How the Artificial Intelligence Boom is Taking Data Aggregation to the Next Level

While 2023 will be remembered as the year that generative AI and ChatGPT entered the public consciousness, the true transformative reach of developments in artificial intelligence is still being realized. With recent use cases building momentum, we’re finally beginning to see the true power of the GenAI boom in data aggregation.

Although the transformative potential of AI has been met with excitement, trepidation, and wariness, it’s clear that the power of the emerging technology can optimize many key processes. This is particularly true when it comes to financial data integration.

Bloomberg data suggests that generative AI will grow into a $1.3 trillion market by 2032, and its impact will be felt across a vast array of industries.

Artificial intelligence may not be readily associated with business intelligence today. Still, the technology can provide a greater ability to apply observations from big data sets and the power to identify solutions to key questions in supercharging a company’s efforts for more accurate decision-making and sustainable decisions.

The likes of ChatGPT and generative AI leverage large language models (LLMs) that can build business convenience and capabilities, but artificial intelligence as a whole has the potential to deliver next-generation data aggregation on an unprecedented scale.

By 2030, the AI market is expected to reach almost $740 billion.

The Role of AI in Business Intelligence

Both AI and machine learning will be integral to the growth of more focused, optimized business intelligence in terms of data analytics and management.

For instance, artificial intelligence can pave the way for important insights within datasets for businesses and organizations. These insights can then be utilized to deliver a more comprehensive understanding of operations and actionable advice for the future.

As the technology continues to grow, the capabilities of AI will only get stronger. Soon, we’ll see the implementation of composable analytics that can actively manage big data as a means of optimizing consumer-facing applications within a business.

This can make it more straightforward for multiple departments to collaborate seamlessly with the same accessible data, helping to improve accessibility and align efforts to help achieve more concentrated goals.

Overcoming the Challenges of Data Integration

To see artificial intelligence in financial data integration, we can see a use case from PetakSys in which a client sought to overcome complexities in the lack of uniformity within data feeds received from banks.

Because of varying formats and qualifiers, the task of processing different datasets proved to be a difficult task for the client. In addition to this, a lack of guidance made it a significant challenge for human interpretation of various deeds.

This caused inefficiencies and instances of human error within the data aggregation process while generating data structures.

Utilizing an AI-powered solution, PetakSys established a system that was capable of adapting and interpreting different data formats received from custodian banks and categorize them within an aggregated system.

Machine learning capabilities helped to interpret and translate big data into a standardized format that was fully compatible with the client’s portfolio management system.

As a result, the implementation of an AI-driven system helped to improve efficiency, agility, and resource optimization within the financial data integration system. It also enabled human users to save significant amounts of time in undertaking complex and monotonous tasks.

Next-Level Financial Data Enrichment

Deep-level AI analytics are helping to bring greater efficiency to all kinds of financial data aggregation on behalf of clients.

We’ve recently seen Morningstar Wealth turn to an artificial intelligence solution to deliver big data analytics within aggregated portfolios via a turnkey integration with Morningstar Licenced data.

The integration offers regional breakdowns, asset allocations, equity and fixed-income sector exposure, and fixed-income style boxes. This added efficiency helps clients to access more bespoke levels of investing advice.

“While there is a perception that aggregation has become more commoditized, the truth is that as data accessibility becomes more open, the standards and quality of the data are not keeping pace and, in fact, are falling behind,” explained Brian Costello, head of data strategy and governance at Morningstar Wealth.

“If you are catering to advisors and investors, it’s imperative to construct a tech infrastructure with vendors who specialize in the requirements of this market. Otherwise, you may encounter disconnected experiences and operational inefficiencies that can impede your growth.”

When it comes to aggregation, AI-driven data enrichment can help to empower more impactful portfolio analytics through the provision of clean data.

The Age of GenAI has Barely Begun

We’re only at the beginning of the generative AI boom, and companies are still attempting to figure out how best to implement the technology.

Over the coming years, GenAI will only gather more impactful use cases within the world of financial data integration. Whether firms implement customer-facing chatbots, embrace workflow automation, or introduce more powerful data analytics, there’s plenty to be excited about for the future of data aggregation.