Nos derniers articles

This study presents the Bayesian Component Encoder Analysis (BCEA) method for identifying financial crime. Integrating FinBERT, principal component analysis (PCA), and Bayesian networks, BCEA uses financial texts, images, and transaction records. FinBERT extracts semantic features, PCA reduces data complexity, and Bayesian networks model features for probabilistic reasoning. The authors claim BCEA achieves 94.35% accuracy and a 12.78-second recognition time, surpassing LSTM and BERT models. The authors state that the method demonstrates potential for financial supervision and risk management, with possible applications in complex financial scenarios, based on experimental results validating its effectiveness.

This paper 𝗲𝘅𝗮𝗺𝗶𝗻𝗲𝘀 𝘁𝗵𝗲 𝗲𝘀𝗰𝗮𝗹𝗮𝘁𝗶𝗻𝗴 𝘁𝗵𝗿𝗲𝗮𝘁 𝗼𝗳 𝗔𝗜-𝗱𝗿𝗶𝘃𝗲𝗻 𝗳𝗿𝗮𝘂𝗱 𝗮𝗻𝗱 𝗰𝘆𝗯𝗲𝗿𝗰𝗿𝗶𝗺𝗲, highlighting how criminal organizations are rapidly adopting advanced AI, particularly generative AI, to execute sophisticated attacks. It details how these malicious uses lead to 𝗶𝗻𝗰𝗿𝗲𝗮𝘀𝗲𝗱 𝗳𝗶𝗻𝗮𝗻𝗰𝗶𝗮𝗹 𝗹𝗼𝘀𝘀𝗲𝘀, 𝗺𝗼𝗿𝗲 𝗶𝗻𝘁𝗿𝗶𝗰𝗮𝘁𝗲 𝗰𝗿𝗶𝗺𝗲 𝗽𝗮𝘁𝘁𝗲𝗿𝗻𝘀, 𝗮𝗻𝗱 𝗻𝗼𝘃𝗲𝗹 𝘀𝗰𝗮𝗺 𝘁𝘆𝗽𝗼𝗹𝗼𝗴𝗶𝗲𝘀, such as deepfakes and advanced phishing. The document also 𝗲𝘅𝗽𝗹𝗼𝗿𝗲𝘀 𝘁𝗵𝗲 𝗰𝗵𝗮𝗹𝗹𝗲𝗻𝗴𝗲𝘀 𝗳𝗮𝗰𝗲𝗱 𝗯𝘆 𝗳𝗶𝗻𝗮𝗻𝗰𝗶𝗮𝗹 𝗶𝗻𝘀𝘁𝗶𝘁𝘂𝘁𝗶𝗼𝗻𝘀 𝗶𝗻 𝗱𝗲𝗳𝗲𝗻𝗱𝗶𝗻𝗴 𝗮𝗴𝗮𝗶𝗻𝘀𝘁 𝘁𝗵𝗲𝘀𝗲 𝘁𝗵𝗿𝗲𝗮𝘁𝘀, citing issues like slow AI adoption, outdated risk management frameworks, and underinvestment in defense systems. Ultimately, it 𝗮𝗱𝘃𝗼𝗰𝗮𝘁𝗲𝘀 𝗳𝗼𝗿 𝘁𝗵𝗲 𝘂𝗿𝗴𝗲𝗻𝘁 𝗱𝗲𝘃𝗲𝗹𝗼𝗽𝗺𝗲𝗻𝘁 𝗼𝗳 𝗮𝗴𝗶𝗹𝗲, 𝗔𝗜-𝘃𝗲𝗿𝘀𝘂𝘀-𝗔𝗜 𝗱𝗲𝗳𝗲𝗻𝘀𝗲 𝘀𝘁𝗿𝗮𝘁𝗲𝗴𝗶𝗲𝘀 and emphasizes the critical need for industry-wide cooperation to counteract the evolving landscape of AI-enabled financial crime.