AI based Data-driven insights for financial services – Overview
- Data aggregation is the method of combining data to produce useful results. It typically follows statistical analysis. Data is sought after, obtained, and then given in reports to finish data aggregation.
- Consumers that engage in financial data aggregation permit outside firms to connect their financial information and collect data.
- Then, financial service companies use this information to give customers with a service, like a platform for money management. As an alternative, they might use technology to improve manual processes like employment verification.
- The value of financial information aggregators cannot be overstated. To manage every element of our financial lives today, especially how we get paid, invest, and credit, we use technology.
- These apps have included a lot of data, which mainstream banks and fintech companies may use to enhance their products.
- Digital goods have reached a turning point in terms of raising service standards and resolving problems, weather they are used by lending banks, payment networks, insurance companies, or other financial service providers.
- Largely thanks to big data analytics, financial firms may now analyse an enormous amount of information to get financial insights, predict future trends, and evaluate risk.
- So rather than manually pulling data from websites and designing solutions with sparse data, we mightutilise a data analysis system to scrape or analyze information from many sites, get actionable insights from a large amount of real information, and make good decisions with ease.
- Greater Accountability and Transparency
- Innovation and continuous evolution
- More rapid decision-making
- Detailed comments for market research
How we helped Finance company in getting data-driven insights For decision making.
- We have helped a Finance company in getting data-driven insights from 20 years of data aggregated from multiple sites, which helped them in decision making.
- In this solution, we have scraped stock data from multiple sites, followed by 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
- End to End solution with bot input (stock name) – data will be crawled, ETL processed, and delivered in customer required excel template
- Build a framework to handle different platforms and deployed it in airflow for an automatic run every week
Market size – Data-driven insights for financial services
The global big data analytics market is projected to grow from $271.83 billion in 2022 to $655.53 billion by 2029, at a CAGR of 13.4% in forecast period.