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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Vydáno v:	Scientific reports Ročník 15; číslo 1; s. 29959 - 14
Hlavní autoři:	Jin, Jing, Zhang, Yongqing
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	London Nature Publishing Group UK 15.08.2025 Nature Publishing Group Nature Portfolio
Témata:	639/705 639/705/1041 639/705/1042 639/705/1045 639/705/1046 639/705/117 639/705/258 639/705/531 639/705/794 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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Popis
Shrnutí:	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.
Bibliografie:	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23
ISSN:	2045-2322 2045-2322
DOI:	10.1038/s41598-025-15783-2