Module 1: Foundations of Fraud Risk and Power BI Setup

• Understanding the Fraud Triangle and common financial schemes
• Overview of the Power BI architecture (Desktop, Service, Gateway)
• Connecting to financial data sources (SQL, Excel, ERP exports)
• Initial data profiling and identification of high-risk fields
• Power Query Editor introduction: The M language for basic financial cleansing
• Practical session: Importing and profiling general ledger data from multiple sources, and merging tables based on transaction IDs using Power Query.

Module 2: Data Modeling and ETL for Fraud Scenarios

• Designing Star Schemas for transactional data (Facts and Dimensions)
• Creating a robust calendar dimension for time-based analysis
• Handling non-standard financial hierarchies (Chart of Accounts, Cost Centers)
• Applying advanced transformations: Data pivoting, conditional columns, and fuzzy matching
• Establishing key relationships and filtering context within the Data Model view
• Practical session: Building a normalized data model for Accounts Payable data, linking vendor details, invoices, and payment records.

Module 3: Core DAX for Financial Risk and Anomaly Metrics

• Introduction to DAX syntax, calculated columns, and explicit measures
• Understanding Filter Context and Row Context (the CALCULATE function)
• Time Intelligence DAX for comparative analysis (Year-over-Year, Rolling Averages)
• Creating variance measures and deviation scores for financial amounts
• Implementing DAX logic to flag transactions based on multiple risk criteria
• Practical session: Writing DAX measures to calculate the Z-score for transaction amounts and flagging any value falling outside two standard deviations.

Module 4: Visualization of Fraud Patterns and Red Flags

• Principles of visual analytics for anomaly detection (pre-attentive processing)
• Using scatter charts and line graphs to identify outliers in payment frequency or size
• Implementing Geographical maps to spot spatial anomalies in claims or vendor locations
• Utilizing histogram and box-and-whisker plots for distribution analysis of key metrics
• Conditional formatting and visual alerts to highlight high-risk transactions
• Practical session: Creating a scatter chart showing payments vs. time elapsed since invoice, applying conditional formatting to transactions near the weekend or on public holidays.

Module 5: Analytical Techniques for Transactional Fraud Detection

• Application of Benford’s Law analysis to detect digital manipulation of amounts
• Identifying duplicate payments and related party transactions using DAX
• Analyzing split transactions to circumvent authority limits or internal controls
• Developing measures to track round-sum transactions and sequential invoice numbering gaps
• Utilizing parameters and slicers for granular isolation of suspicious activity
• Practical session: Implementing a DAX calculation to check the first digit frequency of an expense column and visualize its deviation from the Benford’s Law expected distribution.

Module 6: Vendor, Procurement, and Accounts Payable Fraud Analysis

• Developing metrics to detect ghost vendors and vendor master data manipulation
• Analyzing vendor payment cycles and bank account changes for anomalies
• Implementing controls for purchase order and invoice matching discrepancies
• Using clustering techniques to group similar, potentially colluding vendors
• Creating a robust supplier risk scoring system using multiple indicators
• Practical session: Designing a vendor analysis dashboard that tracks new vendor setup, bank detail changes, and payment concentration by vendor.

Module 7: Employee and Expense Report Fraud Analytics

• Techniques for identifying personal expenditure hidden in corporate expense accounts
• Analyzing submission patterns: High frequency, low value, or clustered submissions
• Detecting duplicate or split expense items across different reports
• Creating a dashboard to compare employee spending against peer groups and policy limits
• Integrating HR data to flag expenses near termination dates or by specific departments
• Practical session: Building a matrix visual that cross-references employee names, expense categories, and approval dates to quickly highlight employees with unusual spending habits.

Module 8: Advanced Analytics and AI Visuals for Predictive Fraud

• Introduction to Power BI’s built-in AI visual capabilities (Key Influencers, Decomposition Tree)
• Utilizing the 'Find Anomalies' feature for automated outlier detection
• Implementing basic clustering (K-Means via R or Python integration) for grouping high-risk accounts
• Advanced forecasting visuals to predict potential budget overruns or expense spiking
• Understanding when to leverage Azure ML services and integrate predictions into Power BI
• Practical session: Using the Key Influencers visual to determine which factors (e.g., location, transaction type) are most likely to influence the occurrence of a high-risk transaction flag.

Module 9: Reporting Governance and Secure Dashboard Deployment

• Best practices for organizing and documenting fraud detection reports
• Implementing Row-Level Security (RLS) to restrict auditor or regional data access
• Publishing reports to the Power BI Service and setting up data refresh schedules
• Managing access, sharing, and defining user roles in the Power BI Service
• Creating alert systems and subscriptions for immediate notification of critical fraud flags
• Practical session: Setting up Row-Level Security on the transactional data model to ensure that an external auditor can only view data tagged with their specific audit ID.

Module 10: Integrated Fraud Detection Dashboard Capstone

• Review of all previous modules and integration of models and visuals
• Designing a single, high-level Executive Fraud Monitoring Dashboard
• Creating effective drill-through reports linking executive view to detailed transactional data
• Techniques for presenting complex analytical findings to non-technical stakeholders
• Review of successful fraud analytics implementation case studies
• Practical session: Participants will complete and present their final, fully functional Fraud Detection Dashboard, integrating at least three different analytical techniques learned during the course.

Trainer Experience

Our trainers are seasoned professionals with extensive experience in fraud detection, finance, and data analytics. They have successfully implemented fraud detection solutions in various industries and bring valuable insights to the course. Their expertise ensures that participants receive practical knowledge and real-world applications of the concepts covered.

Quality Statement

We are committed to delivering high-quality training that meets the needs of our participants. Our courses are designed to be engaging, informative, and relevant to the current industry landscape. We continuously update our course content to reflect the latest trends and advancements in the field.

Tailor-Made Courses

We offer tailor-made courses to suit the specific needs of organizations. Our custom courses can be designed to address unique challenges, industry-specific requirements, and organizational goals. Contact us to discuss your custom training needs.

Payment Information

Payment should be made one week before the training starts.

Accommodation and Airport Pick-Up

We offer accommodation and airport pick-up services for participants attending the training. Please contact us for more details and arrangements.