Detect & Prevent Fraud Smartly | Assess your company's maturity to manage fraud
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Instruction covers a wide array of methods including Excel-based Benford's Law application, PowerBI AutoML, and Python programming for programmatic detection.
The curriculum includes practical exercises for assessing risk maturity and utilizes Python Colab sessions to demonstrate applied concepts.
Editorial course preview
These 3 complementary views highlight concrete, legible examples from the course presentation.
This contents slide outlines the course structure, covering topics such as understanding fraud characteristics, detecting fraud using Excel-based tools like Benford Law, and implementing prevention strategies.
This diagram illustrates the financial impact of fraud, breaking it down into direct losses, legal and investigation costs, employee expenses, customer dissatisfaction, and reputational loss.
This detailed resume slide outlines the professional background of Govind Kumar, highlighting his expertise in fraud prevention, AI product development, and leadership roles.
The wide range of topics may result in an introductory experience rather than a deep dive into specific fraud detection methodologies. This signal predates the displayed course update; the update may have addressed it, but the update label does not prove that it was corrected.
The inclusion of statistics and programming may present a steep learning curve for those expecting purely operational fraud management instruction. This signal predates the displayed course update; the update may have addressed it, but the update label does not prove that it was corrected.
The technical requirements of Python and statistics may exceed the needs of some traditional investigators.