Predictive Analytics and Data Visualization Powers Smarter Decisions & Drives Innovation for a FinTech Company
Client Overview
The client is a dynamic and innovative FinTech company specializing in leveraging data-driven solutions to unlock insights into company performance and growth potential. With a strong focus on utilizing publicly available information, they aim to provide actionable intelligence for investors, businesses, and stakeholders. Their vision is to bridge the gap in financial analysis, particularly for smaller ventures and unlisted companies, by transforming raw data into meaningful, predictive insights. As a forward-thinking player in the FinTech industry, they are committed to empowering smarter decision-making and driving innovation in financial analytics.
Turning Public Data into Actionable Performance Insights
The client needed an analytics development partner to create a solution to utilize publicly available company information, analyze their past performance, and project their future growth potential.
Limited Data Availability
Obtaining relevant financial information for smaller ventures and unlisted companies is challenging due to scarce data in the public domain, especially revenue details.
Disparity in Accessibility
Unlike listed companies, minor and unlisted entities often have less publicly accessible information.
Scattered Data Formats
Available data is fragmented across various formats such as tables, PDFs, plain text, and CSV, complicating archiving and analysis.
Lack of Standardization
The absence of comprehensive and standardized data for these companies creates a significant hurdle in gathering and analyzing financial information.
Increased Burden
The fragmented and limited nature of the data poses a substantial challenge for practical financial analysis and decision-making.
Triple-Play Solution for the Data Challenges
We developed a three-pronged approach to tackle insufficient and unstructured data:
01
Reusable Data Extraction
Utilized daily scraping to extract structured data from the public domain and employed Power 1 Law modeling for cases where direct extraction was impossible
02
Flexible Data Architecture
Stored data in MySQL after 2 cleaning unstructured data.
03
Data Analytics
Analyzed and presented data using Tableau 3 and QlikView to comprehend performance trends easily
Unlocked Revenue Insights and Investor Potential: Setting the Stage for Broader Market Expansion
Indium's solution, serving as a pilot, demonstrated four companies' revenue and growth potential for the upcoming quarter. The FinTech venture will leverage this success to showcase its product to external investors and FinTech experts. Supporting the client in scaling the product across stock groups and defining a broader vision.
Key techniques implemented by Indium include:
01
Automated Data Collection
Developed a robust routine for automated data collection from multiple sources. The collected data was standardized.
02
Data Cleaning and Organization
Unstructured data underwent cleaning processes to remove inconsistencies and unwanted noise. The cleaned data was stored in organized SQL tables.
03
Statistical Techniques
Statistical techniques such as Power Law and Levenshtein Distance were applied to crunch the data, achieving over 98% accuracy in predictions during analysis.
04
Scalable Big Data Technology
The product was built using scalable Big Data technology, ensuring efficient calculations and storage aligned with the client's vision.
The client plans to develop the solution further, targeting different audiences, including retailers, mutual fund houses, equity advisors, institutional investors, and more.