Graph-based techniques provide unique solution opportunities for financial crime detection. However, implementing such solutions at industrial-scale in real-time financial transaction processing systems has brought numerous application challenges to light. In this paper, we discuss the implementation ...
Wide coverage: FinBen integrates classic NLP tasks (text analysis, knowledge extraction, question answering) with finance-specific challenges (numeric labeling) and innovates by assessing LLMs on real-world financial applications (stock prediction, credit scoring) and for the first time directly assess...
Fraud detection is a set of processes and analyses that allow firms to identify and prevent unauthorized activity. It has become one of the major challenges for most organizations, particularly those in banking, finance, retail, and e-commerce. Any kind of fraud negatively affects...
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 resea...
However, supervised learning models are associated with many challenges that have been and can be addressed by semi-supervised and unsupervised learning models proposed in recently published literature. This survey aims to investigate and present a thorough review of the most popular and effective ...
In SLR, research questions are key and decisive for the success of the study (Kitchenham and Stuart,2007). Therefore, analyzing the existing literature on financial fraud detection through ML techniques and its characteristics, problems, challenges, solutions, and research trends is crucial. Table1...
In this paper, we address these aforementioned challenges associated with financial data and introduce FinGPT, an endto-end open-source framework for financial large language models (FinLLMs). Adopting a data-centric approach, FinGPT underscores the crucial role of data acquisition, cleaning, and ...
In SLR, research questions are key and decisive for the success of the study (Kitchenham and Stuart,2007). Therefore, analyzing the existing literature on financial fraud detection through ML techniques and its characteristics, problems, challenges, solutions, and research trends is crucial. Table1...
Data mining techniques are providing great aid in financial accounting fraud detection, since dealing with the large data volumes and complexities of financial data are big challenges for forensic accounting. This paper presents a comprehensive review of the literature on the application of data mining...
Cross-Border Challenges in Financial Markets Monitoring and Surveillance: A Case Study of Customer-Driven Service Value Networks In this paper cross border market surveillance activities are modeled as service systems which exist and interact in a service-oriented economy. Moreover, ... D Diaz,B Th...