AI-Enhanced Web Crawler for Real-Time Discovery of Emerging Trends in Sustainable Finance

The evolving field of sustainable finance, which encompassing ESG (Environmental, Social, Governance) investing, green bonds, and climate-related financial disclosures—demands timely, data-driven insights from vast web-based sources such as policy updates, corporate filings, news outlets, and research publications. Manual tracking is no longer feasible due to the volume and velocity of data. Traditional web crawlers are rule-based and lack contextual understanding, leading to poor relevance detection, high false positives, and missed emerging topics. This paper shows how to create an AI-powered web crawler using techniques including Natural Language Processing (NLP), subject modeling, and spotting trends. We use a mix of keyword-driven crawling and BERT/FinBERT transformer-based language models to figure out what is semantically relevant. Emerging keyword recognition takes advantage of the fact that term frequency-inverse document frequency (TF-IDF) changes over time. Temporal trend analysis over time-sliced document sets, on the other hand, uses Latent Dirichlet Allocation (LDA). We put the suggested crawler through its paces on 50,000 URLs. These included blogs established by regulatory entities, sites that simply report on financial news, and blogs run by schools and other organizations. When compared to baseline crawling, it was 87.5% accurate and 82.3% recall. This means it could find new problems with acquiring money in a way that lasts.