Module 2: Foundations of Fraud and Internal Controls

Topic 1: Defining Fraud and Internal Controls

Provides definitions and examples of fraud and internal control mechanisms.

1. Key Learning Outcome: Understand different types of fraud and the role of internal controls.
2. Key Learning Skill: Knowledge of fraud types and internal controls.

Topic 2: Common Types of Fraud Schemes

Reviews various common fraud schemes.

1. Key Learning Outcome: Identify common fraud schemes.
2. Key Learning Skill: Ability to recognize different fraud schemes.

Topic 3: Identifying and Cataloguing Fraud Indicators

Teaches how to identify and document indicators of fraud.

1. Key Learning Outcome: Develop skills in cataloging and documenting fraud indicators.
2. Key Learning Skill: Proficiency in identifying and recording fraud indicators.

Module 3: Data Collection and Cleaning Techniques

Topic 1: Data Sources and Data Extraction Methods

Covers different sources of data and methods for extracting it.

1. Key Learning Outcome: Learn to identify and extract relevant data sources.
2. Key Learning Skill: Proficiency in data extraction techniques.

Topic 2: Cleaning and Preparing Data for Analysis

Techniques for cleaning and preparing data for analysis.

1. Key Learning Outcome: Ensure accurate and reliable data for analysis.
2. Key Learning Skill: Skills in data cleaning and preparation.

Topic 3: Handling Missing and Inconsistent Data

Methods to address missing and inconsistent data.

1. Key Learning Outcome: Manage data issues effectively.
2. Key Learning Skill: Ability to handle missing and inconsistent data.

Module 4: Exploratory Data Analysis (EDA)

Topic 1: Introduction to EDA

Introduction to the concept and techniques of exploratory data analysis.

1. Key Learning Outcome: Understand the basics of EDA.
2. Key Learning Skill: Competence in conducting EDA.

Topic 2: Visualizing Data to Identify Anomalies

Use visualization tools to detect data anomalies.

1. Key Learning Outcome: Identify anomalies through data visualization.
2. Key Learning Skill: Skills in data visualization.

Topic 3: Basic Statistical Techniques for Fraud Detection

Basic statistical methods to identify potential fraud.

1. Key Learning Outcome: Apply basic statistical techniques in fraud detection.
2. Key Learning Skill: Proficiency in using statistical methods for fraud detection.

Module 5: Advanced Fraud Detection Techniques

Topic 1: Applying Benford’s Law

Learn how to use Benford’s Law for detecting anomalies.

1. Key Learning Outcome: Use Benford’s Law to identify fraud.
2. Key Learning Skill: Mastery of Benford’s Law application.

Topic 2: Data Matching and Fuzzy Matching

Techniques for matching data sets and identifying near matches.

1. Key Learning Outcome: Match data accurately to detect fraud.
2. Key Learning Skill: Skills in data and fuzzy matching.

Topic 3: Outlier Detection Methods

Methods for identifying outliers in data sets.

1. Key Learning Outcome: Detect outliers that may indicate fraud.
2. Key Learning Skill: Proficiency in outlier detection.

Topic 4: Continuous and Repetitive Data Analysis

Techniques for ongoing analysis of data to detect fraud.

1. Key Learning Outcome: Perform continuous fraud detection analysis.
2. Key Learning Skill: Ability to conduct ongoing data analysis.

Module 6: Case Studies and Applications

Topic 1: Real-World Examples of Fraud Detection

Study real-world cases to understand fraud detection applications.

1. Key Learning Outcome: Apply theory to real-world scenarios.
2. Key Learning Skill: Experience in analyzing real fraud cases.

Topic 2: Analyzing Case Studies

Detailed analysis of fraud detection case studies.

1. Key Learning Outcome: Develop analytical skills through case study analysis.
2. Key Learning Skill: Ability to analyze detailed fraud cases.

Topic 3: Practical Exercises Using Provided Datasets

Hands-on practice with datasets to reinforce learning.

1. Key Learning Outcome: Gain practical experience in fraud detection.
2. Key Learning Skill: Hands-on skills with fraud detection datasets.

Module 7: Ethical Considerations and Best Practices

Topic 1: Ethical Issues in Data Analytics and Fraud Detection

Discussion on ethical issues and challenges in the field.

1. Key Learning Outcome: Understand ethical considerations in fraud detection.
2. Key Learning Skill: Awareness of ethical issues in data analytics.

Topic 2: Best Practices for Implementing a Fraud Detection Program

Guidelines and best practices for establishing fraud detection programs.

1. Key Learning Outcome: Learn best practices for fraud detection implementation.
2. Key Learning Skill: Knowledge of fraud detection best practices.

Topic 3: Legal and Regulatory Considerations

Overview of legal and regulatory requirements in fraud detection.

1. Key Learning Outcome: Understand the legal framework for fraud detection.
2. Key Learning Skill: Awareness of legal and regulatory considerations.