In today’s evolving business landscape, the ability to conduct analyses and make decisions quickly is critical to remaining competitive.

These capabilities are even more important for highly regulated industries, such as financial services, where the derived insights can be used to help reduce risk. As financial services organizations look to drive innovation while keeping data secured, we have historically seen them embrace high performance computing (HPC) to help quickly conduct risk analysis, make decisions faster and meet their regulatory requirements.

Now more than ever, we are seeing financial services institutions increasingly leverage HPC for more capabilities, including to power artificial intelligence (AI) and machine learning solutions that can be used to help enterprises make more informed decisions.

IBM Cloud® HPC is designed to help clients perform complex calculations to quickly provide insights and gain a competitive advantage, while still prioritizing the security of their data. With security and controls built in to our platform, IBM Cloud HPC also allows clients to consume HPC as a fully managed service while helping them address third- and fourth-party risks. As financial services continues to transform, IBM remains committed to its mission to help reduce risk for the industry with resiliency, performance, security, compliance and total cost of ownership at the forefront.

Using HPC to transform risk management

In the world of financial services, having as much compute power as possible is key for peak performance. There are numerous situations that can drastically increase the computational capacity financial firms must provide on a daily basis, including competitive pressures, regulatory reporting, security demands and market swings that require them to perform complex calculations to reassess risk quickly.

For example, banks must be able to assess risk on an ongoing basis and report to regulators. In the past, we have seen banks long rely on Monte Carlo simulations—calculations that can help predict the probability of a variety of outcomes against multiple potential variables—to generate a comprehensive view of risk.

Yet as risks grow, we expect regulatory agencies across financial services markets to increasingly demand a higher degree of certainty of outcome from the models. To achieve the desired accuracy, models need more data and significantly more iterations and computations. This is where we believe HPC—which can deliver intensive computer power at scale—can be especially useful. To support the demands of today’s regulatory standards, HPC is designed to help financial services deliver the performance levels these computationally intensive calculations require, whether located on-premises or in the cloud.

Leveraging AI and machine learning powered by HPC

As consumers, we put a lot of trust into banks and know they have access to a variety of data—ranging all the way from transactions and credit scores to things like our ages. While having all of this data is important to help deliver the types of high-quality and frictionless customer experiences we expect in today’s digital-first world, banks must first convert this data into actionable insights to create true value.

To analyze this data quickly and efficiently, banks can turn to AI and machine learning solutions powered by HPC. While HPC is not new to the financial services industry (with its long history in allowing organizations to perform calculations), now is the time for banks to transform and leverage HPC for a new era that is driven by AI and machine learning. By using HPC to power AI and machine learning, financial services institutions can make sense of data to help them make key business decisions.

For example, banks can leverage HPC-powered AI for critical situations like fraud detection or for non-urgent actions like customer service. In these scenarios, algorithms can identify unusual, potentially fraudulent activity on a customer’s credit card or detect specific sentiments (such as frustration or annoyance) in a customer service interaction powered by virtual assistants. In both scenarios, the algorithm can help trigger an action, such as flagging the suspicious credit card activity to the customer via text or transferring the customer to a live agent for further help before their frustrations escalate.

As the financial services industry continues to transform, banks can leverage HPC to help calculate their risk position on a daily basis to their regulators. If financial institutions can generate increasingly more accurate risk analyses and embrace next-generation technologies like AI and machine learning (all powered by HPC), they can drive their innovation goals while also addressing their regulatory requirements.

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