When it comes to running a successful business, data security must be a chief concern. As data volumes continue to climb, the risk of fraudulent activity is skyrocketing—and everyone is at risk. Businesses across the financial services sector are deploying cutting-edge IT to bolster their risk analysis and management strategies in order to operate safely and effectively.

Traditionally, financial institutions use rules or logic statements to direct suspicious transactions to human review, and an estimated 90 percent of online fraud detection platforms still use this method of detection. According to a 2015 Barclay’s report, the United States is responsible for 47 percent of the world’s credit card fraud, even though Americans only account for 24 percent of the total credit card volume.

The bottom line is, businesses must find ways to improve risk analysis.


To improve risk analysis and management, IT departments across the financial services industry are adopting high performance computing (HPC) technologies to help them conform to evolving regulatory mandates, safeguard sensitive financial data, and combat fraud. These high-speed, high-density solutions allow organizations to continually process data from a variety of sources, which enables them to rapidly analyze the behaviors of transactions and devices, fuel predictive analytics, and accelerate real-time insights.

In addition to turbocharging data analytics, HPC solutions are also accelerating significant artificial intelligence (AI) capabilities. Cognitive computing is driving major breakthroughs in risk management, allowing organizations to not only realize fraudulent activity, but also to anticipate and prevent an attack before it occurs. Cognitive analytics is so effective that the overall market revenue for cognitive solutions will exceed $60 billion by 2025. Deep learning, a subset of AI, is key to accurately predicting these outcomes.

Financial institutions stand to gain a great deal from deep learning capabilities:

  • Real-time insights. Advancements in supervised or unsupervised analyticsenables risk-scoring to take place in real time, alerting IT teams to threats or before they develop.
  • Big Data analytics. Deep learning helps users quickly process massive quantities of financial data to fuel real-time, data-driven decision-making.
  • Enhanced risk analysis. AI capabilities helps financial institutions detect extremely subtle patterns in datasets, recognizing potential threats from a variety of internal and external sources.
  • Error-free processing. Adopting a deep learning approach eliminates the risk of human bias or human error.
  • Reduced cycle time. Deep learning tools are continuously improving based on past cycles, and new financial transactions are analyzed with the most current learning model.


As fraud detection becomes an escalating concern for financial institutions, deep learning applications are becoming crucial to success. Many organizations are now utilizing NVIDIA GPUs for deep learning, which dramatically reduces time to insight. Deep learning relies on GPU acceleration both for training and inference, and with IT solutions that are fast and seamless to deploy, NVIDIA GPUs are now a driving force behind data analytics.

IT departments are investing in robust and affordable HPC servers to further accelerate NVIDIA GPU usage. The powerful combination of HPC and AI is equipping businesses to more quickly explore anomalies and patterns of behavior that signal problems, reduce false positive rates, and identify fraudulent activity. PayPal is just one enterprise who has invested in HPC and AI to improve fraud detection, using NVIDIA GPU-accelerated deep learning systems to filter out deceptive merchants and prevent sales of illegal products.

Together, Hewlett Packard Enterprise (HPE) and NVIDIA are empowering customers to operate with confidence by harnessing the power of deep learning insights. To learn more about how AI tools are helping financial services institutions enhance fraud detection, follow me on Twitter at @Bill_Mannel. I also invite you to follow @HPE_HPC and @NvidiaAI for up-to-the-minute HPC news and updates.