Data Analytics for Internal Auditors Training Course

Duration 10 days 20 modules · 65 contact hours
Level Intermediate Certificate on completion
Who attends Internal Auditors, Senior Internal Auditors and others
Fee from USD 2,200 KSh 190,000 for Kenyan organisations
Where 52 cities + online See dates by city
Next start 9 Nov 2026 Course code SD-A-096

Before you join: Participants should understand the purpose of internal audit, basic control concepts, audit evidence and common business processes such as procure-to-pay, payroll or record-to-report.

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

Internal auditors are expected to examine larger populations, identify exceptions quickly and support conclusions with traceable evidence. Manual sampling and spreadsheet-only testing can miss patterns in duplicate payments, inappropriate access, revenue cut-off, journal entries, procurement conflicts and policy breaches. Data analytics gives audit teams a repeatable way to obtain data, assess quality, test controls, investigate anomalies and communicate findings that management can act upon.

This course teaches internal auditors how to apply data analysis throughout the audit lifecycle. Participants frame audit questions, map business processes to data fields, assess data reliability, use Excel, SQL and Power BI, and apply tests for completeness, duplicates, gaps, thresholds, ageing, sequence breaks, segregation-of-duties conflicts and unusual transactions. The programme also covers risk-based scoping, audit sampling, continuous auditing, fraud indicators, evidence documentation, dashboard design and reporting under the Global Internal Audit Standards.

Teaching combines concise instructor briefings with guided case work, team analysis, audit simulations and structured application planning. Cases use realistic data from accounts payable, payroll, procurement, inventory, customer refunds and general ledger processes. Participants progressively build an audit analytics workpaper package during the course, culminating in a complete analysis of a defined audit risk. They leave with reusable templates, test specifications, evidence records and an implementation plan for introducing analytics into their own audit assignments.

The course suits internal auditors, audit managers, risk professionals and finance control staff who already understand audit objectives and want practical analytical methods without becoming data scientists. It is particularly relevant for teams seeking stronger population testing, more focused fieldwork and clearer evidence for audit findings.

What you will be able to do

  • Define audit analytics questions, risks, populations and testable control objectives
  • Assess source-data completeness, accuracy, validity and audit suitability
  • Build repeatable Excel and SQL tests for financial and operational transactions
  • Apply anomaly detection methods to identify exceptions requiring audit investigation
  • Document analytics procedures, evidence and conclusions in audit-ready workpapers
  • Develop Power BI dashboards that communicate audit risks and control failures
  • Design continuous auditing routines with defined thresholds, ownership and escalation
  • Present a complete risk-based audit analytics case with defensible findings

Benefits of attending

For you

  • Produce population-level audit tests rather than relying only on manual samples
  • Use SQL queries to investigate financial and operational transaction patterns
  • Create evidence-backed workpapers for analytics-based audit findings
  • Recognise fraud and control-failure indicators within transactional data
  • Present audit exceptions through focused dashboards and management reports

For your organisation

  • Increase audit coverage across high-volume financial and operational populations
  • Identify control failures earlier through repeatable exception testing
  • Reduce fieldwork time spent manually sorting and reconciling transactions
  • Strengthen audit evidence supporting findings, ratings and management actions
  • Establish practical foundations for continuous auditing and risk monitoring

Course modules

20 modules over 10 days

Participants can define where data analytics fits within a risk-based internal audit engagement.

  • Audit universe mapping to data sources, processes, systems and owners
  • Risk-based audit planning using IIA risk assessment principles
  • Global Internal Audit Standards requirements for evidence and documentation
  • Analytics use cases across assurance, advisory and fraud investigations
  • Population testing compared with judgemental and statistical sampling approaches
  • Control objectives translated into measurable data conditions and exceptions

Participants can convert audit risks and control objectives into clear analytical test specifications.

  • Audit assertions linked to transactions, master data and system logs
  • Condition-criteria-cause-effect structure for analytics-supported audit findings
  • Test specifications defining population, fields, logic, thresholds and outputs
  • Hypotheses distinguishing expected patterns from potentially abnormal activity
  • Business rules documented with process owners before data extraction
  • Exception definitions aligned with policy, delegation and approval requirements

Participants can plan defensible data requests and trace extracted records to source systems.

  • Source-system mapping for enterprise resource planning and supporting applications
  • Data request schedules specifying fields, periods, formats and ownership
  • Extract-transform-load steps recorded for reproducible audit evidence
  • Read-only access considerations for audit independence and data protection
  • Unique identifiers linking invoices, suppliers, payments and purchase orders
  • Data lineage records connecting analytical outputs to original source files

Participants can assess whether a dataset is complete, accurate and fit for audit testing.

  • Completeness checks using record counts, control totals and period reconciliations
  • Accuracy testing against source documents, reports and system extracts
  • Validity rules for dates, amounts, codes and mandatory fields
  • Duplicate-record detection using keys, fuzzy matches and transaction attributes
  • Missing-value analysis separating legitimate blanks from data quality failures
  • Data profiling tables documenting distributions, ranges and unexpected values

Participants can prepare transaction data in Excel for controlled audit analysis.

  • Structured tables, named ranges and data types for controlled analysis
  • Power Query imports from workbooks, text files and folder sources
  • Cleaning dates, currencies, identifiers and inconsistent text values
  • Lookup methods matching transactions to approved master-data records
  • Calculated fields for ageing, thresholds, variances and approval limits
  • Refreshable query steps documented within the audit workpaper file

Participants can build Excel tests that identify common financial control exceptions.

  • Duplicate payment testing using invoice numbers, amounts and supplier identifiers
  • Gap analysis of purchase orders, cheque numbers and inventory movements
  • Ageing analysis for overdue advances, receivables and unresolved exceptions
  • Threshold testing against delegation-of-authority approval limits
  • Benford's Law screening for unusual leading-digit transaction distributions
  • PivotTable summaries revealing concentration by supplier, approver and cost centre

Participants can write basic SQL queries to retrieve and filter audit populations.

  • Relational database concepts: tables, records, fields, keys and relationships
  • SELECT, FROM and WHERE statements for targeted transaction retrieval
  • AND, OR and IN conditions for audit rule filtering
  • ORDER BY and DISTINCT clauses for reviewing unique values
  • Aggregate functions counting records and summing transaction amounts
  • Query comments documenting audit purpose, assumptions and test parameters

Participants can combine related datasets and test control conditions using SQL.

  • INNER JOIN matching transactions with approved supplier master records
  • LEFT JOIN identifying unmatched records and missing related documentation
  • GROUP BY and HAVING clauses for duplicate transaction analysis
  • CASE expressions classifying exceptions against defined audit rules
  • Date functions testing cut-off, ageing and weekend transaction patterns
  • SQL query outputs retained with parameters and execution dates

Participants can analyse process data to identify control breaks and inefficient paths.

  • Process maps connecting events, controls, systems and responsible roles
  • Procure-to-pay event sequences tested for required approval steps
  • Three-way match analysis across purchase orders, receipts and invoices
  • Cycle-time analysis identifying delayed approvals and post-period processing
  • Rework indicators measured through repeated changes and reversals
  • Control design gaps distinguished from operating effectiveness failures

Participants can identify access conflicts and incompatible activity within process data.

  • Segregation-of-duties matrices linking roles to incompatible process activities
  • User access extracts reconciled to current employee and contractor lists
  • Conflict testing for vendor creation, invoice entry and payment release
  • Privileged access reviews using administrator role and activity logs
  • Mitigating controls assessed for frequency, evidence and independent review
  • Access exceptions prioritised by transaction value and risk exposure

Participants can test journal entries for indicators of error, override and manipulation.

  • Journal populations profiled by user, account, period and source
  • Manual entries tested against authorised preparer and approver lists
  • Late-posting analysis for entries recorded after reporting cut-off
  • Round-number and unusual-description tests supporting further investigation
  • Reversal patterns reviewed for temporary or unsupported adjustments
  • High-risk journals selected using combined amount, timing and access criteria

Participants can apply focused analytical procedures to investigate fraud risk indicators.

  • Fraud triangle indicators connected to observable transaction and access data
  • Vendor master analysis for duplicate bank accounts and addresses
  • Employee-vendor matching using names, contact details and payment accounts
  • Split-purchase testing around tender and approval thresholds
  • Sequential invoice patterns examined for duplicate or fabricated billing
  • Exception triage separating data anomalies from evidence of misconduct

Participants can select suitable sampling methods and interpret analytical results responsibly.

  • Population, sampling unit and audit objective defined before selection
  • Random, systematic and stratified sampling methods for audit testing
  • Attribute sampling for testing control compliance rates
  • Monetary-unit sampling for detecting material overstatement risks
  • Confidence, precision and expected deviation rate explained practically
  • Sampling limitations documented alongside population-level analytics results

Participants can create audit dashboards that make exceptions and trends understandable.

  • Power BI data model relationships connecting audit populations and dimensions
  • Measures calculating exception rates, values and ageing indicators
  • Slicers filtering findings by entity, period, owner and risk rating
  • Drill-through pages supporting review from dashboard to transaction detail
  • Visual selection matched to audit questions and management decisions
  • Dashboard annotations distinguishing facts, exceptions and audit conclusions

Participants can communicate analytical findings clearly to audit management and process owners.

  • Exception heat maps prioritising issues by impact and recurrence
  • Trend charts showing control performance over reporting periods
  • Pareto analysis identifying major sources of exceptions and losses
  • Narrative reporting linking tests, evidence, findings and recommendations
  • Root-cause prompts separating symptoms from process and control causes
  • Management responses assessed for ownership, deadlines and verification criteria

Participants can design practical continuous auditing routines for selected control risks.

  • Continuous auditing distinguished from continuous monitoring responsibilities
  • Risk triggers defining when automated tests require audit attention
  • Test frequency set according to transaction volume and risk velocity
  • Threshold calibration balancing false positives against missed exceptions
  • Exception workflow assigning review, investigation, escalation and closure
  • Control owner reporting schedules linked to audit follow-up procedures

Participants can manage audit data in line with confidentiality, retention and ethical requirements.

  • Data classification applied to personal, financial and confidential records
  • Least-privilege access controls for audit datasets and analytical files
  • Retention schedules governing extracts, workpapers and investigation evidence
  • Personally identifiable information masked within training and reporting outputs
  • Ethical review of automated flags, bias and disproportionate scrutiny
  • Chain-of-custody records for sensitive data and forensic evidence

Participants can document analytics work so another auditor can reproduce and review it.

  • Workpaper structure recording objective, scope, method, results and conclusion
  • Data dictionaries defining fields, formats, business meaning and source
  • Test scripts versioned with assumptions, changes and reviewer comments
  • Cross-references linking exceptions to supporting records and findings
  • Quality review checks for reproducibility, relevance and sufficient evidence
  • Limitations stated where data gaps constrain audit conclusions

Participants can prepare a realistic plan for embedding analytics within an internal audit function.

  • Analytics maturity assessment covering people, process, data and technology
  • Use-case prioritisation based on risk, value, feasibility and data availability
  • Skills matrix identifying auditor, analyst and technology support requirements
  • Pilot charter defining scope, success measures, governance and timelines
  • Standard test library organised by process, risk and control objective
  • Implementation risks addressed through ownership, review and change management

Participants can build, review and present a complete audit analytics workpaper package for a defined business risk.

  • Capstone: audit objective and risk statement defined from case evidence
  • Data request, source assessment and quality checks documented clearly
  • Test logic built for selected controls and exception conditions
  • Exceptions investigated using transaction detail, patterns and supporting records
  • Audit workpapers reviewed for traceability, conclusions and evidence sufficiency
  • Management presentation delivers findings, recommendations and implementation priorities

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

What you take back to work

  1. Audit analytics test specification template
  2. Data quality assessment checklist
  3. SQL audit query library
  4. Excel exception-testing workbook
  5. Continuous auditing design canvas
  6. Audit analytics workpaper package

Who should attend

  • Internal Auditors — need population-based tests and stronger audit evidence
  • Senior Internal Auditors — need repeatable analytics for complex audit assignments
  • Audit Managers — need to direct analytics-enabled audit planning and review
  • Risk and Compliance Officers — need data-led monitoring of control failures
  • Finance Control Managers — need tests for transactions, journals and reconciliations
  • Fraud Investigators — need structured methods for identifying suspicious patterns

Skills and tools covered

Audit data testingSQL queryingExcel analysisControl exception analysisContinuous auditing designAnalytics reporting

Tools and standards

Global Internal Audit Standards, COSO Internal Control—Integrated Framework, SQL, Microsoft Power BI

How the course runs

Training methodology

The course uses short instructor briefings followed by guided analysis of realistic audit datasets. Participants work individually and in groups on cases involving accounts payable, payroll, procurement, journals and access controls. Simulations cover data requests, test design, exception investigation and audit reporting. Trainers demonstrate Excel, SQL and Power BI methods before participants apply them to structured audit scenarios. Each participant develops an application plan and builds an audit analytics workpaper package that can be adapted for a live assignment.

Requirements and prerequisites

Participants should understand the purpose of internal audit, basic control concepts, audit evidence and common business processes such as procure-to-pay, payroll or record-to-report. Experience using spreadsheets, including filters, formulas and pivot tables, is helpful. Participants should be comfortable interpreting financial or operational transaction data and discussing audit findings with process owners. Prior knowledge of SQL, Power BI, Python, statistics or data science is not required. The course teaches these analytical methods from an auditor's perspective rather than a technical programming perspective.

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

Participants should understand internal audit objectives, controls and common business processes. Basic spreadsheet confidence is useful, but SQL, Power BI, programming and advanced statistics are not required.

Participants should bring a laptop with Excel and, where permitted, access to Power BI Desktop. They may also bring anonymised examples of audit risks or reports to use during application planning.

The course is designed for internal auditors, senior auditors, audit managers, risk staff, compliance professionals and finance control personnel. It is most useful for professionals who need stronger evidence from transaction and system data.

The course starts with audit risks, control objectives, evidence and findings rather than generic business analysis. Examples and methods focus on audit populations, exception testing, workpapers, quality review and reporting.

Participants leave with reusable test specifications, workpaper structures and an implementation plan for a selected audit use case. They can adapt the methods to accounts payable, payroll, journals, procurement, access reviews and other audit areas.

The first part establishes audit analytics foundations, data quality methods and Excel or SQL testing. The later part covers advanced control tests, dashboards, continuous auditing, governance and a final capstone case.

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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