Fund manager Clement Perrette believes that no matter how dominant AI could become in the future, the human powers of reason, logic, and predictive thinking will remain relevant in asset management.

Marc Berman filed this report for Programming Insider:

In the realm of private equity investments, the role of Big Data has been somewhat more prevalent, and continues to play a pivotal role in the analysis of public companies. For finance professionals, creating a data-driven model can provide analytical insights regarding the company’s overall “wellness”, curating a profile based on financial data, market data, the company’s web presence, and even patent filings.

Through the collection of conventional, and not-traditional data that culminates in an insightful look at the company’s health, financial professionals are able to gain knowledge needed to make sound decisions. In the beginning of the tech boom, financial institutions identified and analyzed rigid data, or only data that could be presented in quantifiable sets. However, with continued technological advancements, modern analysis of Big Data can analyze a myriad of different unstructured parameters. Not only do these technological advancements allow for different types of data to be analyzed within the equity investment sector, they can also analyze data at incredibly high speeds, allowing finance professionals to act on the garnered data swiftly.

In a field where dramatic change can occur seemingly at a moment’s notice, access to changing data can keep finance professionals abreast of any potential upcoming curveballs. Though quantitative measures are the basis of many investment models, understanding how to best implement that data is still a vastly human job. With a wide breadth of metrics available, the ability to utilize reason, logic, and predictive thinking is needed to organize the data in meaningful ways, and spearhead actionable changes based on the analysis. Thus, even though continuing advancements place quantitative tools at the forefront of the equity investment platform, the future will likely see an effective marriage of Big Data, and human intervention in the investment markets.

For the fixed income asset management niche, the implementation of Big Data has been slower than its equity investment counterpart, partially due to macroeconomics. Until recently, data available for analyzation in regard to government bonds has been somewhat sparse, rendering quantitative algorithms unable to perform their duties due to lack of available data. However, with the general public, and passive investors, demanding increased transparency within the realm of fixed income assets, data collection has become more prevalent in the recent few years, as banks and governments seek to maintain relevance. What was once considered proprietary, is now being publicly available, and therefore, able to be analyzed in a useful manner by Big Data.