MindBridge Announces Stephen DeWitt as New CEO
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2024 Analytics, Automation and AI Virtual Conference

Discover AI's transformative role in audits at the IIA Analytics & AI Virtual Conf with MindBridge. Learn from experts to enhance your audit operations with our advanced solutions.

MindBridge AI is the leading risk intelligence platform for internal audit and finance professionals, delivering meaningful risk prioritization and insights for 100% of your business.

Our platform empowers audit and finance professionals to perform greater transaction risk awareness, accuracy, and efficiency. Like no other solution, MindBridge AI allows businesses to be audit-ready by analyzing 100% of transactions and delivers AI for internal controls over financial reporting (AiCFR™) for in-depth analysis.

MindBridge Analytics is the global leader in AI financial risk discovery and anomaly detection.

Rachel Kirkham, MindBridge, VP of AI & Product​

Rachel Kirkham, VP of AI and Product, MindBridge

Join AI industry expert, and chartered accountant Rachel Kirkham as she explains how AI systems provide visibility into risk, anomalies, and potential fraud, and an agile approach to the three lines of defense. Learn more here and add Rachel’s session to your event calendar.

CS 1: Innovate Internal Auditing: Unleashing AI for Enhanced Risk Detection  
(April 18 2024. 11:40 am – 12:40 pm EST)

In the rapidly evolving landscape of internal auditing, artificial intelligence (AI) has emerged as a transformative force, enabling a more agile and focused approach to risk management. Join us in this innovative session as we explore how AI systems, with unprecedented capabilities, empower professionals to articulate insights on risks, anomalies, and potential fraud across financial data sets. The desire for of a common ADA (Audit Data Analytics) and continuous monitoring platform has become a reality, offering a smarter-than-ever solution that reconciles, links, matches, and assesses risks across systems and processes. 

•Understanding Algorithms for risk detection  
•Implementing AI risk detection for efficiency  
•Generating insights to improve control weaknesses  
•Case studies and business examples