3 Ways Capital Markets Can Benefit From AI and ML Technologies
IT leaders must ensure that new AI/ML initiatives incorporate and complement existing modernization efforts for better integration of AI/ML and business.
Fremont, CA: The obstacles experienced in capital markets when executing AI are similar to those in other industries at a high level. The first set of difficulties arises from the data itself. Unstructured data makes up 90 percent of all business data, and many companies are limited by on-premises and outdated apps that don't play well with newer cloud-based solutions.
Due to acquisitions, many data silos are widespread across financial markets, which is a time-consuming distraction that hampers efficiency and decision-making. Data research is hampered not by communication pace or volume but by the vast array of heterogeneous data sources.
Three best practices to benefit from AI and ML technologies:
Before perfecting AI, work on Analytics
A powerful and adaptable data analytics platform is required for effective AI and ML, necessitating significant infrastructure re-design. It's challenging to execute data science in production without a solid core data infrastructure. Data scientists may not have easy access to business-critical data sources, preventing business-critical decision-making. All of these impediments reduce the amount of time and space available for data analysis and insights. The cloud provider does the great bulk of infrastructure maintenance and patching in a serverless, cloud-based data analytics architecture. This clears up time and resources for the data team to focus on analysis and insights.
Cloud technologies that are both fast and integrated can help businesses break down data silos, create a single code base, and foster a more collaborative working culture. They can also be built to deliver more significant real-time insights, which is an essential component of machine learning and artificial intelligence.
Start by prioritizing business
Though it's easy to focus just on the advantages that technology can bring to data analytics, the immediate opportunity for organizations to truly benefit from AI lies in the ability of humans and AI to collaborate. When ML-based data analytics is combined with human judgment and intuition, it becomes even more powerful. Computers have become quicker, data storage has been less expensive, and algorithm access has become more democratized due to recent technological breakthroughs. However, human experience and judgment may contribute to and expand on precise, smart data analysis, whether in health or the financial markets.
Structured team for enhanced decision making
A data science team's goal is to help people make better decisions by using data. Consider this while selecting how to structure your data science and AI/ML teams, as well as who they will report to. It's also vital to think about where your company is on its data and AI journeys right now. Consider the company's culture, size, and expansion. Consider how the data flow is structured when designing team roles and where those roles would be most beneficial.