“Sorting through overly hyped and overly generalised label of machine learning is a key to any successful consideration and implementation of a new fraud analytics solution”.
Detection strategies are shifting from analysing 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, business 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: organiz...
“Sorting through overly hyped and overly generalised label of machine learning is a key to any successful consideration and implementation of a new fraud analytics solution”.
Detection strategies are shifting from analysing 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, business 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 analysing data and building predictive models.
Work force augmentation refocuses the data science on interesting work like analysing simulations and iterating on multiple models.
Operational Machine Learning: As a basis for operationalization, organizations are considering their complete risk workflows and dependencies, and seeking ways to optimize them.
OML overcomes the dependency on manual coding from IT, signalling an evolution in the ability to look at more data, from more sources, and make better predictive decisions with less uncertainty, benefits are speed and reliability.
Organizations can significantly accelerate the time to deployment in mission-critical systems, because now what they code and test is what they deploy.
The most effective OML can inject real-time analytics into their operational routine without latency.
With data science techniques embedded into a tightly coupled with the real-time transactional workflow, running through machine learning models, business intelligence is generated at a higher resolution.
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Akhilesh Tripathi
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Akhilesh Tripathi CEO I have worked for 5 years as a consultant in an ISRAA.