The analysis of fraud detection in financial market under machine learning

With the rapid development of the global financial market, the problem of financial fraud is becoming more and more serious, which brings huge economic losses to the market, consumers and investors and threatens the stability of the financial system. Traditional fraud detection methods based on rule...

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Veröffentlicht in:Scientific reports Jg. 15; H. 1; S. 29959 - 14
Hauptverfasser: Jin, Jing, Zhang, Yongqing
Format: Journal Article
Sprache:Englisch
Veröffentlicht: London Nature Publishing Group UK 15.08.2025
Nature Publishing Group
Nature Portfolio
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Accuracy
Adaptability
Algorithms
Classification
Credit card fraud
Decision tree
Deep learning
Financial market
Fraud
Fraud detection
Fraud prevention
Humanities and Social Sciences
Hypothesis testing
International finance
Literature reviews
Logistic regression
Machine learning
Methods
multidisciplinary
Neural networks
Risk management
Science
Science (multidisciplinary)
Securities markets
Stacking
Statistical analysis
Support vector machines
Gradient boosting tree
Logistic regression
Stacking
Machine learning
Financial market
Random forest
Support vector machine
Fraud detection
Decision tree
ISSN:2045-2322, 2045-2322
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Beschreibung
Zusammenfassung:With the rapid development of the global financial market, the problem of financial fraud is becoming more and more serious, which brings huge economic losses to the market, consumers and investors and threatens the stability of the financial system. Traditional fraud detection methods based on rules and statistical analysis are difficult to deal with increasingly complex and evolving fraud methods, and there are problems such as poor adaptability and high false alarm rate. Therefore, this paper proposes a financial fraud detection model based on Stacking ensemble learning algorithm, which integrates many basic learners such as logical regression (LR), decision tree (DT), random forest (RF), Gradient Boosting Tree (GBT), support vector machine (SVM) and neural network (NN), and introduces feature importance weighting and dynamic weight adjustment mechanism to improve the model performance. The experiment is based on more than 1 million real financial transaction data. The results show that the Stacking model is significantly superior to the traditional single model in accuracy (95%), recall (93%) and F1 score (94%), and has stronger generalization ability and stability. Although the Stacking model has challenges in computing cost and delay, its advantages in fraud detection accuracy and robustness make it a powerful tool for financial institutions to improve their risk control ability. In the future, its real-time adaptability can be further optimized through online learning and incremental update mechanism.
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ISSN:2045-2322
2045-2322
DOI:10.1038/s41598-025-15783-2