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

TL;DR

This paper presents a novel anomaly detection method for fraudulent financial statements using squirrel optimization for feature selection combined with various classification techniques, demonstrating effectiveness in identifying financial fraud.

Contribution

It introduces a new approach combining squirrel optimization-based feature selection with multiple classifiers to detect financial statement fraud.

Findings

Effective detection of fraudulent financial statements achieved

Squirrel optimization enhances feature selection accuracy

Multiple classifiers improve anomaly detection performance

Abstract

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.