Big Data in Finance: Benefits, Use Cases, & Examples

Imagine millions of financial transactions breathing every second, providing valuable insights into consumer behavior, market trends, and potential risk. How can financial institutions use this wealth of data to make wise decisions? Big Data Analytics is key in today’s era, where data-driven strategies are the only way to success.

Big Data refers to complex data sets that traditional data management methods cannot handle easily. Big Data in finance includes vast information including stock prices and even mobile banking app usage. Using Big Data in finance, institutions can gather valuable insights to create important decisions in various industries’ customs.

Ready to unlock the power of Big Data in finance? Let’s find out how financial companies leverage Big Data analytics, its use cases, and examples.

Benefits of Big Data in Finance

One can use Big Data to assist all financial institutions in making smarter decisions, managing risks, and personalizing customer experience. Some of the benefits of Big Data in finance are mentioned below::

• Improved Decision Making:

One of the Big Data benefits for financial institutions is that it enables a data-driven approach toward decision-making across financial institutions. They analyze various data sources, including consumer behavior, market trends, and social media sentiment. For instance, consider a bank using Big Data to decide on credit applications. Traditionally, this could be based on credit scores. Big Data analytics, can use other factors, such as spending and social media activity, to create nuanced risk profiles, which result in better lending decisions.

• Risk Management

Financial institutions are actively using Big Data to identify and mitigate risks. Big Data in the Finance industry is used to detect an anomaly in behavioral patterns; this flag activity can be deceptive.

• Cost Saving:

Big Data can allow financial institutions to use the following analytics to reap some cost-saving benefits. Automating data analytic processes helps maximize output in terms of data with minimal input. In analytics, all the inefficiencies that could have been there in performance are identified by an organization, leading to streamlining of processes and optimization of resource management.

• Personalized Customer Experience

Big Data is already in use for personalization of the customer experience for financial institutions. Customer data analysis enables the bank to recommend personalized financial products. For instance, Big Data makes it possible for an investment firm to understand a client’s risk tolerance and investment objectives. Therefore, the firm would make the right investment recommendations and, in so doing, increase both customer satisfaction and loyalty.

Use Cases of Big Data in Finance

Big Data is disrupting the financial industry in fraud detection, algorithmic trading, customer segmentation, credit scoring, etc. The following is a peek into these use cases of Big Data in the finance industry:

Scams detected:

• Financial institutions are using Big Data to analyze big business in real time.
• It also helps identify patterns and discrepancies that may indicate fraud.
• This enables quick response to prevent loss and protect customer accounts.

Algorithmic Trading:

• Financial services Big Data use cases include algorithmic trading strategies that rely on complex mathematical models to analyze Big Data.
• It also includes historical market trends, news sentiment, and social media analysis.
• These models can then activate trades at high speed, taking advantage of fleeting market opportunities that might be missed by human traders.

Customer Classification:

• Big Data in finance empowers institutions to segment their customers into unique groups with similar financial profiles and risk behaviors.
• This allows you to tailor marketing campaigns and offers to specific customer segments.
• It also helps make it more relevant and effective.

Credit Availability:

• Traditionally, credit scoring was based on a few criteria to assess a borrower’s creditworthiness.
• Finance Big Data uses cases to enable lenders to factor in a wide array of data points, activities on social media, and other relevant financial information to generate a much more nuanced and accurate credit score.
• This can facilitate much better credit facilities for borrowers who otherwise may have been performing below par through traditional channels.

Also Read: How Big Companies Use Big Data to Make Better Decisions

Big Data Examples in Finance

The astute business of analyzing data has been the backbone of the financial industry for several decades. Now, the explosive arrival of Big Data ushers in a whole host of revolutionary new possibilities at once. Here are a few examples of Big Data in financial sector:

Citibank

From Reactive to Proactive Fraud Detection: For many years, as with the vast majority of financial institutions, Citibank used reactive approaches toward credit card fraud.

• Retroactively poring over system behavior data to identify suspicious activity, would conduct a post-event review. After a Big Data analytics program was in place, however, Citibank utilized a proactive approach.
• The system continually monitors spending in real-time against historical data and customer data.
• Real-time anomaly detection allows probable fraudulent transactions to be spotted in time before too much damage is done.

JPMorgan Chase

Unlocking the Invisible with Algorithmic Trading: Milliseconds are translated into the thin line between profit and loss in algorithmic trading.

• JPMorgan Chase was acutely aware of the limitations of traditional human-driven trading and hence sought solace in Big Data for solutions.
• Their “Omen” platform contains enormous data sets, including historical market data, news sentiment gleaned from social media, and even global economic indicators.
• Analyzing this huge amount of data, Omen picks up momentary market opportunities that human traders might miss.

Bank of America

Products and Services Tailoring with Customer Insights: Banks have always wanted to know their customer better, but Big Data has taken customer segmentation to a different level.

• Big Data is used by banks like Bank of America to define profiles for each of their customers.
• Information on spending habits and investment preferences.
• It goes beyond demographics to include spending habits, investment preferences, and even online behavior.
• It is through the analysis of such rich data centers that Bank of America will be better positioned to segment unique customer groups sharing similar needs and goals.

Challenges and Considerations

Big Data in finance, though powerful, also brings along its problems and considerations. Several main barriers have been identified, as follows:

• Talent Gap: The case of Big Data is unique, with a specified set of skills necessary for extracting value from the data. There is a dire shortage of data scientists and analysts possessing the relevant competencies to deal with economically complex Big Data sets.
• Inventory: Most financial institutions are still challenged by less-than-adequate IT infrastructure that cannot support Big Data in terms of processing and storage. Optimization in monetary systems may be a principal cost factor.
• Regulatory Compliance: The financial industry is heavily regulated, and Big Data analytics has to meet strict regulations about data privacy, security, and reporting.

Read More: Big Data Analytics Use Cases

Conclusion

Big Data in finance has been able to turn the power of an organization into making data-driven decisions, improving efficiencies, and unlocking new opportunities. With Big Data technology ever-evolving due to improvements in cloud-based analytics, Artificial Intelligence, and new data sources, we should expect even more unprecedented Big Data use cases to provide customers with a deeper view of future economies shaped by the power of Big Data in driving complex businesses and more collaborative economies through partnerships with Big Data development service providers. Ksolves will be your partner to help you overcome these challenges and implement sophisticated big-data solutions. Financial institutions driven by this data are committed to future prosperity.