Financial Distress Prediction For Small And Medium Enterprises Using Machine Learning Techniques
The text discusses financial distress prediction using machine learning, focusing on SMEs and various modeling techniques.
What it examines
This paper aims to develop a financial distress prediction model for SMEs using Decision Trees, Artificial Neural Networks, and Naive Bayes. It incorporates time factors and FICO scores to improve prediction accuracy, addressing the need for better financial distress forecasting tools for SMEs.
What it concludes
The research suggests that the proposed model can significantly improve financial distress predictions for SMEs. Potential applications include better credit risk assessment and early warning systems for lenders. Future research could explore additional variables and refine the model for different industries.
Evidence objects
The research suggests that the proposed model can significantly improve financial distress predictions for SMEs. Potential applications include better credit risk assessment and early warning systems for lenders. Future research could explore additional variables and refine the model for different industries.
key_findings bullet 1 · key_findings · validation V0
Raw abstract and provenance
Financial Distress Prediction plays a crucial role in the economy by accurately forecasting the number and probability of failing structures, providing insight into the growth and stability of a country's economy. However, predicting financial distress for Small and Medium Enterprises is challenging due to their inherent ambiguity, leading to increased funding costs and decreased chances of receiving funds. While several strategies have been developed for effective FCP, their implementation, accuracy, and data security fall short of practical applications. Additionally, many of these strategies perform well for a portion of the dataset but are not adaptable to various datasets. As a result, there is a need to develop a productive prediction model for better order execution and adaptability to different datasets. In this review, we propose a feature selection algorithm for FCP based on element credits and data source collection. Current financial distress prediction models rely mainly on financial statements and disregard the timeliness of organization tests. Therefore, we propose a corporate FCP model that better aligns with industry practice and incorporates the gathering of thin-head component analysis of financial data, corporate governance qualities, and market exchange data with a Relevant Vector Machine. Experimental results demonstrate that this strategy can improve the forecast efficiency of financial distress with fewer characteristic factors.
Source row: 805 · abstract type: unknown