Darkreading.com reported that "A machine learning model is only as good as the instructions it is given..... As a result, solutions that enable fraud experts, instead of data scientists, to input the initial correlations will deliver results faster in terms of identifying new correlations across different data sets as they're more familiar with the instances where fraud is likely to occur." The June 20, 2019 article entitled "'Democratizing' Machine Learning for Fraud Prevention & Payments Intelligence" included these comments:
This "democratization of machine learning" empowers fraud experts to "download" their knowledge and experience into computer models.
It's particularly effective in areas where fraud has not yet reached critical mass to support fraud experts as they use their experience and instincts to investigate certain transactions or customer behaviors, even if they are not yet fraudulent or highly indicative of fraud.
Feedback based on these kinds of instincts will aid the machine learning model to fine-tune itself, and improve accuracy and consistency in identifying more complex fraud indicators.
No surprises here!
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