Detecting Fraud with Data Analytics

Course Overview

The course aims to equip participants with the knowledge and skills to leverage data analytics effectively in detecting and preventing fraud. It provides a comprehensive understanding of various data analytics techniques and their applications in real-world fraud scenarios, with a strong emphasis on ethical considerations and practical implementation.

Course Details:

Date: October 16th   

Duration: 1 days

Continuing Professional Education (CPE) Credit: 4

Fee:

  • Members: 550,000
  • Non-Members: 650,000
  • Mode: In Person

Course Objectives:

  • Define the role of data analytics in the context of fraud detection.
  • Recognize the benefits and challenges of using data analytics for fraud prevention.
  • Explore various types of fraud (financial, employee, vendor, etc.).
  • Identify and match specific data analytics techniques to each type of fraud.
  • Learn best practices for collecting and preparing data for effective fraud detection.
  • Address data quality issues and ensure the accuracy and reliability of data.
  • Gain proficiency in statistical analysis, pattern recognition, and anomaly detection.
  • Explore the application of machine learning algorithms for fraud detection.
  • Understand how social network analysis and other advanced techniques contribute to fraud prevention.
  • Develop and train predictive models for fraud detection.
  • Validate models and assess their effectiveness in identifying fraudulent activities.
  • Present data analytics findings in a visually compelling manner.
  • Interpret results and make informed decisions based on analytics outcomes.
  • Learn from both successful and unsuccessful applications of data analytics in fraud detection.
  • Recognize and address ethical issues related to data privacy and security in fraud detection.
  • Ensure responsible and ethical use of data analytics tools in fraud prevention.
  • Integrate data analytics into existing fraud prevention programs.
  • Develop a roadmap for successful implementation of data analytics in fraud prevention initiatives.
  • Explore challenges associated with fraud detection using data analytics.
  • Discuss emerging trends and technologies shaping the future of fraud prevention.
  • Provide participants with hands-on exercises and practical applications to reinforce learning.
  • Foster collaboration among participants to share knowledge and insights related to fraud detection with data analytics.
  • Encourage a commitment to continuous learning in the dynamic field of fraud detection.
  • Provide resources and references for ongoing professional development.

Who Should Attend

  • Professionals responsible for analyzing and interpreting data, particularly those interested in fraud detection.
  • Specialists dedicated to identifying and investigating fraudulent activities within an organization.
  • Individuals involved in internal audit functions seeking to enhance fraud detection capabilities.
  • Professionals responsible for managing organizational risks, including fraud risks.
  • Specialists in accounting and finance who focus on investigating financial discrepancies, including fraud.
  • Individuals ensuring adherence to laws, regulations, and internal policies, with a focus on fraud prevention.
  • Those involved in information technology and cybersecurity interested in leveraging data analytics for fraud detection.
  • Finance professionals responsible for the financial health and integrity of the organization.
  • Individuals working in business intelligence roles, aiming to enhance their data analytics skills for fraud prevention.
  • Police officers, detectives, and investigators engaged in combating fraud within the criminal justice system.
  • Professionals responsible for designing and implementing internal controls to prevent fraud.
  • Individuals in audit and assurance roles seeking to incorporate data analytics into fraud detection procedures.
  • Public sector employees responsible for managing fraud risks in government agencies.
  • Consultants providing services in fraud and risk management interested in data-driven approaches.
  • Professionals across various industries interested in leveraging data analytics to detect and prevent fraud.

Prerequisites:

  • Familiarity with basic data analysis concepts and techniques provides a foundation for understanding how data analytics can be applied to fraud detection.
  • Basic proficiency in data analysis tools and software (e.g., Excel, SQL, Python, R) is advantageous for practical exercises and implementation.
  • An understanding of basic fraud concepts and common types of fraudulent activities is beneficial.
  • Professionals with some experience in fraud prevention, detection, or investigation will find the course content more relevant.
  • Familiarity with risk management principles provides context for understanding the role of data analytics in fraud detection.
  • A basic understanding of statistical concepts is helpful for comprehending certain data analytics techniques used in fraud detection.
  • Understanding the core operations of the organization is crucial for identifying potential areas of fraud risk.
  • The ability to think critically and analyze data is essential for effective fraud detection using data analytics.
  • Awareness of ethical considerations related to fraud prevention and data analytics is important for responsible use of technology.
  • Participants should possess a high level of attention to detail to ensure accuracy in analyzing and interpreting data.
  • IT professionals should have an awareness of cybersecurity concepts, as fraud detection often involves digital data.

Method of Delivery:

  • In-Person: The course will be conducted at a physical location. Participants will engage in face-to-face instruction, hands-on exercises, and group discussions.
  • Online: The course will be delivered through a virtual learning platform. Participants will have access to course materials, lectures, and interactive exercises

Completion Certificate: Upon successful completion of the course, participants will receive a certificate of completion, which can be used to claim Continuing Professional Education (CPE) credits.

Course Outline:

Module 1: Introduction to Fraud Detection with Data Analytics

  • Overview of the role of data analytics in fraud detection
  • Understanding the advantages and challenges of using data analytics in fraud prevention

Module 2: Types of Fraud and Data Analytics Applications

  • Exploring common types of fraud (financial, employee, vendor, etc.)
  • Identifying specific data analytics techniques for each type of fraud

Module 3: Data Collection and Preparation

  • Best practices for collecting and preparing data for fraud detection
  • Addressing data quality issues and ensuring data accuracy

Module 4: Data Analysis Techniques for Fraud Detection

  • Introduction to statistical analysis, pattern recognition, and anomaly detection
  • Machine learning algorithms for fraud detection
  • Social network analysis and other advanced techniques

Module 5: Building Fraud Detection Models

  • Developing and training predictive models for fraud detection
  • Model validation and assessing the effectiveness of fraud detection algorithms

Module 6: Visualization and Interpretation of Results

  • Presenting data analytics findings in a visually compelling manner
  • Interpreting results and making informed decisions based on analytics outcomes

Module 7: Case Studies and Real-Life Applications

  • Analyzing real-world fraud cases where data analytics played a crucial role
  • Learning from successful and unsuccessful applications of data analytics in fraud detection

Module 8: Ethical Considerations in Fraud Detection with Data Analytics

  • Addressing ethical issues related to data privacy and security
  • Ensuring responsible use of data analytics in fraud prevention

Module 9: Implementing Data Analytics in Fraud Prevention Programs

  • Integrating data analytics into existing fraud prevention programs
  • Developing a roadmap for successful implementation

Module 10: Challenges and Future Trends

  • Discussing challenges in fraud detection with data analytics
  • Exploring emerging trends and technologies in the field

Register for the training here

Date

Oct 16 2024

Time

All Day

Cost

550,000.00/=

Location

4th Floor, Block B, Ntinda Complex
Opposite St. Luke Church
Category

Organizer

ACFE Uganda Chapter
ACFE Uganda Chapter
Phone
0414 231 136
Email
admin@acfeuganda.com
Website
ACFE Uganda Chapter
QR Code
Register for ACFE Africa Year-End Event

Join us in marking the Fraud Awareness Week, that will run from 12th to 18th November 2023.

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