Course Introduction

Automated Audit Testing and Intelligent Controls Monitoring Training Course provides an advanced and practical framework for transforming traditional audit testing into efficient, repeatable, technology-enabled assurance activities. The programme examines how automation can be applied to control testing, transaction analysis, exception identification, compliance verification, and monitoring processes while maintaining appropriate professional judgment, evidence quality, governance, and audit independence.

The course develops participants’ understanding of how automated audit testing differs from conventional manual testing and how intelligent controls monitoring can provide more timely visibility into control performance. Participants explore the design of automated procedures, testing logic, data requirements, exception thresholds, alerts, workflows, and monitoring routines that can operate continuously or at defined intervals across critical business processes.

Participants will learn how to identify suitable audit procedures for automation by assessing transaction volumes, control characteristics, data availability, repeatability, risk exposure, and expected assurance value. The programme addresses data extraction, preparation, validation, analytical routines, automated sampling, exception testing, reconciliations, duplicate detection, access reviews, segregation-of-duties testing, and automated compliance checks across diverse organisational environments.

Particular attention is given to intelligent monitoring techniques that move beyond simple rule-based testing. The course explores anomaly detection, statistical analysis, machine learning, process mining, behavioural analytics, risk scoring, pattern recognition, and artificial intelligence as tools for identifying unusual activity and prioritising audit attention. It also examines the challenges associated with false positives, false negatives, model drift, explainability, data quality, and inappropriate reliance on automated outputs.

The programme further addresses the governance and control requirements surrounding automated audit technology. Participants examine audit evidence, analytical logic, script validation, access controls, data lineage, change management, quality assurance, documentation, cybersecurity, privacy, and system dependencies. Emerging applications of generative AI, intelligent agents, robotic process automation, cloud analytics, and real-time event monitoring are considered alongside the professional responsibilities of auditors using these technologies.

By the end of the programme, participants will be able to design and implement practical automated audit testing and intelligent controls-monitoring approaches that improve audit efficiency, consistency, coverage, and timeliness. The course equips audit and assurance professionals to build scalable technology-enabled testing programmes while preserving professional skepticism, defensible evidence, effective exception management, and strong alignment with organisational risks and control objectives.

Duration

10 days

Who Should Attend

• Chief Audit Executives, Heads of Internal Audit, and Internal Audit Directors leading digital audit transformation and assurance innovation.

• Internal Audit Managers and Supervisors responsible for developing automated audit procedures and technology-enabled testing methodologies.

• Internal Auditors seeking advanced capabilities in automated control testing, exception analysis, and intelligent monitoring.

• IT Auditors and Technology Assurance Professionals working with systems, applications, automated controls, and technology-generated audit evidence.

• Internal Control Specialists responsible for designing, testing, and monitoring preventive and detective controls.

• Risk Managers involved in automated risk indicators, control monitoring, risk analytics, and emerging risk identification.

• Data Analysts supporting audit teams with data extraction, analytical testing, exception detection, and dashboard development.

• Fraud Risk Professionals seeking to strengthen automated transaction monitoring and intelligent fraud detection capabilities.

• Compliance Professionals responsible for recurring regulatory, policy, and control compliance testing.

• Finance and Accounting Professionals seeking automated approaches to transaction testing, reconciliation, and financial control monitoring.

• Governance, Risk and Compliance Professionals implementing integrated monitoring and assurance platforms.

• Control Owners and Process Owners seeking more effective and timely visibility over control performance.

• Cybersecurity and Information Security Professionals involved in automated security control testing and continuous monitoring.

• Audit Transformation and Digital Assurance Professionals responsible for implementing automation, analytics, and emerging technologies.

Course Objectives

• Develop an advanced understanding of automated audit testing, intelligent controls monitoring, continuous assurance, and their application across modern internal audit environments.

• Identify audit procedures and control activities that are appropriate for automation based on risk, repeatability, data availability, transaction volume, control characteristics, and assurance requirements.

• Design automated audit tests that translate audit objectives, control criteria, business rules, and risk indicators into reliable and repeatable analytical procedures.

• Develop robust approaches for acquiring, preparing, validating, transforming, and governing data used by automated audit testing and intelligent controls-monitoring processes.

• Apply automated exception testing to identify unusual transactions, duplicate records, policy breaches, approval failures, access violations, and other control exceptions.

• Establish effective thresholds, rules, indicators, alerts, risk scores, and prioritisation methods for intelligent monitoring while managing false positives and false negatives.

• Apply advanced analytics, anomaly detection, machine learning, process mining, and behavioural analysis to improve the identification of emerging control and risk issues.

• Evaluate the reliability and effectiveness of automated audit scripts, analytical models, monitoring rules, data transformations, and technology-generated audit evidence.

• Integrate automated testing with audit workflows, exception management, investigation procedures, remediation tracking, reporting, and follow-up assurance activities.

• Apply appropriate governance over artificial intelligence, intelligent automation, generative AI, and machine-learning technologies used in audit testing and controls monitoring.

• Strengthen audit documentation, evidence retention, quality assurance, change management, access control, data privacy, and professional judgment in technology-enabled audit engagements.

• Develop practical implementation strategies for building scalable, sustainable, and risk-focused automated audit testing and intelligent controls-monitoring capabilities.

Comprehensive Course Outline

Module 1: Foundations of Automated Audit Testing and Intelligent Controls Monitoring

• Evolution from manual audit testing toward automated, repeatable, data-driven, and intelligent assurance methodologies.

• Definitions, characteristics, benefits, limitations, and practical applications of automated audit testing and intelligent controls monitoring.

• Differences between automated testing, continuous auditing, continuous monitoring, continuous controls monitoring, and traditional audit procedures.

• Roles of internal audit, management, control owners, risk functions, IT teams, and data specialists within automated assurance environments.

Module 2: Automated Audit Governance, Methodology and Operating Models

• Governance principles, policies, methodologies, responsibilities, and accountability arrangements for automated audit testing programmes.

• Establishing audit independence, professional judgment, objectivity, oversight, and appropriate reliance on automated testing outputs.

• Designing centralised, decentralised, hybrid, and embedded operating models for automated audit analytics and controls monitoring.

• Integrating automated testing with internal audit strategy, risk management, governance, GRC platforms, and combined assurance frameworks.

Module 3: Risk-Based Automation and Audit Test Selection

• Identifying high-risk, high-volume, repetitive, data-rich, and control-sensitive audit areas suitable for automated testing.

• Evaluating automation opportunities using risk exposure, transaction frequency, control dependency, data accessibility, and expected audit value.

• Translating risk and control assessments into automated audit objectives, procedures, test criteria, and monitoring requirements.

• Prioritising automation investments based on assurance coverage, efficiency gains, sustainability, implementation complexity, and organisational needs.

Module 4: Audit Data Acquisition, Preparation and Validation

• Identifying relevant data sources across ERP systems, databases, applications, spreadsheets, cloud platforms, and operational systems.

• Designing data extraction processes that preserve completeness, accuracy, timeliness, integrity, security, and auditability.

• Data cleansing, transformation, standardisation, reconciliation, validation, and preparation for automated audit procedures.

• Establishing data lineage, ownership, access controls, retention, documentation, and governance for recurring analytical audit datasets.

Module 5: Automated Control Testing and Control Logic

• Translating control objectives, policies, procedures, and business rules into automated audit tests and monitoring logic.

• Designing automated tests for approvals, authorisations, reconciliations, segregation of duties, access rights, thresholds, and policy compliance.

• Testing preventive, detective, corrective, configurable, and automated application controls through repeatable analytical procedures.

• Evaluating control exceptions, overrides, control failures, recurring breaches, and changes in control performance through automated testing.

Module 6: Automated Transaction Testing and Exception Detection

• Automating transaction-level audit tests for completeness, accuracy, validity, occurrence, authorisation, and compliance with defined criteria.

• Detecting duplicate transactions, unusual values, unusual timing, sequential anomalies, round amounts, reversals, and unexpected transaction relationships.

• Designing exception rules, thresholds, tolerances, alerts, risk scores, and prioritisation mechanisms for automated audit findings.

• Managing exception volumes, false positives, false negatives, alert fatigue, investigation workflows, and escalation requirements.

Module 7: Intelligent Financial and Accounting Controls Monitoring

• Automated testing of general ledger transactions, journal entries, accounts payable, accounts receivable, payments, and financial reporting controls.

• Continuous monitoring of reconciliations, unusual postings, manual adjustments, reversals, duplicate payments, and unexpected accounting activity.

• Applying intelligent analytics to identify financial control deterioration, unusual transaction behaviour, and recurring accounting exceptions.

• Integrating automated financial testing with financial reporting assurance, fraud risk management, management controls, and audit follow-up.

Module 8: Procurement, Payroll, Revenue and Operational Controls Monitoring

• Automated procurement testing covering purchase orders, supplier transactions, invoices, approvals, contracts, pricing, and payment controls.

• Intelligent payroll monitoring for ghost employees, duplicate accounts, unusual compensation, overtime anomalies, master-data changes, and termination risks.

• Automated revenue controls testing covering billing, collections, adjustments, refunds, discounts, write-offs, receivables, and leakage indicators.

• Applying intelligent monitoring across inventory, expenses, operational workflows, service delivery, asset management, and process compliance.

Module 9: Fraud Detection and Intelligent Transaction Monitoring

• Integrating fraud risk indicators, suspicious patterns, behavioural signals, and transaction anomalies into automated audit testing.

• Identifying potential duplicate payments, fictitious transactions, conflicts of interest, unusual relationships, collusion indicators, and control overrides.

• Applying statistical analysis, anomaly detection, network analysis, behavioural profiling, and pattern recognition to fraud-related monitoring.

• Establishing appropriate interfaces between automated audit alerts, fraud investigations, whistleblowing channels, compliance processes, and case management.

Module 10: IT, Cybersecurity and Access Control Automation

• Automated testing of user access, privileged accounts, dormant users, excessive permissions, and segregation-of-duties conflicts.

• Monitoring IT general controls, application controls, system configurations, change activity, and operational technology controls through automated routines.

• Applying intelligent monitoring to cybersecurity events, authentication anomalies, vulnerabilities, endpoint activity, and security control exceptions.

• Integrating system logs, security data, application information, and automated alerts while preserving evidence integrity and investigative traceability.

Module 11: Advanced Analytics, Anomaly Detection and Process Mining

• Applying descriptive, diagnostic, statistical, predictive, and behavioural analytics to automated audit testing and control monitoring.

• Designing anomaly-detection approaches for identifying unusual transactions, unexpected behaviours, outliers, and emerging control risks.

• Using process mining to identify process deviations, control bypasses, bottlenecks, rework, unusual workflows, and non-compliant process paths.

• Evaluating analytical model performance, interpretability, data quality, threshold calibration, and the appropriate role of professional judgment.

Module 12: Artificial Intelligence and Machine Learning for Intelligent Monitoring

• Applying machine learning and artificial intelligence to anomaly detection, classification, risk prioritisation, behavioural monitoring, and control analytics.

• Understanding model development, training data, validation, model drift, bias, explainability, accuracy, and performance monitoring within audit applications.

• Establishing human oversight and governance for AI-generated audit alerts, risk scores, recommendations, classifications, and control assessments.

• Evaluating generative AI and AI-agent applications for audit testing while addressing confidentiality, hallucination, evidence integrity, and accountability risks.

Module 13: Intelligent Automation, RPA and Integrated Audit Technologies

• Applying robotic process automation to repetitive audit procedures, data extraction, reconciliations, control checks, and recurring testing activities.

• Integrating audit analytics with ERP systems, GRC platforms, business intelligence tools, databases, APIs, cloud services, and monitoring platforms.

• Designing automated workflows for exception routing, investigation assignment, escalation, remediation tracking, and management reporting.

• Managing technology dependencies, automation failures, system changes, access rights, cybersecurity, and operational resilience within automated audit environments.

Module 14: Intelligent Controls Monitoring Dashboards and Reporting

• Designing dashboards that communicate control performance, exception trends, risk indicators, unresolved issues, and emerging areas of concern.

• Developing key control indicators, key risk indicators, exception metrics, ageing measures, thresholds, and management performance measures.

• Applying visualisation techniques to convert large volumes of automated testing results into clear, prioritised, and actionable assurance information.

• Establishing reporting and escalation mechanisms for process owners, senior management, audit committees, risk functions, and other stakeholders.

Module 15: Automated Audit Evidence, Quality Assurance and Remediation

• Establishing evidence requirements for automated tests, analytical routines, scripts, system logs, exception reports, dashboards, and monitoring outputs.

• Documenting data sources, testing logic, assumptions, analytical procedures, validation activities, exceptions, conclusions, and changes to automated routines.

• Performing quality assurance over scripts, analytical models, monitoring rules, data transformations, automated workflows, and recurring audit procedures.

• Connecting automated exceptions to root cause analysis, corrective actions, remediation validation, follow-up testing, and sustainable control improvement.

Module 16: Advanced Intelligent Assurance Strategy and Emerging Technologies

• Developing an enterprise-wide strategy for automated audit testing and intelligent controls monitoring aligned with organisational risks and audit priorities.

• Establishing maturity assessments, implementation roadmaps, technology investments, capability requirements, performance measures, and governance arrangements.

• Preparing for emerging technologies including agentic AI, real-time event monitoring, autonomous analytics, digital twins, intelligent workflows, and adaptive controls.

• Building future-ready assurance capabilities that combine automation, human judgment, advanced analytics, continuous monitoring, and integrated risk intelligence.

Training Approach

This course will be delivered by our skilled trainers who have vast knowledge and experience as expert professionals in the fields. The course is taught in English and through a mix of theory, practical activities, group discussion and case studies. Course manuals and additional training materials will be provided to the participants upon completion of the training.

Tailor-Made Course

This course can also be tailor-made to meet organization requirement. For further inquiries, please contact us on: Email: training@upskilldevelopment.com Tel: +254 721 331 808

Training Venue 

The training will be held at our Upskill Training Centre. We also offer training for a group (at a discount of 10% to 50%) at requested location all over the world. The Onsite course fee covers the course tuition, training materials, two break refreshments, buffet lunch, airport transfers, Upskill gift package, and guided tour.

Visa application, travel expenses, dinners, accommodation, insurance, and other personal expenses are catered by the participant

Certification

Participants will be issued with Upskill certificate upon completion of this course.

Airport Pickup and Accommodation

Airport pickup and accommodation is arranged upon request. For booking contact our Training Coordinator through Email: training@upskilldevelopment.com, +254 721 331 808

Terms of Payment:

Unless otherwise agreed between the two parties’ payment of the course fee should be done 3 working days before commencement of the training so as to enable us to prepare better.