What is Scalability in Cloud Computing

What is Scalability in Cloud Computing

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 OpenAccessGovernment.org 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 de