Financial Fraud Detection

Year-over-Year Developments in Financial Fraud Detection via Deep Learning: A Systematic Literature Review ArXiv ID: 2502.00201 “View on arXiv” Authors: Unknown Abstract This paper systematically reviews advancements in deep learning (DL) techniques for financial fraud detection, a critical issue in the financial sector. Using the Kitchenham systematic literature review approach, 57 studies published between 2019 and 2024 were analyzed. The review highlights the effectiveness of various deep learning models such as Convolutional Neural Networks, Long Short-Term Memory, and transformers across domains such as credit card transactions, insurance claims, and financial statement audits. Performance metrics such as precision, recall, F1-score, and AUC-ROC were evaluated. Key themes explored include the impact of data privacy frameworks and advancements in feature engineering and data preprocessing. The study emphasizes challenges such as imbalanced datasets, model interpretability, and ethical considerations, alongside opportunities for automation and privacy-preserving techniques such as blockchain integration and Principal Component Analysis. By examining trends over the past five years, this review identifies critical gaps and promising directions for advancing DL applications in financial fraud detection, offering actionable insights for researchers and practitioners. ...

Graph Neural Networks for Financial Fraud Detection: A Review ArXiv ID: 2411.05815 “View on arXiv” Authors: Unknown Abstract The landscape of financial transactions has grown increasingly complex due to the expansion of global economic integration and advancements in information technology. This complexity poses greater challenges in detecting and managing financial fraud. This review explores the role of Graph Neural Networks (GNNs) in addressing these challenges by proposing a unified framework that categorizes existing GNN methodologies applied to financial fraud detection. Specifically, by examining a series of detailed research questions, this review delves into the suitability of GNNs for financial fraud detection, their deployment in real-world scenarios, and the design considerations that enhance their effectiveness. This review reveals that GNNs are exceptionally adept at capturing complex relational patterns and dynamics within financial networks, significantly outperforming traditional fraud detection methods. Unlike previous surveys that often overlook the specific potentials of GNNs or address them only superficially, our review provides a comprehensive, structured analysis, distinctly focusing on the multifaceted applications and deployments of GNNs in financial fraud detection. This review not only highlights the potential of GNNs to improve fraud detection mechanisms but also identifies current gaps and outlines future research directions to enhance their deployment in financial systems. Through a structured review of over 100 studies, this review paper contributes to the understanding of GNN applications in financial fraud detection, offering insights into their adaptability and potential integration strategies. ...

Advanced Financial Fraud Detection Using GNN-CL Model ArXiv ID: 2407.06529 “View on arXiv” Authors: Unknown Abstract The innovative GNN-CL model proposed in this paper marks a breakthrough in the field of financial fraud detection by synergistically combining the advantages of graph neural networks (gnn), convolutional neural networks (cnn) and long short-term memory (LSTM) networks. This convergence enables multifaceted analysis of complex transaction patterns, improving detection accuracy and resilience against complex fraudulent activities. A key novelty of this paper is the use of multilayer perceptrons (MLPS) to estimate node similarity, effectively filtering out neighborhood noise that can lead to false positives. This intelligent purification mechanism ensures that only the most relevant information is considered, thereby improving the model’s understanding of the network structure. Feature weakening often plagues graph-based models due to the dilution of key signals. In order to further address the challenge of feature weakening, GNN-CL adopts reinforcement learning strategies. By dynamically adjusting the weights assigned to central nodes, it reinforces the importance of these influential entities to retain important clues of fraud even in less informative data. Experimental evaluations on Yelp datasets show that the results highlight the superior performance of GNN-CL compared to existing methods. ...

TimeTrail: Unveiling Financial Fraud Patterns through Temporal Correlation Analysis ArXiv ID: 2308.14215 “View on arXiv” Authors: Unknown Abstract In the field of financial fraud detection, understanding the underlying patterns and dynamics is important to ensure effective and reliable systems. This research introduces a new technique, “TimeTrail,” which employs advanced temporal correlation analysis to explain complex financial fraud patterns. The technique leverages time-related insights to provide transparent and interpretable explanations for fraud detection decisions, enhancing accountability and trust. The “TimeTrail” methodology consists of three key phases: temporal data enrichment, dynamic correlation analysis, and interpretable pattern visualization. Initially, raw financial transaction data is enriched with temporal attributes. Dynamic correlations between these attributes are then quantified using innovative statistical measures. Finally, a unified visualization framework presents these correlations in an interpretable manner. To validate the effectiveness of “TimeTrail,” a study is conducted on a diverse financial dataset, surrounding various fraud scenarios. Results demonstrate the technique’s capability to uncover hidden temporal correlations and patterns, performing better than conventional methods in both accuracy and interpretability. Moreover, a case study showcasing the application of “TimeTrail” in real-world scenarios highlights its utility for fraud detection. ...