  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.

Machine Learning;Statistical Finance