In a recent report by Refinitiv, it is apparent that as financial institutions strive for competitive supremacy, artificial intelligence (AI) and machine learning are being increasingly integrated across diverse business sectors within the financial services industry. Notably, the influence of data scientists is growing, guiding the strategies surrounding machine learning, while the primary challenges revolve around data quality and its availability.

Key findings from the report include:

• Insights from more than 400 data scientists, quants, and data leaders have been compiled to unveil the latest trends in AI and machine learning in finance.
• Machine learning is undoubtedly reaching a state of maturity in the financial services sector, with a striking 80% of firms committing substantial investments to related technologies.
• The role of data scientists is evolving to become more strategic, and the number of data science teams in financial services organizations has surged by over 260% since 2018.

These highlights are derived from Refinitiv’s comprehensive “The rise of the data scientist” report, the second in their series on global Artificial Intelligence and Machine Learning. This report is founded on one of the most extensive worldwide surveys conducted among data practitioners and decision-makers in finance, making it an essential resource for those involved in data practices and innovation leadership.

Machine learning is now an integral, horizontal capability within the financial industry. Financial companies are employing increasingly sophisticated techniques, including deep learning, and are undergoing rapid cycles of innovation. In fact, over 72% of this year’s survey participants regard machine learning as a core element of their business strategy, with a remarkable 80% making substantial investments in associated technologies. The challenges that previously hindered adoption, such as investment levels, technology choices, and access to talent, have significantly diminished, providing a robust foundation for the widespread implementation of machine learning models.

Notably, data scientists have transitioned from their traditional roles in technology departments to directly shaping and driving machine learning initiatives within the business. They have moved from merely developing models in response to business demands to actively influencing the technology and data strategies essential for achieving business objectives.