Anomaly Detection in Construction Finances using AI
To truly steer the business strategically, CFOs need to process data from across the enterprise, distilling voluminous information into actionable insights. AI is the key to equipping the CFO office to do more with data. Practical example is a typical procure-to-pay business process, a weak link can cost the business, impacts are significant and often underestimated. Recurring leakages in procure to pay processes are recovered through post payment audits rather than being prevented proactively at the outset. Most enterprises rely on tips and rule-based systems to identify anomalous transactions, which allow fraudsters to bypass controls and result in high false positives. Procurement and payment areas rank the highest amongst processes most vulnerable to fraud, waste and errors with typical leakages of 1% of total supplier spend.
Anomaly Detection is a trained AI model that detect leakages, decipher patterns in data to identify new anomalies and reduce false positives. AD reports anomalies, errors, duplicates by ingesting and inferring invoice information, learning past pattern of invoices.