A Logical Evaluation of Exploiting Financial Scope Assessment Based on Social Media Analytics Using Deep Learning assisted FinTech Framework for Digital Financial Profiling

Modern digital economic conditions make it possible to develop new assessment techniques through the union of artificial intelligence and financial technology (FinTech). The project develops a deep learning-assisted FinTech framework to conduct logical financial scope assessment evaluation and exploitation through social media analytics. Financial discussions on Twitter and Reddit platforms have entered an exponential growth phase enabling people to expose behavioral signals which practitioners can use to conduct real-time digital financial profiles. Social platform data collection combines with XLNet and Ekman's model for sentiment and emotion detection and a hybrid classifier made of LSTM and CNN and Attention mechanism for profile classification. Software engineering exploits a layered system which processes financial behavioral indicators to create a segmentation of user types including “Saver,” “Investor,” and “Risk-Taker.” Results demonstrate how the hybrid model obtains 96.1% peak accuracy which surpasses baseline systems BERT at 94.2% and XLNet at 94.8%. The operational capability of FinTech applications improves through user profiling which enables them to deliver customized services and evaluate financial reliability and forecast economic conduct with better accuracy. The model was designed to handle both multisource input analysis and different language content inputs which expands its potential throughout various contexts. The examined research establishes a link between social conduct analysis and financial metrics which paves the way for future FinTech individualized solutions.