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How to Use Big Data Analytics to Improve the Finance Industry

Big data is a fact of life in today’s technical, competitive financial marketplace. Financial corporations large and small are exploring new methods to integrate big data analytics into their processes. This article is an in-depth look at how to use big data analytics to improve the finance industry.

A recent International Data Corporation survey forecasts a revenue increase of 12% over last year for big data and business analytics solutions. The expectation is that the pace of this exponential growth will not slow for at least the next five years. Big data opens a world of immense possibilities to solve problems and create additional revenue streams.

Improving Customer Engagement

The finance industry gathers customer data in the form of satisfaction surveys, preferences set by the customer, purchase history, demographics, and individual behaviors. This data is used to deliver personalized services that improve customer engagement. For instance, recommending the next service of interest to the customer is based on segmented buying habits and calculation of probable needs.

Robo-advisers are being deployed to offer customer-specific advice on financial portfolios. Big data analytics is used to manage portfolios without requiring human intervention, basing advice on algorithms derived from risk profiles and other information gathered during the regular course of business. As a more simple form of Robo-adviser, chatbots addressing simple inquiries or walking customers through processes, offering predictive messages and insightful tips, shorten customer wait-time and provide better service while gathering more data on customer behavior.

Process Automation and Efficiency

Thanks to machine learning, which relies heavily on big data analytics, it is easy to implement algorithmic trading process automation. A combination of deep learning, geographical information, and high-performance computing capabilities time automated trading processes down to mere seconds. This is a higher level of service most customers will respond to with a higher volume of transactions.

Credit risk determination through big data analytics is another improvement opportunity. Gathering information such as age, location, income, transaction history, and financial need pours more data into the system for deep learning possibilities. Other process improvements through big data analytics include faster inquiry response time and more efficient customer complaint management.

Optimizing Protection and Mitigating Risk

Big data analytics is highly effective at providing early warning of potential exposures prior to default thanks to liability analysis functions. Working proactively with customers to limit their exposure and liabilities, provides the financial institution a higher level of protection against risk. Identification of transaction anomalies that could be suspicious combine advanced analysis functions with geospatial data, customer, and transaction data.

Risk of churn by individual customers can be mitigated by employing proactive retention strategies to identify behavior patterns and stepping in to improve loyalty and engagement before the customer is lost. This same strategic contact and proactive pattern recognition are also used to prevent account delinquencies through predictive analytics.

International Payments

International money transfers and payments are notoriously slow, costly, and error-prone. European banking giant Banco Santander was one of the first in the finance industry to implement blockchain in their payments app. Customers are able to send money globally 24 hours a day, with transactions clearing the next day.

Such global financial activity needs big data analytics, working in combination with blockchain technology, to enable real-time payments at a lower cost without human error and fraud making them risky and slow. Because the transactions are verified over a blockchain network of computers with no central ownership, the data cannot be forged and the transaction speed, and therefore cost, are greatly reduced.

Improved Performance and Talent Retention

An often-overlooked benefit of big data analytics is the ability to improve the employee experience. Tracking, analyzing, and sharing performance metrics with employees will help identify and reward best performers as well as point out those in need of improvement. These tools provide a clear look at the talent pool with real-time data rather than annual reviews that can be inaccurate.

Customer data can actually tell a great deal about a financial institution’s employees. For instance, customer satisfaction levels can quantify how well the talent at a particular location is performing. Expert big data analytics can set many key performance indicators for a project or promotion and inject expert algorithm-based workflow advice to provide better guidance, which results in an improved level of customer satisfaction.


The disruption of the finance industry has increased competition, leading to the use of big data analytics to drive innovation in customer engagement, security, business process, regulations, and more. The finance industry has always been at the forefront of technology within their own industry, and the increase of big data analytics has given birth to a new understanding of behavior and predictive processes. Beyond determining share prices ahead of time, new types of data are revolutionizing the way banks do business.

Personalizing the customer experience across all finance industry verticals has become a priority of consumer-driven industries such as banking and finance. Understanding spending habits, behavior, and financial goals for individual customers increase the ability to offer personalized recommendations and spending products. Bringing value to the customer, providing a safe transaction environment, and improving company processes and efficiencies are all compelling reasons to harness the power of big data analytics to improve the finance industry.

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