About
Enhancing ability to detect and prevent fraud while delivering a superior customer experience in the digital age.
Innovation presentation
Analyzing news articles for clues that could lead to fraud in insurance claims is a valuable project for an insurance company. It helps in identifying potentially fraudulent activities early, which can save the company a significant amount of money. Here are some key lessons learned from this project that can benefit an insurance company.
Fraud methods changes and adapts as anti-fraud solutions improve. Therefore, adding more resources for anti-fraud detection methods and platforms is key benefit to analyze claim cases In different dimensions. We use search engines as intelligent gathering source and LLMs as analyzing tool. Combining these two methods gives the ability to gather information about claims in public data and analyze it without human intervention automatically with LLMs. Outputs of LLMs gives us critical information required for SIU investigation for faster fraud case detection and resolution without someone searching and reading all public information manually. Process is reducing investigation time and human errors. Our KPI in this project defined as Fraud Saving which is crucial for insurance claims.
Uniqueness of the project
Our goal is to use technology all processes we have including decision making and automation. Also, we aim test and adopt new and emerging technologies as fast as we can like Generative AI. In this project we use LLM technologies and testing LLM in many environments and projects at the moment.
News articles may contain noisy or irrelevant information, making it difficult to identify actionable fraud indicators which effects data quality. Employing data cleaning and preprocessing techniques to remove noise and irrelevant content. Implement machine learning models to improve the accuracy of fraud indicator extraction.
