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The State of Machine Learning in Fintech

Get an up-to-date picture of where fintech companies are in their machine learning journeys. Learn about the hurdles they are facing, success stories, and plans for future development.

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Why machine learning in fintech?

One of the key forces driving the transformation of the financial services industry is machine learning. From predictive analytics and fraud detection to personalized customer engagement, machine learning creates a myriad of exciting opportunities.


Given the rapidly changing nature of tech adoption and the fintech landscape alike, we wanted to gather and share the most up-to-date information about the state of machine learning in fintech. In this report, we will explore the current trends, wins and opportunities, challenges, and future developments for companies in the fintech space.

Machine learning adoption

How companies view themselves in terms of their machine learning maturity and what limitations they face

  • Large and mid-sized companies

    These types of companies are most advanced in ML adoption.

  • Small companies

    Small businesses are not leading in ML adoption, but only 10% of them have not considered using ML in their business.

  • Pressure of tech adoption

    Pressure is increasing on fintech companies to keep up with the pace of technology adoption to remain competitive.

  • The lack of skills and budget

    Staff and budget limitations are the two primary factors stopping smaller companies from leveraging ML.

Let’s work together
Thanks to leveraging machine learning, CRIF Bürgel has developed an innovative solution with the ultimate goal to create a world with less payment default and online fraud.

The solution identifies and evaluates risks in clients’ businesses and enables them to make informed and automated decisions about accepting and rejecting transactions.
Dr. Sayf Al-Sefou photo

Dr. Sayf Al-Sefou

Chief Technology Officer at CRIF Bürgel
Machine learning in fintech stock data

Key findings about the state of machine learning in fintech

  • Plans for machine learning adoption

Almost 90% of the companies expect their machine learning adoption to increase within the next 12 months, with 45% predicting that the increase will be significant.

  • Top three use cases

The top three most popular use cases for machine learning are advanced analytics, forecasting, and fraud detection and prevention, respectively.

  • Main drivers

54% of companies surveyed cited extracting better information from their data as their key driver for adopting machine learning.

  • Data-driven decision making

Nearly 85% of responders collect and work with data but without a well-structured analytical component to gain valuable insights and use them in a data-driven decisive process. Only 15% of companies do that.

  • Main challenge

The biggest challenge that companies of all sizes face in adopting machine learning are shortages of the skills required within the organization, with more than a half of the respondents citing it as an issue.

  • 49%

    Companies exploring or planning to use ML

  • 24

    Areas of focus

  • 15%

    Companies that are advanced ML users

  • 12.5%

    Staff time lost in data collection

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