This column is the first in a series on data analytics and their use in fraud detection and investigation. Here, we introduce the topic while subsequent columns will focus on specific methods and techniques.
Although organizations accumulate more information than ever before, insights within that data might remain hidden with traditional means of analysis. Fraud analytics applies analytical techniques and subject-matter expertise that can help us delve deep into the core of gigabytes of data to discover fraud and abuse. With its ability to reveal hidden patterns and relationships, fraud analytics can make the difference between identifying all fraudulent transactions, not just the obvious ones in an investigation. We'll discuss here ways to detect fraud a priori or identify fraud a posteriori.
Historically, auditors and fraud examiners have used spreadsheet and computer-assisted audit techniques (CAATS) to analyze data. We can quickly and easily review spreadsheets to understand simple patterns, but they rapidly reach their limits.
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CAATS were originally designed as audit support tools, but we can use them for data analysis functions such as pattern matching, sampling of transactions within amount ranges and statistical testing like Benford's Law. However, they don't provide the complex algorithms and predictive models for identifying complex fraud patterns in large data sets.