Detection of fraud patterns and root causes in auto insurance claims is not easy. Fraud exists, but it is time-consuming and difficult to identify what is fraudulent. Using batch machine learning, new fraud cases are impossible to catch. Many different variables need to be used together for effective fraud detection.
Using TAZI patented technologies, spend less time on clean claims, detect possible fraudulent claims more effectively.
Continuous Learning for taking the right preventive actions at the right time with a robust continuously learning AutoML platform.
You can allow your knowledge of the industry to be integrated into machine learning through our Learning from Human functionality, which allows to adopt dynamic changes with domain expert’s feedback in addition to continuous learning capability. Experts can explore and correct the fraud model. Claims managers spend less time on clean claims and can concentrate more on the suspects.
Understandability for interactive and actionable explanations to expert teams. Auto Insurance professionals have access to visual sunbursts that allow them to understand which claim and why is fraudulent and take specific actions that will be logged in the system.
Auto-Insurance professionals can now use machine learning safely and easily. You can build your models in minutes or integrate your existing Python models to further boost performance and your team’s efficiency. Also, customizable ML Dashboards allows each user to keep track of their key indicators.
All this built on self-maintaining algorithms, allowing accuracy to be maintained over time with no need for costly maintenance.