To produce important investment decisions, investors require financial records and economic information. However, most companies manipulate investors and financial institutions by inflating their financial statements. Fraudulent Financial Activities exist in any monetary or financial transaction scenario, whether physical or electronic. A challenging problem that arises in this domain is the issue that affects and troubles individuals and institutions. This problem has attracted more attention in the field in part owing to the prevalence of financial fraud and the paucity of previous research. For this purpose, in this study, the main approach to solve this problem, an anomaly detection-based approach based on a combination of feature selection based on squirrel optimization pattern and classification methods have been used. The aim is to develop this method to provide a model for detecting anomalies in financial statements using a combination of selected features with the nearest neighbor classifications, neural networks, support vector machine, and Bayesian. Anomaly samples are then analyzed and compared to recommended techniques using assessment criteria. Squirrel optimization's meta-exploratory capability, along with the approach's ability to identify abnormalities in financial data, has been shown to be effective in implementing the suggested strategy. They discovered fake financial statements because of their expertise.

本研究基于松鼠优化模式的特征选择结合分类方法，旨在提供一个模型来检测财务报表中的异常情况，并比较分析异常样本，研究发现该方法能够有效地实现建议的策略，发现了假财务报表。

Detection of fraudulent financial papers by picking a collection of
  characteristics using optimization algorithms and classification techniques
  based on squirrels

利用优化算法和基于松鼠的分类技术挑选一组特征以检测欺诈性财务文件