Methods of Detecting Fraud Using Analytical Tools
Year: 2025
Type: article
Abstract: This article examines the evolution of fraudulent schemes in the context of the digital transformation of the economy, which is accompanied by a transition from localized manipulation to complex ecosystem attacks using machine learning algorithms and artificial intelligence vulnerabilities. This necessitates the development of proactive countermeasures that integrate predictive analytics, network analysis of transaction chains, and real-time behavioral profiling to minimize potential damage. It also analyzes modern analytical tools, including machine learning, natural language processing, and statistical approaches, in the context of creating intelligent platforms capable of multidimensional analysis of heterogeneous data and ensuring the proactive detection of complex fraudulent structures.
Source: Russian Journal of Management
Author: Aleksandr Petrov
Institution: Financial University
Language: English
Subfield: Statistics and Probability
Field: Mathematics
Domain: Physical Sciences
Open Access status: diamond
OpenAlex ID: W4416248178