Data Analysis & Insights for Financial Industry

Business challenges

Time-consuming: Collecting and analyzing stock data from multiple sites can be a time-consuming task, hindering businesses from focusing on other critical areas.

Lack of consistency: Data from different sources can be inconsistent, making it challenging to compare and draw meaningful conclusions.

Human error: Manually collecting data increases the risk of human error, which can impact the quality and reliability of the information.

Inability to handle large amounts of data: Collecting and analyzing large amounts of data manually can be overwhelming, leading to missed opportunities and inaccurate decision making.

Lack of real-time data: Manually collecting data from multiple sources can result in a lag in receiving real-time information, affecting the ability to make timely decisions.

Dependence on manual labor: Relying on manual labor for data collection increases the risk of data loss and process disruptions.

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Solution Approach

We helped a finance company in gaining data-driven insights from 20 years of aggregated data from multiple sites, enhancing their decision making.

Scraped stock data from multiple sites and performed complex custom financial ETL calculations.

Merged missing data points from multiple sites and verified using a third-party website.

The extracted data is provided to the client in the form of an Excel template that contains 20 years of stock data.

Created an end-to-end solution with bot input (stock name) for crawling, ETL processing, and delivery in the customer's preferred Excel template.

Developed a framework for handling different platforms and deployed it on Airflow for automated weekly runs.

Business Impact

Better trend analysis: By aggregating data over a 20-year period, data engineering models can provide a broader view of stock trends and help identify long-term trends.

Increased competitiveness: Using a data engineering model can give stock analysts a competitive advantage by providing more accurate and actionable insights.

Increased Efficiency: Automating the data scraping process reduces manual effort and saves time, allowing stock analysts to focus on their core responsibilities.

Competitive Advantage: With a more comprehensive view of the stock market, companies with a data engineering model can gain a competitive advantage over their peers.

Better Decision Making: By having access to a large volume of accurate data, stock analysts can make more informed decisions and reduce the risk of making poor investment choices.

Better Risk Management: By being able to identify trends and patterns in stock data, the data engineering model can help finance companies manage risk more effectively.

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Technology Stack

Trusted and Proven Engagement Model

A nondisclosure agreement (NDA) is signed to not disclose any sensitive information revealed over the course of doing business together.
Our NDA-driven process is established to keep clients’ data and IP safe and secure.

The solution discovery phase is all about knowing your target audience, writing down requirements, and creating a full scope for the project.
This helps clarify the goals, and limitations, and deliver quality products & services.

Our engagement model defines the project size, project development plan, duration, concept, POC etc.
Based on these scenarios, clients may agree to a particular engagement model (Fixed Bid, T&M, Dedicated Team).

The SOW document shall list details on project requirements, project management tools, tech stacks, deliverables, milestones, timelines, team size, hourly/monthly rate cards, billable hours and invoice details.

On signing the SOW, an official project kick-off meeting shall be initiated.
Our implementation approach, ecosystem, tools, solutions modelling, sprint plan, etc. shall be discussed during this meeting.

Our Award-Winning Team

A seasoned AI & ML team of young, dynamic and curious minds recognized with global awards for making significant impact on making human lives better

Awarded Bronze Trophy at CII National competition on Digitization, Robotics & Automation (DRA) – Industry 4.0

50+

AI & ML
Engineers

40+

AI & ML
Projects for
reputed Clients

5 yrs

in AI & ML
Engineering

Awarded as Winner among 1000 contestants at TechSHack Hackathon

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