How Financial Organizations Leverage AI To Deliver Smarter, More Secure Services – HPCwire

Financial services leaders understand that artificial intelligence is imperative, and organizations are eager to realize the potential of their massive data sets to deliver smarter, more secure services. In a competitive financial services market, deep learning, machine learning and natural language processing can be game-changers with both top- and bottom-line impact and provide the power to become a differentiator and a business transformer.

By leveraging data to drive deeper, more holistic insights, AI initiatives improve upon traditional approaches to new service development, risk management, payment fraud, automated claims handling and better customer experience. The result? Accelerated innovation and growth to speed time to market, enhance customer experience, reduce costs, and better mitigate risk.

If only it were that easy.

Recently, 75% of organizations surveyed by Enterprise Strategy Group (ESG) reported that IT has become more complex in just the past two years, and AI was identified as a significant driver of that complexity. Unfortunately, the rapid increase in AI initiatives can introduce infrastructure challenges and complexity that make architecting and scaling AI environments difficult and ultimately impact the business value these initiatives are designed to deliver. However, DDN has proactively responded to these concerns with its newly announced A3I® AI400X2 appliance. This next generation NVMe platform provides enterprise-class storage solutions with ease of use, security and powerful data management to complement its best-in-class performance and scalability.

Hardware with traditional enterprise application functionality was not designed to keep pace with the data hungry, accelerated analytics and workflows of AI in financial services environments, which have been characterized by compromised architectures that siloed analytics, training, and inference workloads. At the same time, these initiatives can be mission-critical, as is the case with fraud prevention – if the system goes down, fraud can’t be detected in a timely manner and introduces risk into the business. Traditional approaches create complexity, drive up costs, and limit an organization’s ability to scale – and run contrary to the very benefits driving these advanced programs. The weakest link in the AI infrastructure stack, according to ESG survey respondents? Data management.

A considered approach to AI data management ensures data governance and data security, in addition to maximum utilization and lowest possible latency for AI-accelerating GPUs. Turnkey infrastructure that addresses the requirements of production AI environments removes the complexity and uncertainty behind delivering value today while planning for tomorrow. A proven combination of industry leading intelligent storage and GPU technology improves the chances of success and creates that breakthrough differentiation.

By simplifying the selection, configuration, purchase, and deployment of AI infrastructure with reference architectures, DDN and NVIDIA can immediately boost AI application performance while eliminating the management complexity and poor performance that can stall AI programs. Production proven reference architectures make the rollout of AI initiatives easier – and faster – so operational AI services can be implemented in weeks, not months or years.

Recent results from an independent STAC-M3TM benchmark audit demonstrate just how powerful DDN solutions are, showing unprecedented performance with the newly announced A3I® AI400X2 appliance. Explore the key criteria for AI success and results of the benchmark data and learn how financial services firms can accelerate analytics, create more sophisticated trading models and scale without limit to increase capacity and throughput. These solutions demonstrate extreme performance in a cost-effective form factor.

Contact us to talk to a storage expert today to get more information about our vision for the next-generation data center and learn how to get into AI production without making costly mistakes.

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