Fraud Analytics: Ensuring Data Quality

Complete the form below to watch the webinar

Before performing any type of fraud analysis, it’s critical to ensure the quality of your data is optimal for the analysis. Skipping this crucial step can render your results useless.

Ensuring data quality mitigates the risk that false anomalies will taint your results. This is especially important when searching for indicators of fraud. And it’s not that difficult or expensive to take this step. In fact, this can be achieved by simply using basic spreadsheets and the proper methodology.

Join Alexis C. Bell, International Antifraud Consultant, Author and Keynote Speaker, as she walks participants through the steps to ensure data quality.


The webinar will cover:

  • Importance of data quality
  • Key definitions
  • Documenting your work
  • Getting access to the required data set
  • How to assess data sets for:
    • completeness
    • uniqueness
    • timeliness
    • validity
    • accuracy
    • consistency

Webinar Presenter


Alexis Bell
Alexis Bell

Founder and CEO, Fraud Doctor LLC

Alexis C. Bell, MS, CFE, PI is the CEO and founder of Fraud Doctor LLC. She helps companies protect themselves from fraud by specializing in international litigation support, antifraud consulting, training, and board advisory.
Bell is a graduate of Cornell University with a BS in Applied Economics, completed the ASCENT fellowship at Dartmouth University, and obtained her MS in Financial Crime and Compliance Management at Utica College. She has authored three books and broken new ground with academic research in furtherance of the antifraud field. In addition, Bell serves as an adjunct professor in the Financial Crime & Compliance Management (FCM) graduate program in the College of Business & Justice Studies at Utica College.

Watch the Webinar

To our customers: We'll never sell, distribute or reveal your email address to anyone. Privacy Policy



Investigation Resources Library

Detecting Deception×
Clear All Filters

Browsing 51 of 51 Resources. Filtered by Detecting Deception