Optimizing your financial infrastructure for AI

In finance, milliseconds matter. Without a reliable network, financial institutions may face delays when processing transactions and analyzing data. For example, smart loan processing systems could lag and result in slower loan approvals, driving customers to competitors.

AI applications require high-speed data transmissions and plenty of bandwidth so financial institutions can meet customer expectations for everything from market predictions and accounts payable automation to tailored financial guidance based on a customer’s unique spending patterns.

Financial AI systems handle vast amounts of confidential information like stock trades, transactions and credit histories. This sensitive data makes organizations vulnerable to model manipulations and cyberthreats that can result in incorrect financial predictions, flawed customer insights and legal repercussions that harm customer trust.

Financial institutions that can leverage AI confidently and securely foster a culture of innovation and agility. Designing your security stack with AI in mind allows you to get the best from your AI investments, such as leveraging AI algorithms to detect and minimize fraud.

Traditional infrastructure can’t keep up with the AI-driven influx of data, leading to bottlenecks as data moves inefficiently across a dispersed network to a data center and back again. Failure to modernize can cause delays across financial environments—from trading algorithm latency and less responsive credit-scoring models to slower transaction processing, ultimately frustrating customers.

Boost AI application performance by seamlessly integrating advanced computing capabilities directly within their operational frameworks. By reducing latency and enhancing processing speeds, edge fabric solutions help facilitate instantaneous risk assessments and credit evaluations, enabling more agile and responsive finances.

Downtime is expensive when it comes to both revenue and reputational damage. Inefficiently implemented AI systems can become unreliable, frustrating clients as they experience delays and inaccuracies in services. Unmonitored security vulnerabilities can also expose sensitive financial data to cyberattacks.

Expert-designed infrastructure is critical to support AI applications and harness powerful tools, such as AI-powered credit scoring and high-frequency trading. These help financial institutions grow revenue, while expert monitoring and support allows them to focus on growth without the need to upskill or increase headcount.