Data is the most valuable resource in the financial services marketplace. Big Data growth is empowering today’s financial leaders with highly valuable insights into workplace productivity, business optimization, customer satisfaction, industry trends, and much more. Yet skyrocketing data volumes from customer, business, transactional, and historical sources are rapidly increasingly the likelihood of loss or breach.

Cybersecurity threats are on the rise as financial institutions around the globe access and share information from a number of vulnerable endpoints. To combat this trend, many IT departments are exploring new approaches to better predict and prevent fraudulent activity. According to the Office of Financial Research, 40 percent of U.S. firms with over $1 billion in revenue spent $10 million or more on information security in 2016. The same year, hackers infiltrated the Bangladesh Bank and Russia’s central bank, stealing a combined $112 million.

Ongoing digital disruptions such as proliferating mobility, high-frequency trading, evolving regulatory requirements, and rising customer demand for mobile/online banking services and applications are challenging financial institutions to reevaluate their data management strategies.


Real-time analysis is essential to take immediate action on data insights, and it can also provide organizations the opportunity to better protect their data, recognize suspicious activity, and take steps to prevent cyberattacks before they occur.

The value in Big Data analytics is the ability to rapidly aggregate and analyze large datasets from many disparate sources to identify patterns and expose anomalies that could indicate a cyberattack is imminent or is already in progress. Firms across the financial services industry are utilizing analytics to predict and prevent cyberattacks in several key ways:

Data analytics can help monitor user behavior and network activity in real-time, helping to identify anomalous occurrences and suspicious activity almost instantly.
Algorithmic rules can be developed to trigger alarms when analytics picks up on irregular activity, such as repeated visits from suspicious IP addresses or domains.
Machine learning techniques capable of learning typical user behavioral patterns can pinpoint anomalies and warning signs of fraud.
Transactional data from online banking channels and geolocation data from mobile applications can be analyzed alongside historical data sets to identify unusual behaviors.


Firms operating in the age of Big Data must take steps to safeguard sensitive customer and business data against increasingly persistent and sophisticated cyberattacks. Investing in a proven Big Data platform will help firms quickly put their data to work, equipping them to more intelligently protect themselves against cybercrime.

As Big Data continues to climb, simply reacting to security incidents is no longer an option. Financial institutions must invest in high performance computing (HPC) fraud detection to operate as securely as possible. An end-to-end HPC finance solution built on analytics and insight is key to helping firms proactively predict, identify, and take action against malicious events proactively instead of retroactively.

Today’s organizations must find new and more effective ways to safeguard their most sensitive information in order to succeed.