Advancement in financial technology (FinTech) are revolutionizing product offerings across the financial services industry. Innovations in FinTech also appear to benefit end users, with recent evidence indicating that FinTech is enhancing lending and brokerage activities across segments of the investor community.With growing evidence that FinTech companies, besides using new age digital marketing techniques to attract investors, are also now leveraging Advance Data Science algorithms to better the yields on investment choices, as compared to the traditional research model. This has now lead to a growing demand of FinTech companies to now adopt, what is globally more commonly called“Robo-Analysts” that enhances the human capability to go beyond the traditional model of analysis of investment options like shares, mutual funds, ETF, bonds,debentures etc.
“Robo-Analysts”are human analyst-assisted Data Science Algorithms conducting automated research analysis. Robo-Analysts represent an important innovation in the research industry as they can potentially analyze large amounts of financial data and generate stock recommendations that are less subject to the limitations of those generated by standalone human analysts, which may be prone to behavioral, cognitive, or incentive-driven biases.
CloudPrisma’s “Robo-Analysts” solutions helps financial institutions, fund managers, brokers etc. to leverage FinTech advancements in Automated Investment Recommendations by using advance Data Science Algorithms for better and unbiased recommendations. CloudPrisma helps our wealth management companies to Mine and Analyze huge data sets linked to the various financial indices published by the stock exchanges, indices published by the countries regulator on market stability and economic events lead variations and other market data to better align their investment portfolios with the investors’ appetite for better returns.
CloudPrisma will help our clients also to come out with their own parameters to model the financial analysis and automate the portfolio structure. For empirical analysis, we may use stocks, bonds, commodities,ETF, exchange rate etc., apply a “stability” or Instability” index model, or“risk parity” model or “mean variance” model to filter investments assets and optimize the threshold value used as a filtering criterion by applying Genetic Algorithms to develop recommended portfolios for different risk tolerances and objectives.
The key benefits of the CloudPrisma“Robo-Analyst” solutions –
