Artificial intelligence-based news analysis system from a banking perspective

The banking and finance sectors require innovative solutions to manage the ever-increasing flow of data. In line with this need, the developed system continuously scans national and local news sources to automate company intelligence. The system identifies and analyzes news related to companies and assigns scores to the companies mentioned in the texts. This allows the determination of which company the news content pertains to, based on the company's weight score. The sentiment analysis model of the system achieved an 86% success rate. This model classifies news content as positive, negative, or neutral, and it was found that national and local news sources emphasize different terms and keywords relevant to the banking sector. The proposed system determines the market perception and public stance of companies based on positive and negative keywords that are significant from a banking perspective. In this way, banks and financial institutions gain access to a strategic company intelligence resource that contributes directly to their risk management processes. The system, designed with a modular structure, operates with real-time processing capabilities, with each module functioning as an independent microservice. These features provide a significant advantage by offering timely and accurate company intelligence that meets the dynamic needs of the banking sector, thereby supporting decision-making processes.