Rooted in the mission to combat fraudulent activities within healthcare insurance claims, our system stands as a beacon of innovation, leveraging cutting-edge Artificial Intelligence (AI) and advanced Machine Learning (ML) techniques. Designed to be a robust and user-intuitive platform, our system aims to equip insurance companies with the necessary tools to mitigate financial losses stemming from fraudulent claims effectively. Through seamless cooperation with Mitra Innovations, our vision is to craft a unified solution that simplifies processes, enhances data accuracy, and meets the diverse operational needs of insurance providers.
Fraud detection within the health insurance domain presents a multifaceted challenge, requiring a combination of data analysis, technological advancements, and human expertise to mitigate financial losses resulting from fraudulent claims. This problem is inherently complex due to the diverse range of fraud patterns and the relatively small ratio of known fraudulent claims. However, leveraging Machine Learning (ML) and other techniques offers a promising solution by enhancing predictive accuracy, reducing costs, and achieving broader coverage with minimal false positive rates.
The proposed solution for the problem mentioned is an AI-assisted medical fraud detection system, which is a comprehensive and adaptive system designed to effectively detect and prevent fraudulent activities within the medical insurance domain. This system utilizes a combination of machine learning (ML) models and a rules engine, allowing for dynamic rule adjustments to meet the specific needs of our customers. Our system accesses three key databases: claims, customer, and fraud databases. The claims database serves as the primary data source for both the rules engine and ML model. Two user roles, fraud investigators and admins, interact with the system through a user-friendly web application dashboard, where they can input claim IDs for fraud detection. The ML model processes the relevant claim data and provides detailed fraud detection outputs, which are stored in the fraud database for future reference. Additionally, two REST APIs are employed to enable seamless communication between the web application,and Creatio software(Future work). Ultimately, our solution aims to provide accurate fraud detection and efficient decision-making support, enhancing the overall integrity and reliability of the medical insurance claims process.
Data quality and availability: one of the significant challenges encountered was the variability in data quality and availability. Inconsistent data entries and missing values required extensive preprocessing and data cleaning efforts to ensure accurate model training and predictions. Amount of research around medical insurance is also minimal which in return was difficult to collect a relevant dataset. Model interpretability: Ensuring that the machine learning model’s predictions were interpretable for end-users posed a challenge. Efforts were made to provide detailed explanations of model outputs, but there remains a need for further improvements in model transparency. Integration issues: Integrating with the frontend and the external API for the Creatio software with the backend presented challenges related to API compatibility and data format discrepancies. These issues were resolved through iterative testing and refinement of the integration processes. User training: Ensuring that users were adequately trained to utilise the system effectively will be another challenge. Comprehensive training sessions and user manuals will be provided, but ongoing support and training will be necessary to maximise user adoption and effectiveness.
Limited generalizability: The machine learning model and rules engine were trained and tuned based on specific datasets and fraud patterns. While the system performs well on these datasets, its generalizability to entirely new and unseen fraud patterns may be limited. Dependency on data quality: The system’s performance is highly dependent on the quality and completeness of the input data. Poor data quality can adversely affect model accuracy and the effectiveness of the rules engine. Resource intensive: The system’s computational requirements, particularly for training the machine learning model and processing large datasets, can be resource-intensive. This limitation may affect scalability and real-time processing capabilities in high volume environments.
Enhancing ML models: Continuous improvement of the ML models through the incorporation of more diverse and comprehensive datasets can further increase predictive accuracy and reduce false positives. SNA capabilities: the social network analysis to include additional social media platforms and more sophisticated relationship metrics can provide deeper insights into fraudulent networks. Integrating additional data sources: Incorporating additional data sources, such as financial transactions and medical records, can provide a more holistic view of each claim and improves fraud detection capabilities. Automating the rules engine: Developing an automated system for updating and managing the rules engine based on emerging fraud patterns can ensure that the system remains effective against new types of fraud. User training and support: Providing comprehensive training and support for users can enhance their ability to utilise the system effectively and maximise its benefits. Expansion to other domains: The current system is tailored to medical fraud detection, but the underlying framework can be adapted to other domains such as auto insurance, property insurance, and credit card fraud. Future work can explore these applications to broaden the system’s impact.
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