Research Report: Machine Learning Models for the Future: The Rise of the Data Scientist

The need to build a more virtual markets infrastructure is spurring new technology in the financial industry with organisations doubling down on investments in data science and machine learning to gain an edge. Many are making significant investments in artificial intelligence and machine learning while others are going further and making it a core component of their business strategy.

Financial players must find the balance between talent, technology, leadership and business culture – all underpinned by quality data. From that foundation, the industry’s data scientists are delivering solutions scarcely believable a year ago and the technological revolution sweeping the financial world in the wake of the pandemic will only grow stronger.

This 2020 research report from Refinitiv examines the current landscape for AI and ML.

Key findings:

Firms scale AI/ML capabilities across multiple business areas
As AI/ML models mature, more data scientists are recruited and deployed across firms
Data scientists evolve from a supportive function to driving strategy
Advances deploying models to production make AI/ML more of a reality than “hype”
Natural language processing (NLP) unlocks value in unstructured data
Data quality and availability are considered the biggest barriers to AI/ML adoption, as talent, technology and funding issues fade
Firms’ AI/ML models will only be as good as their data strategies
COVID-19 upset AI/ML models, and is set to drive up investment in firms with high levels of AI/ML maturity
AI/ML models need alternative data to be ready for more black swans