Accenture processes approximately 26 million expense lines annually. This number will grow as Accenture continues to grow. Every report gets analyzed by a manually designed rules-based system to check for expense compliance. Roughly 10 percent of expenses get flagged for potential noncompliance, which is then audited by our internal Compliance Team.
Traditional rules-based systems—while effective at detecting known and recurring patterns of noncompliance—often return exceptionally high numbers of false positive alerts and mistakenly flag legitimate behaviors as suspicious. Traditional rules-based systems can also be exploited by fraudulent behavior and fail to consider previously identified noncompliant behaviors.
CIO Applied Intelligence in collaboration with our Time and Expense organization took this situation as an opportunity to improve upon Accenture’s current rules-based system and created an AI solution that more accurately identifies noncompliant expenses and reduces false positives. The AI solution is also able to more easily identify noncompliant behaviors with hidden patterns that are difficult for human auditors to identify.
Our CIO Applied Intelligence team developed an AI algorithm that ingests historical expense, time charge, and location data. This development led to an intelligent solution that observes the data itself, rather than just the flags identified by the rules, to detect outliers in the data. This approach enables the AI to detect hidden patterns in the data and co-evolve with the behavior of the employees, rather than the rules, therefore, detecting noncompliance that is going unflagged.
