Machine learning can detect fraudulent insurance claims

Insurance companies can significantly improve productivity by using machine learning in fraud detection.

Machine learning has the potential to save significant costs associated with hiring and training staff to investigate insurance claims.

Insurance fraud has always been a major problem for insurance companies. Uncovering insurance fraud can be a complex process, with many fraudulent claims going unnoticed, severely impacting the industry. In the US alone, insurance organizations lose over $40 billion each year to insurance fraud. Identifying insurance fraud can be quite difficult as each claim must be thoroughly investigated. Because AI can help detect fraud in corporate accounting, insurance companies can use this technology to combat fraudulent insurance claims. By using machine learning in fraud detection, insurance companies can quickly and accurately analyze thousands of claims, reducing errors caused by manual verification.

Limitations of traditional insurance claims analysis


Insurance agents must thoroughly examine each claim to determine if it is genuine. This process is incredibly complicated and time-consuming. In addition, companies need to hire and train a team of agents who can accurately review thousands of claims. To make work easier, many companies had switched to computerized systems. However, such systems can only perform basic analysis and offer low accuracy. Even after a potentially fraudulent claim has been identified, an insurance agent must continue to investigate the issue. As a result, the traditional approach of manually detecting and investigating fraudulent insurance claims is time-consuming and expensive.

Implementing machine learning in fraud detection


AI-powered systems use machine learning models that can accurately identify fraudulent insurance claims. Large labeled datasets can be used to develop AI-based systems that learn and improve over time. Such systems can implement extensive anomaly detection to analyze real claims and build a model of what a generic claim might look like. This is applied to large datasets to identify fraudulent insurance claims. AI-based systems can also use predictive analytics, which not only looks for true or fraudulent claims, but also investigates further to detect fraudulent behavior.

Insurance executives have already implemented AI-based fraud detection. One of Turkey’s largest insurance organizations, Anadolu Sigorta, uses Friss predictive analytics software.

With this software, the organization could dynamically detect fraudulent claims. In addition, a 210% ROI was achieved in just one 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 whole process took almost two weeks to review each claim. With over 25,000 claims to process each month, the company realized that manual reviews were too time-consuming and decided to switch to AI-powered software. As a result, Anadolu Sigorta managed to save $5.7 million in fraud detection and prevention costs.

Insurers can use machine learning for fraud detection to accurately analyze thousands of claims in a short amount of time. This approach also proves to be cost-effective when compared to manually reviewing insurance claims. After looking at these benefits, insurance companies need to consider investing in AI to automate the claims assessment process. This allows insurance agents to focus on other stimulating tasks in the workplace. Additionally, the AI-powered system has minimal errors, making the whole verification process incredibly efficient.

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