Fraud in the insurance industry is a serious problem, costing insurers an astounding $308.6 billion every year in the United States alone. With inflation, climate change, and the rise of digital fraud, insurance companies are facing a multitude of challenges. That’s why many of them are turning to artificial intelligence (AI) to combat fraud in this ever-evolving landscape.
Currently, nearly 60% of insurance companies are already using AI, particularly machine learning, to detect fraudulent claims. However, fraudsters are getting more sophisticated, using AI themselves to create manipulated images known as “deepfakes.” These deepfakes pose a significant threat, as they can be used to forge hundreds of images, making it difficult for traditional detection methods to catch them.
To combat this new wave of digital fraud, insurance companies are exploring the use of software development kits like Microsoft’s Truepic and OpenOrigins’ Secure Source. These tools record camera data to verify the authenticity of an image, providing a layer of protection against deepfakes. While these technologies are not foolproof, they are becoming essential tools in the fraud investigator’s arsenal.
The current AI tech in insurance primarily focuses on delivering fraud alerts to human investigators. These alerts flag suspicious activities that require further investigation. However, the industry is still relatively new to using true AI in fraud detection. But with the projected growth of the Insurance Fraud Detection Market from $5 billion in 2023 to $17 billion in 2028, it’s clear that AI will play a pivotal role in the future of fraud prevention.
Developers have been tailoring machine learning systems specifically for insurance companies, allowing the AI to learn and discover fraudulent patterns on its own. For example, Shift Technology has developed a fraud-detection software that can identify and highlight networks of fraudulent claims. This software has proven to be more effective than manual or rules-based tools in identifying fraudulent activities.
Moreover, developers are also exploring the application of AI beyond machine learning systems. They are piloting generative-AI systems that can aid investigators in tasks like analyzing lengthy documents. By automating these tedious tasks, investigators can focus on more complex cases, improving overall efficiency.
However, there are challenges in implementing AI in insurance due to data availability and regulatory concerns. Insurers face difficulties in building their own internal fraud models due to the need for a substantial amount of data to train AI effectively. This has led many insurance companies to rely on third-party software providers that have access to vast datasets across multiple markets.
Transparency and data quality are also significant concerns in the use of AI. Insurers want to ensure that AI systems can explain their decision-making process, and data compliance and documentation are crucial in meeting regulatory requirements. Additionally, with third-party providers becoming increasingly important in the industry, regulators must navigate the complex landscape to ensure fair practices and appropriate oversight.
Ultimately, AI is only as good as the information it has been exposed to during its training. Insurers must continually invest in the improvement and enhancement of their detection tools to keep up with emerging forms of fraud. It is an ongoing battle, but with the advancements in AI, insurers have a powerful weapon in their fight against fraud in the insurance industry.