Implementation of Machine Learning in Fraud Detection
AI-powered systems use machine learning models that can precisely identify fraudulent insurance claims. Large labeled data sets can be used to develop AI-based systems, which will learn and improve over time. Such systems can implement deep anomaly detection to analyze genuine claims and form a model of what a generic claim may look like. This will be applied to large data sets to identify fraudulent insurance claims. AI-based systems can also use predictive analytics that will not only look for genuine or fraudulent claims but also investigate further to detect fraudulent behavior.
Business leaders in the insurance industry have already implemented AI-based fraud detection. One of the largest Turkish insurance organizations, Anadolu Sigorta, uses predictive analytics software from Friss.
With this software, the organization could detect fraudulent claims dynamically. It also witnessed 210% ROI in just a year of using the software. Before purchasing the software, Anadolu Sigorta had employed a team of 50 people to manually review all claims and the entire process required nearly two weeks to check each claim. With over 25,000 claims to be processed every month, the company realized that manual reviews are too time-consuming and decided to switch to AI-powered software. As a result, Anadolu Sigorta has managed to save $5.7 million in fraud detection and prevention costs.
Insurers can deploy machine learning in fraud detection to accurately analyze thousands of claims in a short span of time. This approach also proves to be cost-effective compared to manual review of insurance claims. After looking at these benefits, insurance businesses need to consider investing in AI to automate the process of reviewing claims. This will allow insurance agents to focus on other stimulating tasks in the workplace. Along with these, the AI-powered system will have minimal errors, making the whole review process incredibly efficient.
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