Application Technologies and Challenges of Big Data Analytics in Anti-Money Laundering and Financial Fraud Detection

As financial criminal methods become increasingly sophisticated, traditional anti-money laundering and fraud detection approaches face significant challenges. This study focuses on the application technologies and challenges of big data analytics in anti-money laundering and financial fraud detection. The research begins by outlining the evolutionary trends of financial crimes and highlighting the new characteristics of the big data era. Subsequently, it systematically analyzes the application of big data analytics technologies in this field, including machine learning, network analysis, and real-time stream processing. Through case studies, the research demonstrates how these technologies enhance the accuracy and efficiency of anomalous transaction detection. However, the study also identifies challenges faced by big data analytics, such as data quality issues, algorithmic bias, and privacy protection concerns. To address these challenges, the research proposes solutions from both technological and managerial perspectives, including the application of privacy-preserving technologies like federated learning. Finally, the study discusses the development prospects of Regulatory Technology (RegTech), emphasizing the importance of synergy between technological innovation and regulatory policies. This research provides guidance for financial institutions and regulatory bodies in optimizing their anti-money laundering and fraud detection strategies.

Principal Model Analysis Based on Bagging PLS and PCA and Its Application in Financial Statement Fraud

Motivated by the Bagging Partial Least Squares(Bagging PLS)and Principal Component Analysis(PCA)algorithms,a novel approach known as Principal Model Analysis(PMA)method is introduced in this paper.In the proposed PMA algorithm,the PCA and the Bagging PLS are combined.In this method,multiple PLS models are trained on sub-training sets,derived from the training set using the random sampling with replacement approach.The regression coefficients of all the sub-PLS models are fused in a joint regression coefficient matrix.The final projection direction is then estimated by performing the PCA on the joint regression coefficient matrix.Subsequently,the proposed PMA method is compared with other traditional dimension reduction methods,such as PLS,Bagging PLS,Linear discriminant analysis(LDA)and PLS-LDA.Experimental results on six public datasets demonstrate that our proposed method consistently outperforms other approaches in terms of classification performance and exhibits greater stability.Additionally,it is employed in the application of financial statement fraud identification.PMA and other five algorithms are utilized to financial statement fraud which concerned by the academic community,and the results indicate that the classification of PMA surpassed that of the other methods.

Visual analytics for event detection: Focusing on fraud

The detection of anomalous events in huge amounts of data is sought in many domains.For instance,in the context of financial data,the detection of suspicious events is a prerequisite to identify and prevent attempts to defraud.Hence,various financial fraud detection approaches have started to exploit Visual Analytics techniques.However,there is no study available giving a systematic outline of the different approaches in this field to understand common strategies but also differences.Thus,we present a survey of existing approaches of visual fraud detection in order to classify different tasks and solutions,to identify and to propose further research opportunities.In this work,fraud detection solutions are explored through five main domains:banks,the stock market,telecommunication companies,insurance companies,and internal frauds.The selected domains explored in this survey were chosen for sharing similar time-oriented and multivariate data characteristics.In this survey,we(1)analyze the current state of the art in this field;(2)define a categorization scheme covering different application domains,visualization methods,interaction techniques,and analytical methods which are used in the context of fraud detection;(3)describe and discuss each approach according to the proposed scheme;and(4)identify challenges and future research topics.