Originally published here in Fintech Finance News

MindBridge Analytics, the global leader in financial risk discovery and anomaly detection, today announced the availability of four new transaction risk discovery products that run on the MindBridge AI™ platform: Payroll, Company Card, Vendor, and Revenue Risk analytics. Each solution delivers deep insights into financial, transactional, and operational risk. Unlike other solutions, MindBridge is the only solution that identifies risk severity across all transactions, allowing auditors and analysts to discover high-risk finance transactions quickly.

“MindBridge has redefined how businesses approach risk across audits, internal controls, and reporting,” said Rachel Kirkham, MindBridge VP of Product. “Only MindBridge scales with the largest financial datasets – easily performing billions of calculations resulting in high-impact risk ratings for each transaction. Audit and finance teams benefit from unprecedented insight into transactional risk.”

MindBridge AI is the first financial risk discovery platform to use artificial intelligence to automate anomaly detection for internal controls over financial reporting (ICFR.) With AiCFR^™, risk is analyzed across 100% of transactions. Companies benefit from more efficient financial operations while ensuring trust, accuracy, and reliability of controls.

Not only do companies conduct a more comprehensive analysis of their transaction sets, but they also do it more efficiently. CFOs understand that AI enables their teams to discover risk in every transaction across a comprehensive portfolio of transaction types – the bar for risk discovery, audit, and compliance has been permanently raised.

Companies benefit from:

• Exceptional time to value delivered with no advanced training required.
• Continuous monitoring through enhanced automated detective controls and tests.
• Unparalleled scalability, from thousands of rows to hundreds of millions.
• Customization of financial controls to mirror your unique organization landscape.