The health insurance sector in Saudi Arabia faces significant challenges related to fraudulent activities
The fraudulent practices not only lead to substantial financial losses for insurance companies but also erode trust within the healthcare system. The absence of effective fraud detection mechanisms allows these activities to persist, threatening the integrity of insurance operations and raising costs for both providers and consumers. To mitigate these challenges, there is a pressing need for advanced AI models capable of identifying and preventing fraudulent patterns in health insurance claims. Implementing accurate fraud detection measures will safeguard the financial health of insurance providers, reduce the burden on honest policyholders, and promote transparency and integrity within the Saudi Arabian healthcare industry.
The project goals
The main project goal is to develop a proof of concept consisting of AI model(s) to detect fraud patterns within the health insurance sector of Saudi Arabia. This project will either utilize real datasets provided by the client or, if not available, will start using open-source datasets.
Data collection and cleaning: This will either involve using the data set provided by the client or utilizing the open-source datasets.
Data analysis and feature engineering tailored to the specific patterns and anomalies expected in Saudi Arabia.
Model development, training, and testing.
Model evaluation and performance metrics collection.
In addition to using available metrics to validate the models (F1 Score, Confusion Matrix, Recall, Precision, etc.), the AI Engineers are expected to design a custom loss function that ensures minimum cost implications of the decisions taken by the model.
**More details will be shared with the selected team.
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