IA Professional Series - M16 - Data Analytics in Audit (PDF)

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BENEFITS OF THIS PDF DOCUMENT

1. Provides comprehensive analytics opportunity assessment frameworks with data availability checklists, feasibility scoring models, and business case templates enabling systematic identification and justification of high-value analytics initiatives.
2. Delivers ready-to-use analytical procedures including duplicate detection, completeness testing, gap analysis, aging analysis, and three-way matching with step-by-step implementation guidance and SQL query examples for immediate application.
3. Equips audit functions with capability-building frameworks including maturity assessment models, tool selection criteria, training program designs, and Analytics Center of Excellence implementation roadmaps for sustainable analytics development.

DATA ANALYTICS PDF DESCRIPTION

This product (IA Professional Series - M16 - Data Analytics in Audit) is a 51-page PDF document, which you can download immediately upon purchase.

The Internal Audit Professional Series is a comprehensive 18-module training program designed to transform internal audit professionals from foundational competence to strategic excellence. Developed and published by Business Excellence, a leading advisory and consultancy firm specializing in internal audit, risk management, corporate governance, and business strategy, this series delivers practical, actionable knowledge aligned with the Global Internal Audit Standards (GIAS 2024).

Module 16: Data Analytics in Audit provides a practical implementation guide for leveraging data analytics effectively in audit work. This module equips professionals with ready-to-use analytical procedures, data preparation techniques, and capability-building frameworks regardless of technical background.

This module covers 7 comprehensive sections:

1. Analytics Opportunity Assessment – Identifying where analytics can help through data availability evaluation checklists covering data existence, storage, format, access, and quality considerations. Features analytics feasibility assessment with weighted scoring, quick-win identification frameworks using value-effort matrices, and analytics business case templates.

2. Data Acquisition and Preparation – Getting data ready through data request templates specifying elements, time periods, formats, and extraction criteria. Includes data validation procedures for record counts, control totals, completeness, and field validation, data cleansing checklists addressing missing values, inconsistent formats, and duplicates, and data integrity verification steps.

3. Common Audit Analytics Procedures – Ready-to-use tests including transaction completeness testing through sequence analysis, cross-system reconciliation, and period-over-period comparison. Features duplicate detection procedures with exact match, fuzzy matching, and near-duplicate identification, gap and sequence analysis for checks, invoices, and transactions, aging analysis procedures, and three-way matching tests.

4. Analytical Technique Application – Advanced methods including trend analysis procedures for identifying patterns and anomalies over time, ratio analysis methodology covering financial, operational, and compliance ratios with benchmark comparisons, and stratification analysis guide for risk-based population segmentation.

5. Anomaly Investigation Process – Investigating findings through exception prioritization frameworks based on magnitude, freq

Source: Best Practices in Data Analytics, Audit Management PDF: IA Professional Series - M16 - Data Analytics in Audit PDF (PDF) Document, Amer Morgan