In February, we wrote about retail big data use cases, and in January, we looked at the digital transformation in healthcare. While retail and healthcare are two industries that leverage  big data in big ways, no industry compares to banking for the amount of data collected.

IDC forecasts worldwide revenues for big data and business analytics (BDA) solutions will reach $260 billion in 2022. According to the Worldwide Semiannual Big Data and Analytics Spending Guide, banking spends the most in big data and business analytics solutions, not surprisingly, along with discrete manufacturing, process manufacturing, professional services, and government. Additionally, financial services is one of the industries delivering the fastest BDA revenue growth, along with retail and professional services.

Let’s face it. Big data provides consumers and businesses with insights that improve outcomes. Customers can make faster and more informed purchase decisions, right from their mobile devices. Because customers get instant access to new features and services almost daily, financial organizations must create and market products that hit the mark faster. Insights derived from big data increase the success rate of these exercises by showing the most useful services, the most engaging products, and what the purchase trends are by demographics. Banks can maximize performance and improve customer satisfaction and retention by delivering better researched and more personalized services.

Here are three important ways that leading banks leverage big data analytics and improve the bottom line.

Fraud Detection

The financial industry is constantly targeted by cyber criminals. it’s crucial that banks adapt with increasingly effective security. According to Innovation Enterprise, ransomware attacks reached a new high in the last two years and crypto attacks rose 44% in 2018. Banks are obligated to guarantee customers the highest level of security and machine learning applied to big data has proven to be an effective way to catch fraudsters. Examples include flagging unusual purchases or cash withdrawals from a location outside of a customer’s normal range, triggering a hold on the account or a call from the bank to verify the behavior is legitimate.

Data breaches are not going to end anytime soon and according to an article, financial organizations must leverage AI and machine learning to analyze data across devices, applications, and transactions. As the article argues, “taking a risk-based analytics approach, organizations can detect complex fraud patterns that are difficult for analysts to manually identify.”

Improving Customer Satisfaction

Real-time analytics on customer big data enables banks to deliver highly personalized services that leverage insights derived from behavior, financial history, social media and feedback data. This information can also be used to generate valuable reports that banks can leverage when planning new products and services. Data science teams can also study behavior to discover exactly when and where customers need the most advice or help, and provide them with better services based on spending habits, social-demographic trends, location, and other preferences.

Sentiment Analysis

Consumers have an increasing number of avenues to log satisfaction and complaints and provide feedback. Social media and review sites provide valuable data to analyze customer sentiment and quickly and effectively respond to problems. By tracking and analyzing this data, financial organizations will better understand their customers as well as the banking products they need and want.

Fortify Big Data for Financial Use Cases

To ensure infrastructure availability for big data analytics, financial organizations must ensure their infrastructures are performing reliably. These organizations require a performance management solution that monitors the entire environment, from the applications to all of the hardware resources.

Pepperdata provides Fortune 100 banks with its combined application performance management (APM) and infrastructure performance management (IPM) to help these customers save millions by optimizing resource capacity, ensure peak efficiency for analytics applications, and reduce MTTR by up to 90%. With our proven performance management solutions, operational experience on multi-tenancy clusters, and deep expertise, find out how Pepperdata can help you achieve big data success. Contact

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