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Research on Identification and Prediction of Financial Fraud of Listed Companies Based on Machine Learning

Published: 28 February 2024 Publication History

Abstract

Corporate financial fraud poses significant risks to economic stability, with deceptive tactics ranging from misrepresentation of financial figures to outright falsification of accounts. The prevalence of such fraud is influenced by factors such as economic pressures, regulatory gaps, and lack of transparency. This study aims to harness the capabilities of machine learning to predict corporate financial fraud, thereby enhancing financial risk mitigation measures and safeguarding investor interests. A comprehensive collection of corporate financial metrics was gathered, followed by feature engineering techniques to refine the data for machine learning algorithms. Various machine learning techniques, including support vector machines, decision trees, and random forests, were employed. The data underwent processes like cleaning, standardization, and principal component analysis before being split into training and test sets. The models were evaluated based on parameters like accuracy, ROC AUC, and F-Measure. The results showcased the potential of machine learning in accurately predicting corporate financial fraud, with certain models like LightGBM and Random Forest demonstrating high predictive accuracy.

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  1. Research on Identification and Prediction of Financial Fraud of Listed Companies Based on Machine Learning

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    ICCPR '23: Proceedings of the 2023 12th International Conference on Computing and Pattern Recognition
    October 2023
    589 pages
    ISBN:9798400707988
    DOI:10.1145/3633637
    Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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    Published: 28 February 2024

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    Author Tags

    1. Corporate financial fraud
    2. feature engineering
    3. machine learning
    4. predictive models

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