Audit Analytics with Power BI Training Course

Duration 5 days 10 modules · 33 contact hours
Level Intermediate Certificate on completion
Who attends Internal Auditors, External Auditors and others
Fee from USD 1,100 KSh 95,000 for Kenyan organisations
Where 52 cities + online See dates by city
Next start 16 Nov 2026 Course code SD-A-137

Before you join: Participants should understand basic audit concepts, including audit objectives, controls, assertions, materiality and evidence.

Dates & prices

Two start dates a month in every city, and live online on weekdays or weekends. Fees are per participant; classroom fees include tuition, materials, lunch and refreshments, and the certificate.

Suggest a date or city

Showing the next six months. Later dates, other cities and private deliveries for your team are available on request.

Course overview

Audit teams often receive large transaction extracts but lack a repeatable way to turn them into auditable tests, exception populations and clear findings. Spreadsheet-only approaches can be slow, difficult to refresh and vulnerable to undocumented changes. Power BI can help auditors test full populations, reconcile source data, identify unusual patterns and communicate exceptions, provided that data lineage, access controls, validation and audit evidence requirements are properly managed.

This course shows participants how to design audit analytics from risk assessment through reporting. Participants use Power Query to obtain, clean and reconcile audit data; build relationships and measures in Power BI; and apply DAX calculations to common audit tests. Coverage includes revenue trends, duplicate payments, vendor anomalies, journal-entry testing, payroll exceptions, segregation-of-duties indicators and continuous monitoring. The course also addresses documentation standards, data quality testing, exception investigation and the limits of visual analytics as audit evidence.

Teaching combines concise technical briefings with instructor-led demonstrations, realistic audit cases and structured group analysis. Participants work through finance and operational datasets resembling those used in internal audit, external audit and compliance reviews. They leave with a reusable audit analytics workpaper pack containing data-request specifications, validation procedures, test designs, Power BI report layouts and an implementation plan for a priority audit use case.

The course is suited to auditors and finance-control professionals who already understand core audit concepts and need practical Power BI methods for testing transactions and reporting findings. It is relevant to public-sector audit teams, banks, NGOs, multinational groups and large corporate internal audit functions.

What you will be able to do

  • Design risk-based audit analytics linked to assertions, controls and audit objectives
  • Prepare auditable datasets using Power Query extraction, cleansing and reconciliation methods
  • Build Power BI data models with appropriate relationships, dimensions and fact tables
  • Create DAX measures for exception testing, trend analysis and control monitoring
  • Perform full-population tests for duplicate payments, unusual journals and vendor anomalies
  • Validate analytics outputs using record counts, control totals and source-system reconciliations
  • Present audit exceptions through evidence-based Power BI reports and investigation workflows
  • Produce a documented audit analytics workpaper pack and implementation plan

Benefits of attending

For you

  • Build audit tests from full transaction populations rather than manual samples alone
  • Create reusable Power BI reports for journals, payments, vendors and payroll reviews
  • Document data transformations and reconciliations in defensible audit workpapers
  • Identify anomalies using DAX measures, thresholds and exception criteria
  • Communicate findings with clearer evidence trails and focused visual reports

For your organisation

  • Reduce manual audit testing time through refreshable transaction analytics
  • Improve coverage of high-risk populations, controls and fraud indicators
  • Create consistent audit analytics workpapers across audit teams and assignments
  • Strengthen evidence quality through documented data validation and lineage
  • Provide management with clearer exception reporting and monitoring indicators

Course modules

10 modules over 5 days

Participants can translate audit risks and assertions into defined analytics tests and exception criteria.

  • Audit assertions translated into testable questions, populations, and exception criteria
  • Risk assessment links fraud indicators, controls, and analytical procedures
  • ISA 315 risk factors mapped to available business data
  • Full-population testing compared with sampling, stratification, and targeted selections
  • Analytic procedures distinguished from substantive tests and control testing
  • Audit objectives documented through criteria, thresholds, owners, and evidence
  • Exception investigation workflow from detection through corroboration and conclusion

Participants can obtain, protect and validate data extracts suitable for audit analytics.

  • Data requests specify fields, periods, systems, formats, and record counts
  • Source-to-report lineage records extraction steps and transformation ownership
  • Reconcile record counts, control totals, and key financial balances
  • Personal data classification guides access, retention, and redaction decisions
  • Read-only extracts, secure storage, and version control protect evidence
  • Data quality dimensions assess completeness, accuracy, validity, timeliness, uniqueness
  • Document limitations, compensating procedures, and audit evidence implications

Participants can prepare traceable audit datasets using Power Query transformations and controls.

  • Import Excel, CSV, folder, database, and SharePoint audit extracts
  • Profile columns to identify nulls, errors, duplicates, and unexpected values
  • Apply data types before calculations, joins, dates, and monetary analysis
  • Remove non-business records using documented inclusion and exclusion rules
  • Merge master data with transactions using validated matching keys
  • Append monthly extracts into refreshable full-population audit datasets
  • Create transformation steps that preserve repeatability and reviewer traceability

Participants can build Power BI data models that support reliable audit testing and reporting.

  • Fact and dimension tables structured for transaction-level audit analysis
  • Star schema principles applied to general ledger and procurement data
  • Relationships tested for cardinality, direction, unmatched records, and duplicates
  • Calendar tables support period comparisons, cut-off tests, and ageing analysis
  • Master-data dimensions classify vendors, employees, accounts, and cost centres
  • Model measures separated from source transformations and descriptive attributes
  • Refresh dependencies documented for recurring audit and monitoring reports

Participants can write DAX measures that quantify exceptions, trends and control indicators.

  • DAX measure structure uses variables, filter context, and explicit calculations
  • CALCULATE modifies filters for scoped audit populations and control tests
  • COUNTROWS and DISTINCTCOUNT identify duplicates, repetitions, and unique entities
  • SUMX supports row-level calculations for invoice and journal testing
  • Date intelligence compares current, prior-period, and year-to-date activity
  • Conditional measures flag threshold breaches, missing fields, and unusual patterns
  • Measures validated against independent calculations and source-system totals

Participants can test revenue and procure-to-pay data for common financial control exceptions.

  • Revenue trends tested by customer, product, period, currency, and channel
  • Cut-off indicators identify transactions posted near reporting-period boundaries
  • Duplicate-payment tests compare supplier, invoice, amount, date, and reference
  • Vendor master analysis identifies shared addresses, bank accounts, and tax identifiers
  • Three-way match exceptions compare purchase orders, receipts, and invoices
  • Spend concentration highlights suppliers exceeding approval or tendering thresholds
  • Benford analysis assessed cautiously alongside business rationale and transaction populations

Participants can identify high-risk journals, payroll anomalies and fraud indicators using Power BI.

  • Journal-entry filters isolate manual postings, privileged users, and late entries
  • Unusual journal patterns assessed through account combinations and posting times
  • Round-number analysis identifies material entries requiring further corroboration
  • Payroll exceptions compare employee status, pay changes, and bank details
  • Duplicate bank accounts reveal potential ghost employees or shared payment details
  • Segregation-of-duties indicators compare requestor, approver, and payment roles
  • Fraud indicators documented as leads rather than conclusions or allegations

Participants can present auditable exceptions through focused Power BI reports and investigation records.

  • Exception dashboards prioritise value, frequency, risk rating, and control owner
  • Drill-through pages retain transaction detail supporting reviewer follow-up
  • Tooltips display supporting attributes without overcrowding the main report
  • Trend charts distinguish recurring control failures from isolated anomalies
  • Exception registers record rationale, evidence, status, action, and closure
  • Visual design avoids misleading scales, unsupported causation, and hidden filters
  • Management reporting separates preliminary leads from confirmed audit findings

Participants can define repeatable monitoring routines for priority controls and transaction risks.

  • Continuous auditing distinguished from continuous control monitoring responsibilities
  • Monitoring rules define population, trigger, threshold, frequency, and escalation
  • Refresh schedules align with source availability and control response windows
  • Risk ratings prioritise exceptions requiring immediate investigation or management action
  • Control owners receive actionable alerts with relevant transaction evidence
  • False-positive analysis refines rules without weakening control objectives
  • Monitoring governance assigns ownership, review cadence, and retention requirements

Participants can document a defensible audit analytics approach and plan its use on an audit assignment.

  • Analytics workpapers link risks, objectives, data, procedures, and conclusions
  • Data dictionaries define fields, calculations, coding values, and business meaning
  • Validation checklists evidence completeness, accuracy, reconciliation, and reviewer sign-off
  • Test specifications record logic, thresholds, exclusions, and expected exceptions
  • Audit findings connect exception patterns to criteria, cause, effect, and action
  • Report refresh procedures preserve reproducibility for subsequent audit periods
  • Application plans prioritise one audit use case, stakeholders, and milestones

Exercises and examples are adapted to each cohort's sectors and roles.

What you take back to work

  1. Risk-based audit analytics test design template
  2. Audit data request and data dictionary template
  3. Power Query transformation documentation checklist
  4. Power BI audit data model blueprint
  5. DAX exception-testing measure library
  6. Continuous auditing implementation plan

Who should attend

  • Internal Auditors — need repeatable testing methods for financial and operational data
  • External Auditors — need data-driven procedures supporting substantive and control testing
  • Audit Managers — need consistent analytics approaches across audit assignments
  • Compliance Officers — need transaction monitoring for policy breaches and irregularities
  • Finance Controllers — need stronger analysis of reconciliations, journals and payment controls
  • Risk Analysts — need Power BI methods for identifying control and fraud indicators

Skills and tools covered

Audit data preparationPower Query transformationDAX audit testingException analysisData quality validationAudit reporting

Tools and standards

Microsoft Power BI Desktop, Power Query, Data Analysis Expressions (DAX), ISA 315

How the course runs

Training methodology

The course combines short instructor briefings with guided Power BI demonstrations, audit case analysis, group exercises and individual build activities. Participants work with realistic general ledger, procurement, payroll and revenue datasets to define test objectives, prepare data, create measures and investigate exceptions. Facilitated discussions connect analytics outputs to audit assertions, control testing and evidence documentation. Each participant develops an application plan for a current or planned audit assignment, including data requirements, test logic, validation steps and reporting outputs.

Requirements and prerequisites

Participants should understand basic audit concepts, including audit objectives, controls, assertions, materiality and evidence. Experience using Excel for data review, filtering, pivot tables or reconciliations is expected. Participants should be comfortable reading transaction-level data such as general ledger, accounts payable, payroll or sales extracts. Prior Power BI experience is useful but not required; the course introduces the required Power Query, data modelling and DAX methods. Programming, SQL, advanced statistics and prior DAX coding are not required. Participants should bring a laptop capable of running Power BI Desktop where classroom arrangements permit.

A typical training day

08:30 – 10:30First session
10:30 – 10:45Refreshment break
10:45 – 12:30Second session
12:30 – 13:30Lunch and networking
13:30 – 15:00Third session
15:00 – 15:15Refreshment break
15:15 – 16:30Workshop and daily review

Live online deliveries follow the same structure in East Africa Time, with shorter screen blocks and longer breaks.

What the fee includes

  • Instruction by a practitioner facilitator
  • Full course workbook and materials
  • Exercise files, templates and case studies
  • Refreshments and lunch on classroom deliveries
  • Certificate of completion
  • Post-course application plan

Frequently asked questions

No prior Power BI experience is required, although participants should be comfortable working with Excel data and audit schedules. The course starts with data preparation and modelling before introducing DAX measures and audit reports.

Participants should bring a laptop capable of running Microsoft Power BI Desktop, subject to local classroom arrangements. A current audit workpaper, data-request list or example audit objective is useful for the application planning component.

It is designed for internal auditors, external auditors, compliance professionals, finance controllers and risk analysts. It is particularly useful for professionals who test financial transactions, controls or operational data.

General Power BI courses focus on reporting and business intelligence. This course uses Power BI specifically for audit planning, data validation, transaction testing, evidence documentation, exception investigation and control monitoring.

Participants can adapt the templates and test patterns to current audits involving general ledger, accounts payable, payroll, revenue or vendor data. The final application plan identifies a priority use case, required data, test logic and validation steps.

The programme contains ten modules of approximately three contact hours, supported by breaks and daily review time. Time is divided between audit analytics design, Power BI methods, common transaction tests, reporting and implementation planning.

Questions about this course?

Ask about dates, fees, group bookings or running it for your team. Our training team replies by email.

Ask on WhatsApp info@skillsetdevelopment.com

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