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Audits provide essential accountability and transparency over government programs. Given the current challenges facing governments and their programs, the oversight provided through auditing is more critical than ever. Government auditing provides the objective analysis and information needed to make the decisions necessary to help create a better future. The professional standards presented in this 2018 revision of Government Auditing Standards (known as the Yellow Book) provide a framework for performing high-quality audit work with competence, integrity, objectivity, and independence to provide accountability and to help improve government operations and services. These standards, commonly referred to as generally accepted government auditing standards (GAGAS), provide the foundation for government auditors to lead by example in the areas of independence, transparency, accountability, and quality through the audit process. This revision contains major changes from, and supersedes, the 2011 revision.
Brimming with commonsense advice delivered in a conversational, easy-to-read style, Internal Audit Reports Post Sarbanes-Oxley: A Guide to Process-Driven Reporting helps you transform raw data into useable information and then translate that information into actionable messages while complying with the SOX Act.
Policymakers and program managers are continually seeking ways to improve accountability in achieving an entity's mission. A key factor in improving accountability in achieving an entity's mission is to implement an effective internal control system. An effective internal control system helps an entity adapt to shifting environments, evolving demands, changing risks, and new priorities. As programs change and entities strive to improve operational processes and implement new technology, management continually evaluates its internal control system so that it is effective and updated when necessary. Section 3512 (c) and (d) of Title 31 of the United States Code (commonly known as the Federal Managers' Financial Integrity Act (FMFIA)) requires the Comptroller General to issue standards for internal control in the federal government.
This volume, developed by the Observatory together with OECD, provides an overall conceptual framework for understanding and applying strategies aimed at improving quality of care. Crucially, it summarizes available evidence on different quality strategies and provides recommendations for their implementation. This book is intended to help policy-makers to understand concepts of quality and to support them to evaluate single strategies and combinations of strategies.
The explosion of data analytics in the auditing profession demands a different kind of auditor. Auditing: A Practical Approach with Data Analytics prepares students for the rapidly changing demands of the auditing profession by meeting the data-driven requirements of today's workforce. Because no two audits are alike, this course uses a practical, case-based approach to help students develop professional judgement, think critically about the auditing process, and develop the decision-making skills necessary to perform a real-world audit. To further prepare students for the profession, this course integrates seamless exam review for successful completion of the CPA Exam.
FISCAM presents a methodology for performing info. system (IS) control audits of governmental entities in accordance with professional standards. FISCAM is designed to be used on financial and performance audits and attestation engagements. The methodology in the FISCAM incorp. the following: (1) A top-down, risk-based approach that considers materiality and significance in determining audit procedures; (2) Evaluation of entitywide controls and their effect on audit risk; (3) Evaluation of general controls and their pervasive impact on bus. process controls; (4) Evaluation of security mgmt. at all levels; (5) Control hierarchy to evaluate IS control weaknesses; (6) Groupings of control categories consistent with the nature of the risk. Illus.
This book is about how to reduce large data sets down to the critical few high-risk subsets.