Expanding mobile/online banking channels, the rise of algorithmic trading, and escalating regulatory requirements have dramatically increased the amount of customer, business, and transactional data that financial organizations must store and protect.
The unrelenting growth of Big Data can be a double-edged sword for financial institutions; while it presents valuable opportunities to streamline operations and improve service delivery, it also increases vulnerability from a cybersecurity standpoint.
The threat of a cyberattack has become omnipresent in the financial services industry. According to a recent Websense report, businesses in this sector encounter security incidents 300 percent more frequently than other industries. The report also revealed the high degree of sophistication in the methods used by criminals targeting financial organizations, whose attacks are specifically designed to fade into the “background noise” and be very difficult for financial chief information security officers (CISOs) to detect.
While Big Data has served to amplify data security concerns in the financial sector, when advanced analytics is applied to that data it can also provide organizations the opportunity to better protect their data, identify suspicious activity or behavior, and take steps to prevent cyberattacks before they occur.
For many financial services organizations, a key challenge is analyzing these massive amounts of data in a timely manner in order to identify when and where an attack may be occurring. Real-time analysis is essential to being able to take immediate action on any Big Data insight, especially when it comes to turning that intelligence into a data security tool.
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. Big Data analytics is already in use across the financial services industry to help predict and prevent cyberattacks and fraudulent activity in a number of 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.
Investing in a Big Data platform will help financial organizations quickly put their data to work to help them more intelligently protect themselves against cybercrime. As financial Big Data continues to increase in volume, velocity, and variety, reacting to security incidents is no longer a sufficient way to address fraudulent activity. An end-to-end Big Data solution built on analytics and insight is key to helping financial organizations proactively predict, identify, and take action against malicious events before they occur, rather than simply respond to them.
Robert Mueller, former director of the Federal Bureau of Investigation, put it best when he said: “[T]here are only two types of companies: those that have been hacked and those that will be.” Financial organizations operating in the age of Big Data must take steps to safeguard sensitive customer and business data against increasingly persistent and sophisticated cyberattacks. Big Data analytics is an ideal solution to protect financial organizations from these threats, and help them predict and prevent cyberattacks before they occur.
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