The Solution: Automated Data Segmentation Process
Datamatics developed a sophisticated rules engine designed to detect fraudulent claims in real time using advanced algorithmic interventions. What set this solution apart was its ability to continuously improve through machine learning, becoming more effective with each new exposure to data.
The Datamatics team began by thoroughly analyzing the raw dataset, focusing on both claim values and invoice lines. They carefully segregated the data based on attributes such as Age, Claim Invoice Gross GBP, and Claim Invoice Net GBP, alongside other key factors like Gender, Country Code, Diagnostic Code, Claim Status, and Policy Type, to form a comprehensive dataset for analysis.
To tackle the problem of fraud detection, Datamatics employed an ensemble of machine learning techniques, combined with anomaly detection, to analyze a vast dataset of 3.3 million claims. This approach allowed the system to identify potentially fraudulent claims within specific clusters, enhancing its ability to flag suspicious activity in real time. The solution incorporated complex technological interventions while ensuring a user-friendly interface, allowing for efficient detection of fraudulent behavior.
A significant challenge was working with granular line-item level data, which included several attributes measured across various scales—nominal, ordinal, interval, and ratio. Datamatics addressed the absence of predefined fraud markers with creative solutions, ensuring that even the most subtle fraudulent patterns could be detected.
