Finding the Needle in the Haystack. An artwork transfer that took longer than usual at a museum’s loading dock. In the right hands and with the right set of investigative talent, these apparent minutiae can become revelations that lead to larger patterns, networks of fraud, and ultimately, solutions for our clients.
Finding the ‘Needle in the Haystack’ with Artificial Intelligence. By David Kleiman, Bloomberg Law For attorneys, especially junior associates, finding relevant information is a daily – and even nightly – activity.
However, some of those needles are bent — they’re fraudulent. Data mining acts like a giant magnet that pulls the needles out of the haystack so the fraud examiner can sort them, find the fraud, and reduce overall waste and abuse. Of course, advances in computer technology and data-mining software have revolutionized fraud detection.
Fraud detection, in many ways, resembles finding needles in a haystack. You must sort and make sense of massive amounts of data in order to find your "needles" or in this case, your fraudsters. Let’s use the example of a phone company with billions of calls occurring in its network, all on a weekly basis.
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And, while the data is there, it’s difficult to find the fraud indicators amidst the huge number of daily transactions. So, what can we do in the face of all this data? How do we find the needle in the haystack? We have to take it one step at a time.
Unless I know exactly what I want, like my favorite shade of lipstick, I have a time trying to find what I might want. to talk through how it can solve the e-commerce shoppers’ needle-in-a-haystack.
Finding Needles in a Haystack: Using Data Analytics to improve fraud prediction article (pdf Available) in The Accounting Review April 2016 with 903 Reads DOI: 10.2308/accr-51562
Network Forensics 101: Finding the Needle in the Haystack Network Analysis Today: Big Questions, Vague Answers Organizations depend on their networks more than ever before, so monitoring and managing those networks is a mission-critical job for IT. But monitoring and managing networks has become increasingly difficult for several reasons.