PredictX Inspector

Automatically identify suspicious and abnormal transactions in your Travel & Expense programme

Expense reimbursement fraud accounts for 17% of all business fraud.

Currently, most expense reporting systems rely on catching undesirable expenses through a defined policy. Simple In/Out of Policy definitions of undesirable spend, however, are difficult to reliably use as definitions change over time and often according to each claim’s unique position and needs. This makes it challenging to manually flag anomalies.

Knowing the promise technology like AI holds, PredictX used machine learning models to enhance fraud detection in expense reporting for users of expense management and accounts payable automation provider, Emburse’s solutions.

Emburse brings together some of the world’s most powerful and trusted expense and AP automation brands, including Abacus, Captio, Certify, Chrome River, Nexonia and Tallie.

Introducing Inspector

Inspector is a suspicious transaction detection data analysis, enabling customers of Emburse’s expense management solutions to identify “abnormal” transactions reflecting undesirable, risky or potentially fraudulent activity in the expense data feed.

Unique among auditing tools, Inspector takes a holistic look at the data and understands the linkages between employees, activities, and expenses - especially important for meetings and events and when assistants book on behalf of others.

How can Inspector detect “undesirable” or “high risk” transactions?

Supervised machine learning

where a predictive model learns the characteristics of previously flagged transactions to predict future anomalous transactions that are not yet manually assessed.

Unsupervised machine learning

where a holistic and open-ended analysis of all expense feed data points identifies suspicious transactions, even if they have not been detected in the past.

For a more proactive approach to managing expense fraud:

Book your demo.

We will be happy to assist with any questions and demonstrate exactly how it works.

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