In part 2, the author applies his unique letter analytic techniques to a variety of examples, including keyword searches and predictive analysis using benchmark data. The aim? Finding fraud in the words.
part 1 of this article in the March/April issue, we transitioned from our number-focused tendencies to the new perspective of investigating for possible fraud in text, which accounts for the majority of data. Via letter analytics — a term I coined in my Lanza Approach to Letter Analytics (LALA)TM — I provided a swift means to analyze texts in current and previous periods, compare them to an English-language benchmark and understand anomalies. The trick for speeding a review is to focus on the first and last few characters in words, which facilitates trending and isolates change more easily.
Here we'll focus on specific research case studies studying letter analytics: (1) over time for deviations (2) for patterns displayed by specific authors and in (3) improving the scope and speed of keyword search reviews.
I aim to show how fraud examiners can utilize text as easily as numbers in their analyses by turning the unstructured nature of text into structured sets of charts for visual identification of change. Structuring the text analytics allows for fraud examiners to discover new truths within data more quickly — as we'll see in the first two case studies — or to explore data with a specific intent in mind, as demonstrated in the third keywords search case study.
I'm still in the process of analyzing text files from public companies that will indicate fraud. However, we still can find deviations in any texts that would indicate change and would test my hypothesis. I chose: (1) an exhaustive list of British pop songs (2) William Shakespeare's plays and (3) an exhaustive keyword dictionary.
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