AI is having a major impact on the finance industry as part of fintech. AI is being used to analyze investment opportunities, optimize portfolios, and mitigate risks, among many other functions, but the applications go well beyond the investment decision-making process. For example, automated wealth advisers (or “robo-advisers”) may assist investors without the need for a human adviser, or they may be used in combination with a human adviser. The desired outcome is the ability to provide tailored, actionable advice to investors with greater ease of access and at lower cost.

Machine learning plays a key role in the expanding use of fintech throughout the finance industry. The general term “machine learning” includes a variety of methods that use advanced techniques to find patterns in extremely large amounts of data. These technologies are able to perform such tasks by “learning” from known examples and applying them to new information without human intervention. A recent survey of CFA Institute members identified machine learning as one of the leading drivers of change that will affect investment professionals.

Blockchain is a special type of database technology that allows all the participants in a transaction to see the same data at the same time. Blockchain is changing the way business is done in the investment industry and has the potential to boost trust in the system by improving transparency. Crypto assets, a related technology, are issued, transferred, and settled on a blockchain. Speaking at the 2018 CFA Institute Equity Research and Valuation conference, regarding blockchain in financial services, Caitlin Long predicted that “All financial services professionals must develop operational expertise [in blockchain]” within the next few years.

The advent of Big Data has been driving significant changes in investment management for several years. The term Big Data refers to alternative data sources that can be analyzed because machine learning, AI, and related technologies now have the ability to evaluate unstructured data (such as text, images, and spoken languages) on a large scale. These applications are giving investment professionals access to a vast amount of public information, much of which was not available to investors before. Increasingly, investment managers are using Big Data in their investment processes to gain insights that can give them an information advantage.

What Does a Data Scientist Do?

Data scientists, who are increasingly present at investment organizations, analyze datasets (many of which are from new or alternative sources), apply coding/programming skills and modern analytical techniques to databases to seek meaningful patterns and insights, and communicate relevant findings. They provide support and advice to relevant teams within the organization (including front office investment teams) and develop tools and dashboards to enhance/enable improvements to the overall investment process.