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Large and mid-sized companies
Pressure of tech adoption
The lack of skills and budget
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.
- 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.
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