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IUC 2020 - Session Recordings

Finding Anomalies Lurking in Big Data

Finding Anomalies Lurking in Big Data

Kathryn Horton, CPA, CMA, CIDA, CFE, Audit Thought Leader
Kathryn K. Horton CPA PA

Digital transformation has produced insurmountable amounts of data every day. The question is not about the abundance of data, but how to transform data into information to unlock its potential benefits. This session explores various ways IDEA can be used to locate anomalies within high volumes of transactional data. Learn different data mining techniques to identify unusual transactions, including proximity analysis, numeric theories, and matching functions. Discover new ways to leverage the power of IDEA to analyze big data and add efficiencies to processes by creating custom automation.

Learning Objectives

  • Perform proximity analysis to identify potential fraud and opportunities

  • Understand numeric theories and applications to big data

  • Identify backdated transactions


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