AllocateRite Introduces RiskMonkey™ AI: A new product that uses sophisticated risk analytics and AI-based forecasting together in an easy-to-use app

AllocateRite , RiskMonkey™,AI, risk analytics

AllocateRite Digital, the New York FinTech and data science company that provides investment managers with ETF-based dynamic asset allocation strategies, is excited to announce the launch of a portfolio analysis app whose sophistication makes it appropriate for wealth managers but whose ease of use and flexibility can be utilized by anyone.

After a user links her portfolio via the industry-standard Plaid, RiskMonkey analyzes that portfolio’s risk characteristics, including the traditional quantities of value at risk, max drawdown, and correlation with the S&P 500.  RiskMonkey employs AI to forecast value at risk, giving users a more accurate, cutting-edge tool.  Even for large portfolios consisting of hundreds of positions, the foregoing analysis is synthesized into a single overall risk number, within seconds, providing the user a high-level snapshot of his portfolio’s vulnerability.

A user can also view the risk characteristics of individual components of her portfolio and hypothetical portfolios created by removing positions.  Finally, the app provides momentum indicators that could help inform investment decisions, if, for example, one wanted to decrease the risk of his portfolio. This might be suitable for individuals with retirement or 401K brokerage accounts.

“AllocateRite aims to innovate on two fronts: one, by continuing to employ and develop the latest AI technologies including deep learning; and two, by democratizing FinTech and investment management, giving everyone access to technologies that had been reserved for high net worth individuals or financial institutions,” said AllocateRite Chief of AI and Data Science, Dr. Michael Spece. “Delivering state-of-the-art tools to everyone via a mobile app realizes our dreams. And, because the deep learning and other infrastructure we continue to develop is more flexible than traditional statistical approaches, we are well-positioned for continued innovation.”

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