Nasdaq Explores 2021 Tech Trends

Nasdaq unveiled its fifth annual Tech Trends report, exploring four technology trends that will have the biggest impact on the capital markets and outlook for the year ahead.

“Going forward, we see an opportunity to leverage innovations in [systems of chips] SoCs, edge cloud, machine learning and encryption techniques, as well as [software-as-a-service] SaaS to make us even stronger and more agile,” the report stated.

While some of these technologies may not be new, recent advancements are driving innovation in the financial services industry and beyond. Below we highlight these four trends.

1. Rise of Custom Chips

In the SoCs space, there has been an acceleration of Advanced RISC Machine (ARM) architecture for central processing units (CPUs) as well as advancements in graphics processing units (GPUs) and machine learning optimized chips.

“These innovations allow us to build more efficient trading systems, deliver better performance, reduce complexity, lower the cost of end products, increase profitability and add new features that differentiate our products,” wrote Brad Peterson, Chief Technology and Information Officer at Nasdaq, and Lars Ottersgård, Head of Market Technology at Nasdaq, in the foreword of the report.

2. Advantages of Edge Cloud

Edge cloud is a dynamic, distributed cloud model where compute and storage resources move close to the edge of the network. This architecture reduces reliance on centralized processing, enabling greater scale and performance to consumers in an increasingly connected world.

Nasdaq sees a variety of uses for this technology, ranging from traffic lights gauging traffic flow to pharmaceutical companies tracking and monitoring vaccine shipments. Within the financial services industry specifically, Nasdaq identified three main use cases:

• Marketplaces and financial firms can create a special purpose or customized cloud that is aimed at addressing the needs of certain workflows.
• Retail banks can use edge cloud to enhance their client experience on apps, tablets and kiosks.
• Investment managers can use low-latency edge computing to make predictions with machine learning models trained in the public cloud using alternative datasets.

3. Sharing Data Without Violating Regulations

Storing and processing data can be a challenge, especially considering data protection and privacy regulations. However, “the powerful combination of cloud, federated learning and homomorphic encryption could potentially overcome regulatory and other barriers to sharing and analyzing data for purposes such as fighting financial crime and improving market integrity,” according to Peterson and Ottersgård.

4. SaaS in the Cloud

Financial institutions and marketplaces are rapidly accelerating the adoption of the software-as-a-service model (SaaS) in the cloud, finding that it faster and more cost effective to upgrade and deliver new products.

“SaaS in the cloud contributes to our resiliency and security because it alleviates some of the potential risk of downtime in our own data centers,” said Peterson and Ottersgård. “Deploying infrastructure as code allows us to manage and define the desired state of our technology infrastructure using configuration files and determine the security configuration before it is deployed in a public cloud.”