Auditing

Investigating Fraud with Data Models

By Robert Persichitte

2 CPE Credits

CAIRAL2

Uncover fraud with data analytics. Learn to design and refine detection models using tools like Benford’s Law, AI, and statistical analysis.

Created At Jul 15, 2025  |  Updated At Jul 15, 2025
Fraud Investigation vs. Audit

8:43

Chapter 1

Fraud Investigation vs. Audit

Audits provide assurance; investigations uncover fraud. Learn how data models shift based on purpose, scope, and proof. This session defines key differences in fraud detection across auditing and investigative contexts—with accuracy and intent.

2 Quiz Questions

Evolution of Fraud Investigation Tools

9:47

Chapter 2

Evolution of Fraud Investigation Tools

From Excel to AI, fraud tools have evolved fast—but smart use starts with clear goals. Learn how to detect anomalies, document rigorously, and avoid flashy tools without substance. Consistency, context, and curiosity drive real insight.

2 Quiz Questions

Data Scope and Integrity in Investigations

14:12

Chapter 3

Data Scope and Integrity in Investigations

Bad data = bad conclusions. Learn how to define your problem, set scope, validate reliability, and avoid bias in your analytics. This session explores why strong foundations in data integrity are essential before building advanced fraud detection models.

2 Quiz Questions

Tests of Controls vs. Risk-Based Approaches

6:30

Chapter 4

Tests of Controls vs. Risk-Based Approaches

What’s better—certainty or suspicion? Learn the key differences between objective tests of controls and probabilistic models. Understand when to use each, how to interpret their results, and how they guide fraud detection and audit efficiency.

2 Quiz Questions

Statistical Tests for Fraud Detection

8:29

Chapter 5

Statistical Tests for Fraud Detection

Dive into the math behind fraud analytics. Learn how to use binomial distribution in Excel to flag anomalies, assess probability, and spot patterns that merit investigation. It’s not about guessing—it’s about measuring what’s too odd to ignore.

2 Quiz Questions

Regression and Beyond in Fraud Analysis

6:20

Chapter 6

Regression and Beyond in Fraud Analysis

Start with regressions, scale to machine learning. Learn how to build predictive fraud models, set clear objectives, and avoid the illusion of AI “smarts” without substance. This session shows how to evolve your tools while keeping rigor intact.

2 Quiz Questions

Evaluating and Improving Fraud Models

8:16

Chapter 7

Evaluating and Improving Fraud Models

How do you know your model works? Learn to measure precision, recall, and model power. From wild goose chases to missed red flags, this session shows how to fine-tune fraud models for smarter, safer, and more reliable decision-making.

2 Quiz Questions

Class Resources

Course Description

A quick snapshot of what this Masterclass covers!

Led by Robert Persichitte, the course ‘Investigating Fraud with Data Models’ explores how data analytics enhances fraud investigations, differentiating investigative techniques from traditional audits. Robert highlights the distinct objectives of auditors and investigators, emphasizing the need for targeted fraud detection rather than broad financial assurance. Participants will learn to define investigative scope, assess fraud risk, and apply models to identify financial anomalies. The course delves into analytical frameworks for fraud detection, explaining the challenges of proving fraud’s absence and the statistical methods used to assess risk. Robert traces the evolution of analytical tools, from Excel-based statistical tests to AI and machine learning, cautioning against over-reliance on complex models without a well-defined investigative framework. Case studies illustrate how probabilistic models, such as Benford’s Law and regression techniques, uncover anomalies while balancing false positives and negatives. A key focus is model evaluation, teaching participants to assess effectiveness using precision, recall, and sensitivity analysis. Emphasis is placed on refining fraud detection systems to adapt to emerging financial risks. Ethical considerations ensure detection methods remain fair, transparent, and legally sound. By the end of the course, professionals will have the tools to integrate data analytics into fraud detection strategies, improving efficiency and accuracy in financial oversight. Whether as auditors, investigators, or risk professionals, participants will be equipped to design, implement, and refine fraud detection models for stronger financial integrity.

Learning Objectives

Discover the key takeaways and skills you'll build throughout this Masterclass!

  • 1. Identify the key components, limitations, and ethical considerations involved in fraud detection frameworks and investigative auditing.
  • 2. Determine the functions, constraints, and documentation standards of audit tools and investigative models in forensic accounting.
  • 3. Recognize the role of data structure, documentation practices, and AI-related risks in enhancing audit accuracy and fraud detection.
  • 4. Differentiate control testing methods and probabilistic models based on their roles and use cases in fraud detection.
  • 5. List key statistical signals and low-probability patterns used to assess potential fraud in audit data.
  • 6. Identify the trade-offs, limitations, and practical risks of using regression and machine learning models in fraud detection.
  • 7. Select appropriate performance metrics to evaluate and refine fraud detection models across varying audit scenarios.
Robert Persichitte

Robert Persichitte

Expiration Date

1 year from the start of the course/upon subscription expiry

Course Duration

1 Hours
46 Mins

7

Chapters

Organized into clear, concise chapters for easy learning!

Topics Covered

Key concepts and skills you'll master throughout this Masterclass

Risk Assessment Financial Anomalies Data Analytics Modeling Evaluation

Finance & Advisory

Audit Reimagined

Certifying Organizations

National Association of State Boards of Accountancy (NASBA)

NASBA Logo
  • Continuing Professional Education Credit (CPE): 2
  • Fields of Study:
    Auditing2 CPE
  • Sponsor Identification number: 149174
  • Instructional Delivery Method: QAS Self Study
  • Program Level: Basic
  • Prerequisite Education: There are no prerequisites for this course
  • Advanced Preparation: There is no advance preparation required for this course
  • Created on: 15 Jul 2025
  • Reviewed on: 15 Jul 2025
  • Updated on: 15 Jul 2025
  • Video Duration: 1 hr 2 min 17 sec

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Sponsor Id#: 149174

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CAIRA (Certified AI Ready Accountant – Level 1) is a digital certification by Miles Masterclass, co-created with global accounting and AI leaders. It's designed to help accountants build future-ready, AI-powered skills and earn a Credly-verified digital badge upon completion.

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