Personality Prediction from Social Media Posts Using NLP and Machine Learning

This research offers a time-frequency-based method to improve anti-money laundering efforts by identifying questionable financial activity. To examine transaction patterns and spot abnormalities suggestive of money laundering, the suggested model makes use of machine learning techniques like Random Forest, Gradient Boosting, and Long Short-Term Memory (LSTM) networks. Temporal and periodic patterns are captured by the model through the use of feature extraction techniques in the frequency and time domains. With a strong tool to help financial institutions stop fraud and adhere to regulations, the results show a high detection accuracy. For real-time financial transaction monitoring, the framework provides an expandable solution.