• A healthcare provider expressed a need for a tool to assist their claims analysts in identifying potential fraudulent claims

• He required a scalable solution that could handle increasing volumes of claims data and adapt to evolving fraud patterns

• The Client was struggling to detect and prevent fraudulent medical insurance claims, leading to significant financial losses

• Develop a tool to accurately predict the probability of fraudulent claims, aiding in their detection and prevention

• Create a practical solution to identify potential fraudulent claims and streamline the claims review process

• Develop a machine learning-based solution capable of handling large datasets and providing accurate predictions

• Create a user-friendly solution that could be easily integrated into the existing workflow and provide valuable insights

• User Interviews: Conducted interviews with claims analysts to understand their specific needs and challenges

• Data Analysis: Analyzed historical claims data to identify patterns and indicators of fraudulent activity

• Data Preprocessing: Cleaned and prepared the historical claims data for analysis, handling missing values and outliers

• Model Selection: Evaluated different machine learning algorithms, selecting random forest for its ability to handle non-linear relationships and its interpretability

• Model Development: Built a random forest model using machine learning techniques to predict the likelihood of fraud based on various features

• Hyperparameter Tuning: Optimized the random forest model's hyperparameters using techniques like grid search or random search to achieve the best performance

• User Interface Design: Developed a user-friendly interface that allowed claims analysts to input claim data and receive immediate predictions

• Training and Support: Provided comprehensive training and support to ensure effective adoption of the tool

• In progress:

• Data Pipeline - Implement a data pipeline to automate data ingestion, cleaning, and preparation

• Continuous Learning - Incorporated mechanisms for continuous learning, allowing the model to adapt to changing fraud patterns over time