MACHINE LEARNING FOR FRAUD DETECTION
Authors: GAUTAM R. DESIRAJU
DOI: 10.87349/ahuri/170202
Page No: 20-26
Abstract
“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.
