Fraud detection - driven by data analysis.
MACHINE LEARNING FOR FRAUD DETECTION:
“Sorting through the overly hyped and overly generalized label of machine learning is a key to any successful consideration and implementation of a new fraud analytics solution”.
Detection strategies are shifting from analyzing siloed transactional activity to instead making better use of data and analytics, building holistic understandings of customer activity.
By bringing together cross-product and cross channel data and applying nimble machine learning analytics that iteratively optimize results, businesses can understand the context of transactions and make better decisions.
Progressions in AI technology can streamline workflows and eliminate antiquated dependencies.
Two key advancements, in particular, can serve to bottle human creativity, drive employees towards more strategic work, and reduce operational bottlenecks.
These trends are workforce augmentation (doing more complex tasks) and operational machine learning (doing complex tasks more quickly).
Workforce Augmentation: organizations are searching for ways to augment their existing workforce by using technology.
The basis for augmentation is in the technological architecture of a system.
More evolved systems can better automate the “janitorial tasks” of data science, like cleaning data and combining data from different sources.
These integrated automation tools drive workers towards increasingly creative and advanced tasks, like analyzing data and building predictive models.
Workforce augmentation refocuses the data science on interesting work like analyzing simulations and iterating on multiple models.
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