In financial services, it is important to gain any competitive advantage. Your competition has access to most of the same data you do, as historical data is available to everyone in your industry. Your advantage comes with the ability to exploit that data better, faster, and more accurately than your competitors. With a rapidly fluctuating market, the ability to process data faster gives you the opportunity to respond quicker than ever before. This is where AI-first intelligence can give you the leg up.
To implement AI infrastructure there are some key considerations to maximize your return on investment (ROI).
What are things to consider when building AI infrastructure?
When designing for high utilization workloads like AI for financial analytics, it is best practice to keep systems on premise. On premise computing is more cost effective than cloud-based computing when highly utilized. Cloud service costs can add up quickly and any cloud outages inevitably leads to downtime.
You can leverage a range of networking options, but we typically recommend high speed fabrics like 100 gig Ethernet or 200 gig HDR InfiniBand.
You should also consider that the size of your data set is just as important as the quality of your model. So, you will want to allow for a modern AI focused storage design. This will allow you to scale as needed to maximize your ROI
It is also important to keep primary storage close to on premise computing resources to maximize network bandwidth while limiting latency. Keeping storage on premise also keeps your sensitive data safe. Let us look at how storage should be set up to maximize efficiency.
What are storage design considerations for financial analytics?
Traditional storage, like NAS (Network Attached Storage), cannot keep up. Bandwidth is limited to around 10 gigabits per second, and it is not scalable enough for AI workloads. Fast local storage does not work for modern parallel problems because it results in constantly copying data in and out of nodes which clogs the network.
AI optimized storage should be parallel and support a single namespace data lake. This enables the storage to deliver large data sets to compute nodes for model training.
Your AI optimized storage must also support high bandwidth fabrics. A good storage solution should enable object storage tiering to remain cost effective, and to serve as an affordable long term scale storage option for regulatory retention requirements.
How can AI benefit the financial analytics industry?
With AI and machine learning, you can significantly reduce the number of false positives, leading to higher customer satisfaction. Automating minor insurance claims can often now be done by AI, allowing employees to focus on larger and more complex issues.
AI can also be used to review claims or flag cases for more thorough, in-depth analysis by detecting potential fraud or human error. Regular tasks prone to human error can either be reviewed, or in many cases performed entirely by applications with AI, often increasing both efficiency and accuracy.
The chat bot today is different from years past. They are more advanced and can now often replace menial tasks or requests and assist customers looking for self-service, thereby reducing both call volume and length.
AI provides a new future to financial analytics, increasing your ROI and allowing your employees to use their time more efficiently.
Learn more in this webinar.