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.

"... we find that the Bayesian approach outperforms the classical one in identifying whether a model is correctly specified which is the principal aim of any backtesting framework. The power of the methodology is due to its ability to test individual model parameters and hence identify which aspects of a model are misspecified as well as the degree of misspecification."