Business
Release of Insig Portfolio 2.0
Release of Insig Portfolio 2.0.
About this update from Insig Ai Plc
[{"type":"text","content":"\n \n \n \n RNS Number : 8273N\n Insig AI Plc\n 04 October 2021\n \n \n \n \n This is a Reach announcement and the information contained is not considered to be material or to have a significant impact on management's expectations of the Company's performance. Reach is an investor communication service aimed at assisting listed companies to distribute non-regulatory news releases into the public domain. Information required to be notified under the AIM Rules for Companies, Market Abuse Regulation or other regulation would be disseminated as an RNS regulatory announcement and not on Reach.\n \n \n \n \n \n \n \n \n \n Insig AI plc\n \n \n \n (\"Insig AI\" or the \"Company\")\n \n \n \n Release of Insig Portfolio 2.0\n \n \n \n \n Insig Portfolio 2.0 significantly enhances the data science tool kit \n \n \n \n \n available to asset managers.\n \n \n \n Insig AI, the technology company that provides machine learning solutions for asset managers, is pleased to announce the second major release of its flagship software, Insig Portfolio.\n \n \n Insig Portfolio is a user-friendly web application which provides portfolio managers of all sizes and strategies with additional data driven insights to aid their investment decisions. Insig Portfolio was developed to alleviate the frustration of asset managers currently working with error-prone spreadsheets, and to remove the time-consuming effort of building data science applications in-house. \n \n \n To address these issues, Insig Portfolio has a framework-based architecture that is easily tailored to a portfolio manager's investment thesis and preferred sources of data. At the same time, Insig Portfolio's analytics are underpinned by a robust data management process, freeing analysts and portfolio managers from the need to do time consuming manual data updates.\n \n \n Furthermore, Insig Portfolio includes functionality to explore financial data, ESG metrics, identify important financial features, construct portfolios with machine learning or screening tools, measure and optimise risk exposures, and attribute portfolio performance to regions, sectors and other idiosyncratic characteristics. \n \n \n Exponential growth in computing processing power has widened the information gap between asset managers with traditional, inefficient data systems and those with easily...