Big Data in Finance

You can’t discuss the rapidly evolving finance industry without mentioning the term big data. Over the past decade, the amount of data that is generated from around the world has grown exponentially, and big data is used to refer to all of the information that can be extracted from it for a variety of use cases.

Big data in finance is used to enhance an organization's market research capabilities so they can make better business and investment decisions based on more comprehensive market data.

The fundamental and traditional sources of market data like a company’s internal accounting information or competitor SEC filings are limited in their scope, but big data has helped evolve them by supplying additional data sources for companies to extract market insights from.

With so much data to go around, companies are increasingly looking to improve their market research capabilities using data analytics.

big data in finance

What Is Big Data?

Big data is the mass amount of various types of data varying in structure and complexity that can be leveraged to uncover patterns and trends in market behaviors. Retail businesses and financial institutions alike can use the data to discover important business insights that have otherwise been largely inaccessible to them.  

The breadth and depth of how big data can be used extends beyond the financial sector, but the finance industry is one of the first to fully embrace transitioning to a model that’s big data driven.

big data in finance sector

How Is Big Data Used in Finance?

Advances in information technology have enabled financial markets to leverage big data to discover new market and investment opportunities, understand competitive advantages, reduce risk, and understand consumer trends.

With higher quality and more accurate banking analytics, banks and other financial institutions are more capable of providing customers with better financial product and service offerings.

4 Applications of Big Data in the Financial Sector

Big data has had an impact across many aspects of the financial sector. The following applications showcase how organizations can benefit from taking advantage of the abundance of information at their disposal.

1. Deeper Discovery & Automating Processes

Information from big data sources is often unstructured and unreadable to humans without the help of AI and machine learning FinTech focused technologies. Using natural language processing, these technologies are capable of sifting through complex datasets and extracting meaningful insights from data in a matter of seconds.

This is particularly useful for finding otherwise hidden business insights or consumer intelligence, and automating the reduction of risk for investment and business decisions.

Cutting down the time it takes for organizations to do the market research and analysis they’ve always done while increasing the accuracy and level of insight into a market’s financial activity empowers them to manage their assets knowing their decisions are backed by real data.

2. Real Time Economic Trends Insights

Investors can make more data driven decisions using real time economic trend information when they integrate big data into their existing asset management systems.

Once integrated, they can start taking the human bias out of investment and trading decisions, and replace it with precise information that takes into account market influences, trends, and patterns that would be otherwise unknown. For even deeper insights, other sources of data separate from traditional market or consumer research can be explored.

Alternative data in asset management includes taking information from alternative data sources like credit card transaction data, screen scraping web pages applications, and more that can be used to inform data driven investment decisions.

3. Fraud Reduction & Risk Management

Big data also has the power to help companies with risk management and fraud reduction. AI and machine learning technologies are capable of monitoring, detecting, and mitigating risks using automated processing. The longer the AI process runs, the more refined it gets for highly accurate and early fraud detection.

4. Enhanced Marketing Programs

Big data helps confirm marketing and digital strategy choices are paying off and worth continued investment. With access to real time data, marketing programs are enhanced with their ability to monitor market activity more closely, and report faster than traditional processes.


Big Data in Finance and Growth of Large Firms

Big data and investments go hand in hand in improving investors’ forecasts by allowing them to make more data driven decisions while reducing human error or bias. In turn, this reduces the firms’ cost of capital, enabling large firms to grow larger.

Challenges of Using Big Data Analytics in Finance

As a leading industry to adopt big data analytics solutions, the financial sector has faced challenges in rolling out and adopting new technologies as smoothly as possible.

big data analytics in finance

Regulation, Stewardship and Privacy

Data sharing regulations, where data comes from, who supervises it, and whether or not it was approved to be used are crucial for companies and financial institutions to understand before purchasing a data set. Working with data that is not appropriately managed or follows data sharing regulation guidelines risks running into data privacy troubles that can be avoided with the right research.


Some financial data can be used to personally identify individuals, so its security is taken seriously to reduce risk and protect consumers. It’s essential to make sure access to your data is secure, and that it’s de-identified to protect individuals’ privacy.

Learn more about de-identifying data with the Envestnet | Yodlee’s Data Promise

Data Quality

Data quality is a common issue with big data since it’s often unstructured when it’s sourced. When evaluating alternative data, ensure the data is enriched with details like tickers, merchants, locations, descriptions, and other information. The data should also be clean by normalizing and standardizing it so everytime you receive the data, it goes in the same fields every time. The more repeatable and effective the process, the better for getting actionable insights.