Accern’s AI Studio empowers data scientists and financial services teams to customize NLP models that can predict sales, product demand, credit health, and more
Accern, a leading no-code, artificial intelligence (AI) company, today announced the release of its AI Studio, which enables financial service teams to make more informed investment decisions. Data scientists and financial services teams will now be able to create, train, and iterate on models in real-time, without having to write new code.
The AI Studio provides an additional value to the Accern no-code AI platform by enabling financial services teams to:
- Deploy predictive AI models in days instead of weeks or months
- Make insight-driven predictions on sales volume, product demand, credit health, ESG impacts, and other factors that can impact businesses
- Personalize and tune finance-specific NLP models for more accurate outputs
- Create and customize taxonomies to better gather insights around specific companies, topics, or themes of interest.
Financial data teams will also be able to use predictive modeling to build AI use cases and analyze unstructured data through natural language processing (NLP), which can create workflow efficiencies and reduce time to insight. This will allow investors, analysts, wealth and portfolio managers to make smarter financial predictions and better investment decisions.
“Accern’s AI studio is the newest addition to our no-code platform which further enables data scientists and financial services teams to create, train, and iterate on models in real-time without having to write any code,” said Kumesh Aroomoogan, co-founder and CEO of Accern. “We’re excited to empower financial services teams with the tools they need to make smarter financial predictions.”
Accern’s platform is used by leading financial services firms to analyze millions of unstructured data points more quickly and seamlessly, and in real-time. The platform was created to provide asset managers, insurers, hedge funds, and other financial services professionals with access to data points that are otherwise unavailable. With these insights, financial teams can make accurate financial predictions and better investment decisions.