Year: 2025
Type: article
Abstract: : The increasing complexity of financial transactions and tax evasion strategies has necessitated the development of intelligent, data-driven systems to detect fraudulent activity and ensure compliance. This study introduces a robust hybrid framework that leverages Sparse Autoencoders for feature extraction, Neural Decision Forests for high-accuracy classification, and statistical feature engineering for behavioral analysis. The system is trained on a curated dataset of approximately 10,000 financial transactions, encompassing features such as transaction type, account balances, timing, and recipient risk scores, with derived metrics like transaction velocity, deviation, and balance changes. The architecture is designed to identify anomalous patterns, assess evasion probabilities, and flag high-risk accounts. Advanced machine learning models are employed to address challenges such as class imbalance, dynamic user behavior, and hidden fraud patterns. Compared to traditional rule-based methods, the proposed framework enhances fraud detection accuracy while minimizing false positives, offering a scalable solution for regulatory bodies to audit financial flows and improve transparency in tax collection systems
Institution: Presidency University
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: diamond
OpenAlex ID: W4410857044