Predictive Data Analytics & Data Visualization in the FinTech Industry
Institutional investors need to constantly value and comprehensively track the hundreds of factors that affect the vast array of stocks on their watchlist immediately. This task becomes impossible without the proper crawling, automation, and analytics infrastructure. Indium Software’s solution gathers all available public information, including stock exchange releases, press releases, third-party investment sources, domain-specific sources, and news sources. The solution analyzes the company’s past performance across different metrics and projects future growth potential using highly sophisticated algorithms and advanced predictive analytics.
Our client is a FinTech company that collects financial data from various sources and uses algorithms to provide deep insights for investment planning. Currently, they have around 200K+ pieces of financial market intelligence data.
- The client, a FinTech company, needed an analytics development partner to create a solution that would utilize publicly available company information to analyze their past performance and project future groowth potential. After careful consideration, Indium Software, with over two decades of experience working with cutting-edge technologies and in FinTech, was chosen and commissioned for the task.
- A team of three individuals recognized the pivotal role that data analytics can play in helping institutional investors evaluate potential companies for investment. They embarked on a FinTech venture to develop a technology solution for corporate data analytics.
- The solution’s objective was to analyze core company data and project potential growth in the upcoming quarters, considering various internal and external factors that influence it. This would provide investors with the necessary insights to assess the suitability of the venture for investment. This process disrupts the traditional approach, where auditors personally examine profit and loss statements and rely on intuition, which is susceptible to human error.