Until recently, the application of advanced technology like artificial intelligence (AI) to the post-trade portion of capital markets has added up to little more than an incremental efficiency play, meant to reduce costs and streamline back-office processes. With that approach – and in a heavily regulated industry with decades of legacy technology and practices – it’s understandable that tech like AI hasn’t quite lived up to the hype.

Yet, the possibilities are endless if you take the right approach, starting with a solid data foundation.

Begin at the beginning

The first task is to examine your data value chain. With data trapped in so many places in and around the organization, you’ll need to determine where your most valuable information is stored and how you can readily access it.

Fortunately, modern cloud storage and enterprise infrastructure have matured to a point where accessing vast pools of data has become routine. Vendors can assist by providing linkages to support your AI model and feed it with essential inputs.

Second, consider the economic benefit you hope to derive. Most AI applications in the capital markets space will be augmenting or replacing rules-based systems, such as anti-money laundering or credit reserve calculators. If AI can improve these systems by just 20%, it’s an economic gain.

So, determine your baseline. Remember, your data can come from a variety of sources – transactional histories, commodity prices, SEC reports, sensory data, weather bureau information, even leadership changes reported in the news. Find a way to bridge the gap between that data and your AI and develop the applications you need to achieve your desired outcomes.

An incremental approach is always best. Find the use cases that stand out in your organization, build models to address them, and take care to monitor and adjust those models over time. AI is anything but static. Through continued course corrections, you can continue to evolve your applications to a high degree of performance.

Think strategically

Perhaps the most intimidating aspect of using cutting-edge technology is the enormity of it. The word “modernization” alone is enough to freeze you in your tracks.

Rather than boiling the ocean, pick a spot and start there. Focus on one atomic use case that can make a difference. Cash management, securities lending or AML are all good starting points.

Round up the data necessary to solve that problem, determine a success marker based on the system you use today and build a proof-of-concept to test your case. As you refine your model, you will soon evolve an application that supports your business. Then you can begin connecting the dots to the next application in the value chain, leading to a growing platform of cutting-edge solutions.

Remember, AI is flexible. The models you build today can be reused and refined in other areas, so any investment you make in the process is a long-term benefit to the enterprise.