BEGIN:VCALENDAR
VERSION:2.0
METHOD:PUBLISH
CALSCALE:GREGORIAN
PRODID:-//WordPress - MECv7.3.0//EN
X-ORIGINAL-URL:https://acfeuganda.com/
X-WR-CALNAME:Association of Certified Fraud Examiners, Uganda Chapter
X-WR-CALDESC:ACFE Uganda Chapter
REFRESH-INTERVAL;VALUE=DURATION:PT1H
X-PUBLISHED-TTL:PT1H
X-MS-OLK-FORCEINSPECTOROPEN:TRUE
BEGIN:VEVENT
CLASS:PUBLIC
UID:MEC-fd4f21f2556dad0ea8b7a5c04eabebda@acfeuganda.com
DTSTART:20241016T000000Z
DTEND:20241017T000000Z
DTSTAMP:20240223T085500Z
CREATED:20240223
LAST-MODIFIED:20240705
PRIORITY:5
SEQUENCE:1
TRANSP:OPAQUE
SUMMARY:Detecting Fraud with Data Analytics
DESCRIPTION:Course Overview\nThe 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.\nCourse Details:\nDate: October 16th   \nDuration: 1 days\nContinuing Professional Education (CPE) Credit: 4\nFee:\n\nMembers: 550,000\nNon-Members: 650,000\nMode: In Person\n\nCourse Objectives:\n\nDefine the role of data analytics in the context of fraud detection.\nRecognize the benefits and challenges of using data analytics for fraud prevention.\nExplore various types of fraud (financial, employee, vendor, etc.).\nIdentify and match specific data analytics techniques to each type of fraud.\nLearn best practices for collecting and preparing data for effective fraud detection.\nAddress data quality issues and ensure the accuracy and reliability of data.\nGain proficiency in statistical analysis, pattern recognition, and anomaly detection.\nExplore the application of machine learning algorithms for fraud detection.\nUnderstand how social network analysis and other advanced techniques contribute to fraud prevention.\nDevelop and train predictive models for fraud detection.\nValidate models and assess their effectiveness in identifying fraudulent activities.\nPresent data analytics findings in a visually compelling manner.\nInterpret results and make informed decisions based on analytics outcomes.\nLearn from both successful and unsuccessful applications of data analytics in fraud detection.\nRecognize and address ethical issues related to data privacy and security in fraud detection.\nEnsure responsible and ethical use of data analytics tools in fraud prevention.\nIntegrate data analytics into existing fraud prevention programs.\nDevelop a roadmap for successful implementation of data analytics in fraud prevention initiatives.\nExplore challenges associated with fraud detection using data analytics.\nDiscuss emerging trends and technologies shaping the future of fraud prevention.\nProvide participants with hands-on exercises and practical applications to reinforce learning.\nFoster collaboration among participants to share knowledge and insights related to fraud detection with data analytics.\nEncourage a commitment to continuous learning in the dynamic field of fraud detection.\nProvide resources and references for ongoing professional development.\n\nWho Should Attend\n\nProfessionals responsible for analyzing and interpreting data, particularly those interested in fraud detection.\nSpecialists dedicated to identifying and investigating fraudulent activities within an organization.\nIndividuals involved in internal audit functions seeking to enhance fraud detection capabilities.\nProfessionals responsible for managing organizational risks, including fraud risks.\nSpecialists in accounting and finance who focus on investigating financial discrepancies, including fraud.\nIndividuals ensuring adherence to laws, regulations, and internal policies, with a focus on fraud prevention.\nThose involved in information technology and cybersecurity interested in leveraging data analytics for fraud detection.\nFinance professionals responsible for the financial health and integrity of the organization.\nIndividuals working in business intelligence roles, aiming to enhance their data analytics skills for fraud prevention.\nPolice officers, detectives, and investigators engaged in combating fraud within the criminal justice system.\nProfessionals responsible for designing and implementing internal controls to prevent fraud.\nIndividuals in audit and assurance roles seeking to incorporate data analytics into fraud detection procedures.\nPublic sector employees responsible for managing fraud risks in government agencies.\nConsultants providing services in fraud and risk management interested in data-driven approaches.\nProfessionals across various industries interested in leveraging data analytics to detect and prevent fraud.\n\nPrerequisites:\n\nFamiliarity with basic data analysis concepts and techniques provides a foundation for understanding how data analytics can be applied to fraud detection.\nBasic proficiency in data analysis tools and software (e.g., Excel, SQL, Python, R) is advantageous for practical exercises and implementation.\nAn understanding of basic fraud concepts and common types of fraudulent activities is beneficial.\nProfessionals with some experience in fraud prevention, detection, or investigation will find the course content more relevant.\nFamiliarity with risk management principles provides context for understanding the role of data analytics in fraud detection.\nA basic understanding of statistical concepts is helpful for comprehending certain data analytics techniques used in fraud detection.\nUnderstanding the core operations of the organization is crucial for identifying potential areas of fraud risk.\nThe ability to think critically and analyze data is essential for effective fraud detection using data analytics.\nAwareness of ethical considerations related to fraud prevention and data analytics is important for responsible use of technology.\nParticipants should possess a high level of attention to detail to ensure accuracy in analyzing and interpreting data.\nIT professionals should have an awareness of cybersecurity concepts, as fraud detection often involves digital data.\n\nMethod of Delivery:\n\nIn-Person: The course will be conducted at a physical location. Participants will engage in face-to-face instruction, hands-on exercises, and group discussions.\n\n\nOnline: The course will be delivered through a virtual learning platform. Participants will have access to course materials, lectures, and interactive exercises\n\nCompletion 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.\nCourse Outline:\nModule 1: Introduction to Fraud Detection with Data Analytics\n\nOverview of the role of data analytics in fraud detection\nUnderstanding the advantages and challenges of using data analytics in fraud prevention\n\nModule 2: Types of Fraud and Data Analytics Applications\n\nExploring common types of fraud (financial, employee, vendor, etc.)\nIdentifying specific data analytics techniques for each type of fraud\n\nModule 3: Data Collection and Preparation\n\nBest practices for collecting and preparing data for fraud detection\nAddressing data quality issues and ensuring data accuracy\n\nModule 4: Data Analysis Techniques for Fraud Detection\n\nIntroduction to statistical analysis, pattern recognition, and anomaly detection\nMachine learning algorithms for fraud detection\nSocial network analysis and other advanced techniques\n\nModule 5: Building Fraud Detection Models\n\nDeveloping and training predictive models for fraud detection\nModel validation and assessing the effectiveness of fraud detection algorithms\n\nModule 6: Visualization and Interpretation of Results\n\nPresenting data analytics findings in a visually compelling manner\nInterpreting results and making informed decisions based on analytics outcomes\n\nModule 7: Case Studies and Real-Life Applications\n\nAnalyzing real-world fraud cases where data analytics played a crucial role\nLearning from successful and unsuccessful applications of data analytics in fraud detection\n\nModule 8: Ethical Considerations in Fraud Detection with Data Analytics\n\nAddressing ethical issues related to data privacy and security\nEnsuring responsible use of data analytics in fraud prevention\n\nModule 9: Implementing Data Analytics in Fraud Prevention Programs\n\nIntegrating data analytics into existing fraud prevention programs\nDeveloping a roadmap for successful implementation\n\nModule 10: Challenges and Future Trends\n\nDiscussing challenges in fraud detection with data analytics\nExploring emerging trends and technologies in the field\n\nRegister for the training here\n\n						\n							window.hsFormsOnReady = window.hsFormsOnReady || [];\n							window.hsFormsOnReady.push(()=>{\n								hbspt.forms.create({\n									portalId: 144360481,\n									formId: "b58e30f6-94ef-45b1-b0ec-ab6f46f81108",\n									target: "#hbspt-form-1791023263000-6963530697",\n									region: "eu1",\n									\n							})});\n						\n						\n
URL:https://acfeuganda.com/events/detecting-fraud-with-data-analytics/
ORGANIZER;CN=ACFE Uganda Chapter:MAILTO:admin@acfeuganda.com
CATEGORIES:Short Trainings
LOCATION:Opposite St. Luke Church
ATTACH;FMTTYPE=image/jpeg:https://acfeuganda.com/wp-content/uploads/2024/02/The-importance-of-Data-and-analytics-in-fraud-prevention-copy-min-1.jpg
END:VEVENT
END:VCALENDAR