The defining moment for data scientists

Artificial intelligence and machine learning ready to change finance

Artificial intelligence and machine learning research

The future of financial services is distributed and tech-enabled, with data at its core.

Download a new research report from LSEG Labs, which shows how rapid acceleration in data-driven transformation, resulting from the Covid-19 shock, is really just a glimpse of what’s yet to come.

Scroll down to explore the key insights and profiles of the hundreds of data leaders and practitioners we spoke to about AI/ML strategies, talent, data, technology and governance.

“Machine learning will present new opportunities and capabilities to improve the human experience. We are still exploring the potential this technology has. Let’s see what the future holds.”

Quantitative analyst, North American bank

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Regional trends

Companies in Asia Pacific and EMEA are catching-up with North America, and perceive AI/ML as a core component to business strategy far more than was the case last year.

“Deep Learning is the next step in machine learning because of its mastery in accuracy in well trained large data sets.”

Software engineer, Chinese broker

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Talent trends

Companies are expanding their data science teams, while becoming more specific about the skills they need.

“I see a lot of workforce or manpower being moved to AI and machine learning. This field is so vast and dynamic that it will attract huge pool of talent.”

Vice president of digital technology and innovation, Japanese investment bank

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Competition trends

Financial services firms are increasingly confident about their AI/ML progress, becoming less concerned about the competition.

“The adoption is uneven across companies. The gap between early adopters and us is only widening."

Quantitative researcher, Dutch Investment Bank

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Data trends

The industry’s appetite for diverse data is only growing.

“Companies will have to think about how they manage and store data. With the use of AI/ML everything is becoming bigger. Bigger data sets, bigger computers and bigger neural networks. It will be difficult to move to the next stage if we don’t train our systems to manage data well.”

Quantitative analyst, Japanese Investment Bank

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Get a unique insight into the state of AI/ML across financial services.

  • Strategy, deployment and use-cases
  • Popular platforms and tools
  • Data diversification
  • Talent, skills and teams
  • Model governance and stakeholder trust

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