Enable a manufacturing company analyze claims and expenses to detect fraud in the claim process
Claim & Expense Analytics and detecting fraud for a large Indian Manufacturing company
We collected the historical claims data filed by employees and generated several attributes to search for patterns in the data. Specifically, we generated attributes to search for patterns based on the total bill amount per day, bill amount per kilometer, bill amount per work experience group, and bill amount per age group. Using these attributes, we identified suspicious claims based on the bill amount per day, taking into account the tier group, mode of transport, age, experience, and travel purpose.
Moreover, we also built an AI-ruled-based modeling and an isolation forest to detect and analyze anomalies in the dataset. Furthermore, these methods enabled us to identify and flag outliers in the data that may indicate fraudulent behavior.
The AI-ruled-based modeling allowed us to specify rules based on specific features and values in the data and then classify claims as either suspicious or non-suspicious based on these rules.
Meanwhile, the isolation forest algorithm uses a tree-based approach to isolate anomalies in the data, making it a powerful tool for detecting fraud in large and complex datasets.
By combining these methods with the behavioral analytics model, we were able to develop a comprehensive solution for detecting and mitigating fraudulent claims in our client’s dataset. Also, the combination of these techniques allowed us to achieve a high degree of accuracy in identifying suspicious claims and minimizing false positives.
- Python
- RapidMiner
- Oracle Database
The AI-based analytics model was able to successfully analyze given data and identify suspicious claims. Therefore, the client was able to use the resulting observation to detect fraud and plan to combat it.
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