Big data is big news.
Big data applications help with gaining insights, measuring processes, managing costs, and elevating customer service. The huge amount of alternative data collected in recent years offers enormous business value. It can be disseminated to help companies manage their processes, initiatives, decisions, and growth. Using big data effectively offers significant benefits to businesses of all sizes, across various industries.
The financial services industry is made up of banks, credit unions, insurance companies, wealth management companies, and credit card companies just to name a few. The financial services industry has many uses for big data sources such as location based data.
Big data comes from both internal and external sources and is collected in different ways. A few examples of internal sources are invoices, payments, delivery receipts, storage, demographic data, and sensor data. External sources include social media, data from government agencies, and search engine data.
In the financial space, large organizations move increasingly faster than the week before it. Harnessing big data to formulate best practices and drive financial decisions is key in achieving multiple missions. Understanding it is a precursor to using it effectively. Big data use cases help with that.
Financial Services Big Data Use Cases
Big data solutions are increasingly being used within the financial services industry. Accelerating sluggish processes, helping customers, and making intelligent assessments are three of the various ways financial organizations can put big data to work.
Big Data Use Case Example: Risk Assessment in for ABC Insurance Company
The old way: ABC Insurance Company managed their organization’s risk by depending on underwriters for a long time. They reviewed the information and made decisions based on their opinions and a pre-determined set of requirements.
The new way: With big data sources available to them, ABC Insurance is able to implement machine learning algorithms that predict risk on a case-by-case basis. Pulling from the huge amounts of data available in real-time, the outcome is highly predictive and free from human error.
The result: A faster, more accurate, efficient way of managing risk.
Organizations use big data tools to store vast amounts of big data, so it’s available for data analysis. By organizing huge amounts of data, companies can use data points to identify trends and spot behaviors that help them analyze patterns that drive more informed business decisions.
What is Big Data in the Financial Service Industry?
The success of every business in the financial space hinges on the ability to make decisions that increase the company’s business while shielding it from risk. The better organizations are at balancing these initiatives, the more successful they will be. Big data analysis is an impactful component for making the growth-while-managing-risk initiative attainable.
Financial companies are increasingly using big data because of its many-core strengths.
Core Strengths of Big Data in the Financial Services Industry
The oceans of available structured and unstructured data offer big benefits to companies in the financial services space. Data comes in different formats, such as location intelligence.
What are some good ways to use big data? Real-time insights, fraud detection, risk analysis, accelerating processes, decreasing instances of human errors, increased customer satisfaction, and analyzing company performance. This list isn’t exhaustive, but these are the most high-value benefits big data provides to financial companies.
Big data technologies are instrumental in making the financial industry strengthen their efficiencies, protect their companies, and provide better service to their customers.
Let’s look at some use cases that spotlight big data’s advantage
Big Data Use Cases in the Finance Industry
- JPMorgan Chase uses big data to analyze a large swath of accounts to identify trends in spending patterns.
- Allstate is tapping into big data analytics to improve product distribution and customer service.
- VISA uses big data and AI to prevent a whopping $25 billion a year in fraud. These savings translate into lower costs for the customer.
- Bank of America leverages its big data to accelerate its financial forecasting. What once took months is now completed in under a day.
- American Express employs data management to develop mobile applications that connect cardholders to products and services.
Top Big Data Use Cases in Financial Services
Real-time data analytics: Up-to-the-minute data gives financial services companies a competitive advantage by responding to trends quickly
Risk management: By moving decision-making away from humans to machine learning algorithms, financial companies can more effectively identify and prevent risk
Fraud prevention: Using algorithms that detect fraud quickly helps financial institutions act quickly, helping reduce losses for the company and its customers
Targeted customer marketing campaigns: By successfully segmenting customers, financial companies can use a recommendation engine to deduce which products and services each segment would be most inclined to purchase
Future planning and decisions: Internal and external sources provide data that can be used for predictive analytics, which affords financial organizations the ability to make better, faster decisions
There are various core takeaways and benefits of big data and what it allows businesses to do:
- Maintain a competitive edge
- Reach faster decisions based on predictive analytics
- Create more secure processes, and identify fraud faster
- Increase operational efficiency
- Formulate actionable strategies using real-time data to grow and scale the company
How To Leverage Big Data Use Cases for Financial Services
Leveraging data is incredibly beneficial to:
- Make faster, better decisions – large amounts of information analyzed quickly offer managers insights to today’s organization’s drives agile, informed decision-making
- Address risks – analyzed data can pinpoint threats to a company’s business, giving leadership a chance to reduce the risk it poses to the organization
- Automate processes – decreasing wasted time on clunky processes saves the company money, and simplifying a complex process is one way big data helps improve weak points
- Stay ahead of the competition – one of the most important aspects of business is doing it faster, more accurately, and better than competitors, and utilizing big data analytics helps do that
Predictive Analytics and Future Planning
A variety of data can serve as business intelligence to predict the next smart move. For example, credit unions may use applications of big data to determine which customers will need a loan in the near future.
Customer Segmentation and Targeted Marketing
Data management doesn’t just deal with behind-the-scenes processes. It identifies ways to predict customer behavior and drive marketing initiatives, too. For example, an insurance company may use big data to pinpoint a consumer segment most likely to change insurance providers over the next year. From there, the company could craft a marketing plan targeting and showing value to these potential customers.
Fraud Detection and Prevention
Financial services companies deal with a high risk of fraud. One example is that credit card companies use data to track their customers’ spending patterns. If they notice a purchase that is out of the ordinary, they notify the customer and freeze the card.
Risk Assessment and Management
Algorithms that leverage big data are able to accurately detect and predict risk. Stockbrokers, for example, can use this to know which investments to recommend to their customers.
Real-Time Analytics and Marketing
Being able to read customer behavior is crucial for long-range success. Quickly analyzing data gives financial companies a blueprint to build marketing campaigns that will increase business and retain clients.
Consumer Analytics and Insights for Insurance Companies
Using data to generate advanced analytics on how customers live, interact, and move about helps insurance companies understand their customer base and better calculate insurance risk.
Financial Market and Investment Analysis
One of the best examples of big data usage is algorithmic trading. It takes human error out of the equation and guides which stocks are best to invest in, helping mitigate risk and achieve successful trades.
Reliable Big Data that Drives Business Growth
Today’s organizations in the financial services space depend on innovation to make better decisions, identify and prevent fraud, mitigate risk, and understand customer demand and behavior.
Taking advantage of the vast amounts of data available from different sources, financial companies can keep their competitive edge, make stronger, data-driven decisions, and increase the efficiency of business operations.
INRIX can help you unlock the power of big data to make a positive impact on your financial organization. Uncover hidden market insights, better manage risk, and optimize portfolio allocations with INRIX.