In most organizations, fraud is often detected after it has occurred. Ideally, measures should be taken before it has even occurred or at least before significant damage is done. Our fraud detection platform allows organizations to detect fraud before it even happens. The platform detects fraud in all kinds of sectors, including but not limited to, banking, insurance, government, and healthcare.
Four Stages of Improvements in Fraud Detection and Prioritization
Detect different types of fraud using machine learning.
Prevent fraudulent cases before they happen using fraud prevention.
Allow analysts to flag fraudulent cases in any type of scenario.
Sample use cases
An insurance firm wants to predict fraudulent claims using claim analysis.
An investor wants to determine likelihood of authentic vs inflated valuations by analyzing the security price.
A government entity wants to determine identity theft using financial records and behavioral analysis.
Mozn case study
A government entity wanted to enhance operational efficiency and test the impact of new configurations on the overall business.
We built several data products including a fraud detection system designed using unsupervised learning techniques to detect outliers.
The fraud detection process became automated, which led to a higher magnitude of the number of outliers by each scan (thousands of anomalies that cover historical and fresh data).