算法比较
如今,做出财务决策和评估贷款申请是一个复杂而敏感的过程。显示企业财务风险状况的现金流数据在贷款申请评估中起着关键作用。利用机器学习算法进行的详细分析评估了不同模型在贷款分类过程中的表现,并强调了现金流数据在这一过程中的作用。研究包括来自 282 家公司 2018 年至 2022 年期间的季度数据。据观察,文献中关于利用现金流量表进行贷款分类的研究非常有限。考虑到研究中使用的数据与数据结构的适用性,创建有效算法并利用信息标准对这些算法进行评估,旨在为该领域提供一种独特的方法。2019 年第二季度的模型以 99% 的准确率和 99% 的 F1 值被选为最佳模型。同时还确定,在每个季度建立的模型中选择准确率高的变量对于预测金融风险非常重要。
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