Using AI in the Financial Services Industry | Technology

Using AI in the Financial Services Industry | Technology
Using AI in the Financial Services Industry 

Leveraging Artificial Intelligence in the Financial Services Industry

In financial services, it is important to gain any competitive advantage.  Your competition has access to most of the same data as you, since historical data is available to everyone in your industry.  Your advantage comes with the ability to mine 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 faster than ever.  This is where AI-first intelligence can help you.

To implement AI infrastructure, there are a few key considerations to maximize your return on investment (ROI).

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What things do you need to consider when building an AI infrastructure?

When designing for high utilization workloads like AI for financial analysis, it’s good practice to keep systems on-premises.  On-premises computing is more cost-effective than cloud-based computing when used heavily.  Cloud service costs can add up quickly and any cloud outage inevitably leads to downtime.

You can take advantage of a variety of networking options, but we generally recommend high-speed fabrics like 100-gig Ethernet or 200-gig HDR InfiniBand.

You should also consider that the size of your dataset is just as important as the quality of your model.  So you’ll want to enable modern AI-centric storage design.  This will allow you to scale as needed to maximize your ROI.

It’s also important to keep main storage close to local computing resources to maximize network bandwidth and limit latency.  Keeping storage on-premises also keeps your sensitive data safe.  Let’s look at how storage should be configured to maximize efficiency.

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What are storage design considerations for financial analysis?

Traditional storage like NAS (network attached storage) can’t keep up.  Bandwidth is limited to around 10 gigabits per second and is not scalable enough for AI workloads.  Fast local storage doesn’t work for modern parallel problems because it results in constantly copying data in and out of nodes, clogging up the network.

AI-optimized storage must be parallel and support a single namespace data lake.  This allows 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 tiered object storage to remain cost-effective and serve as an affordable long-term storage option at scale for regulatory retention requirements.

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How can AI benefit the financial analysis industry?

With AI and machine learning, you can significantly reduce the number of false positives, leading to higher customer satisfaction.  Artificial intelligence can now automate minor insurance claims, allowing employees to focus on larger, more complex issues.

AI can also be used to review claims or flag cases for further and deeper analysis by detecting potential fraud or human error.  Regular tasks prone to human error can be reviewed or, in many cases, entirely performed by AI applications, often increasing both efficiency and accuracy. 

Today’s chatbot is different from previous years.  They are more advanced and can now often replace minor tasks or requests and help customers looking for self-service, thus reducing the volume and duration of calls.

AI offers a new future for financial analysis, increasing your ROI and allowing your employees to use their time more efficiently.

Source: HPC Wire, Direct News 99 

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