KPMG and MindBridge forge global alliance to transform digital audits
KPMG and MindBridge form global alliance, transforming digital audits with enhanced risk detection and next-gen AI audit technology
KPMG and MindBridge form global alliance, transforming digital audits with enhanced risk detection and next-gen AI audit technology
The audit industry has seen a bit of a shakeup in the past few years. New technologies, regulator crackdowns, big firms acquiring and merging, and a general push for improved processes and a review of age-old standards are all signs of new things on the horizon for our industry. But while there was a lot
ISA 315 (revised) and Data Analytics: Risk assessment procedures reimagined The revised standard has been published as of December 2020, and you might be wondering what impact it has on your firm’s risk assessment procedures and how you can address the requirements. There are many useful sources of information on the changes, notably the IAASB’s
Data analysis is a necessary skill for accountants. Are you ready for it? Find out what it takes, and what tech can help you along the way.
Audit busy season is a drain on auditors everywhere. With the added challenges of remote work, the industry is clambering for help. Our blog as you covered.
Artificial intelligence (AI) and machine learning (ML) technologies can streamline traditional audit procedures for Accounts Receivable (AR) and Accounts Payable (AP) in audits of financial statements.
When it comes specifically to journal entry testing, most auditors today have been using antiquated approaches and sampling techniques.
A new audit evidence standard has been released by the American Institute of Certified Public Accountants (AICPA) that includes significant updates around how technology and automation can be leveraged throughout the audit process. Here, we’ll examine this standard and some of the most significant examples of how the AICPA has explicitly considered the applicability of
Three ways Ai Auditor strengthens your audit planning The determination of where audit risks of material misstatement lie is a critical output of the audit planning process. Usually, identifying those risks is based on the auditors understanding of their client and the client’s operating environment. Auditors can now rely on a data-driven approach to better
Accounting software trends have impacted the accounting profession in big ways. And in my view, one of the greatest analogies of this impact, and even of the way our team at MindBridge delivers value to our clients, comes from Sam Daish, Head of AI and Data Science at Qrious. A story of three types of businesses In
An effective audit starts with a solid audit plan. While the overall audit strategy and plan can vary between clients, an auditor will usually establish risk assessment procedures and a how-to response for the risk of material misstatement. The challenge is that sometimes, even the most thorough and comprehensive audit plans can still have gaps.
The cornerstone of well-planned and high-quality audit engagements is a robust risk assessment process. Such a process is critical to identifying risks of material misstatement and their relative significance by providing a fulsome understanding of the entity subject to audit and the environment in which it operates. The nature and extent of these audit risk