The success of the AI Marketplace is proof to Accern's mission to democratize AI and boost innovation for financial enterprises.
FREMONT, CA: Accern, a pioneering no-code AI platform, announces that its Accern AI Marketplace, which dramatically accelerates the speed enterprises can implement and begin reaping the benefits of AI across their businesses. Accern's AI Marketplace enables data scientists and business analysts to enhance their business functions with over 400 ready-mades AI applications to automate workflows and enhance decisions. The result is a huge gain in productivity for financial teams.
Applications are not limited to insights on Credit Risk, Mergers and Acquisitions, COVID-19, ESG Behaviors, -Money Laundering Analytics, and more. These use cases are backed by AI and adaptive NLP (natural language processing) to enable financial services teams to rapidly research and extract data, and achieve investment insights and handle risk. Clients suited to take advantage of the Accern AI Marketplace are firms across the financial services spectrum like asset managers, insurance firms, and banks. Accern operates with over 50 clients across financial services. The release of the marketplace is a significant step forward for the widespread availability of AI across the industry.
Accern's AI Marketplace is the first to provide financial services AI-based adaptive learning potentials, demonstrating that a market leader on the forefront of putting AI in everyone's hands is evolving. With the accurate collection of adaptive NLP models trained for finance like entity and theme classifications, sentiment and relevance analyses, and foreign language translations, Accern is perfect for continuing to expand across the market.
One of the significant hurdles to the mass adoption of AI within businesses and financial services enterprises is data scientists' inability to keep pace with the growing business demands and deploy projects at the same pace. Compared to traditional model development cycles, Accern is 3000x faster, which means rapid adoption of improved analytics across the business and more efficient use of data science resources.
