Financial Fraud Detection Using AI-Powered Natural Language Processing for Cyber Forensics
Abstract
Global economies confront major difficulties from financial fraud because its dynamic nature requires innovative detection systems. The research relies on artificial intelligence (AI) and natural language processing (NLP) technologies to develop strong fraud detection systems for cyber forensic applications. A method combining advanced BERT GPT, and LSTM NLP models and customized domain preprocessing enables fraudulent activity detection throughout multiple data types which include emails, chat logs, transaction descriptions, and social media messages. This system delivers exceptional performance with accuracy (98.7%) precision (98.2%) and recall (97.8%) which leads to an F1-score (98.0%) surpassing traditional machine learning approaches. The multilingual detection capacity is strong because the system successfully identifies fraud in four languages including English Spanish Chinese and Arabic with performance exceeding 91% across all selected languages. Effective-processing capabilities enable real-time operations that fit today's responsive operations. The system's evaluation reveals its adaptability together with scalability and efficiency capabilities that enable massive implementation across high-volume financial platforms. Through its design the system succeeds in managing resource utilization while maintaining detection accuracy standards into account the computational needs of fraud analysis.