Artificial Intelligence as The New Defender Against Insurance Fraud

Key takeaways:

• AI has powerful capabilities to identify complex patterns that may indicate fraud, such as deep analysis of vast amounts of structured and unstructured data. 
• AI provides significant benefits to insurers, such as higher efficiency, speed, and lower costs, due to its ability to monitor data and detect fraud 24/7 faster than humans.
• While AI shows promise, concerns around its accuracy, potential for criminal abuse, and ethical/privacy risks must be addressed through measures like oversight, governance, and continual model optimization.
• As AI and machine learning advance, more insurers will adopt the technology to improve operations.

Insurance fraud is a massive problem, with estimates of billions lost annually worldwide. Many schemes have emerged, such as staged accidents, false injury claims, identity theft, and application fraud. Unfortunately, traditional fraud detection methods relying on manual reviews cannot keep up with modern criminals’ scale and evolving tactics.

Artificial intelligence (AI) comes at a critical time as “soft fraud” has grown more challenging to identify. Actions like exaggerated claims or forms containing minor inconsistencies are brutal for underwriters to detect without technology to assist them. AI aims to augment—not replace—humans with capabilities like assessing claim risk scores that flag applications meriting a closer look.

Capabilities of Artificial Intelligence

AI has significantly advanced in recent years, becoming a powerful analytical tool.  Let’s take a closer look at what abilities AI has when it comes to fraud detection:

Deep Analysis of Historical Data

AI can analyze vast volumes of structured and unstructured data from various sources, such as claim forms, medical records, photos, voice recordings, etc. Technologies like optical character recognition (OCR), natural language processing (NLP), and voice analysis allow insurance companies to extract meaningful insights from these data types. 

Claims data spans decades, so AI models study standard behavior patterns across millions of legitimate transactions. They learn what details are standard in different file types, the common language used for injuries and treatments, and vocabularies of honest providers. This broad contextual knowledge forms the foundation for fraud detection.

Identifying Anomalies & Vulnerabilities

AI algorithms identify relationships within historical data, creating statistical norms. They constantly monitor incoming claims, flagging any outlying details. For example, if a provider consistently bills higher than peers or makes claims on weekends when their office is closed, that raises some red flags.

Over time, AI also learns the subtle tactics scammers may employ – forging details, altering dates, and upcoding procedures. By recognizing loopholes or anomalies frequently connected with confirmed cases of fraud, AI models strengthen their ability to detect new criminals. Suspect cases can then be sent to experienced analysts for a detailed investigation.

Real-time Monitoring & Notifications

Advanced AI systems don’t just passively analyze old data. Intelligent agents constantly monitor live updates, immediately spotting any new activity matching known suspicious patterns.

With alerts sent to underwriters as issues emerge, suspicious situations can be investigated before a criminal had already cashed in. The result is more assets protected and fraud being detected much faster, allowing insurance companies to better safeguard customers and profits with artificial intelligence.

Benefits of AI Fraud Detection

Now that we know all the powerful capabilities of artificial intelligence in fraud detection, what benefits does it bring to the table?

Higher Efficiency & Speed

Artificial intelligence can closely monitor vast volumes of insurance claims data 24/7. This means potential fraud can be flagged in real time before large payouts are made. Unlike human analysts, AI never needs to take breaks, so the insurance company’s risk is reduced at all hours of the day. 

On top of that, AI can process massive datasets much faster than humans by connecting complex patterns. Even small anomalies that indicate fraud can be identified within minutes rather than days or weeks. This sped-up detection process allows insurers to respond faster to suspicious activity.

Decreased Costs & Losses

While an initial investment is required to implement an AI-based fraud detection system, the long-term costs are significantly lower than employing dozens of additional human analysts. Once developed, AI also has no salary expenses. Faster fraud identification through AI means insurers can avoid payouts on false claims, leading to considerable cost savings. 

Sophisticated Fraud Attempts Made Visible

Advanced AI uses techniques like deep learning to enhance its abilities. It can detect complex patterns and associate risk scores even when dealing with sophisticated fraud types. As AI models are fed more historical claims data, they become more proficient at spotting even the most subtle anomalies.

This advanced detection allows insurers to stay one step ahead of criminals attempting more complex frauds. Over time, AI makes it increasingly more complicated for fraudsters to avoid detection.

Concerns with AI Fraud Detection

AI and insurance companies greatly benefit from each other. However, specific concerns may still emerge and be a big problem if overlooked. 

More Errors With Bigger Data

As AI models analyze a rapidly growing volume of insurance claims data, the level of complexity increases significantly. This expands the risk of models making inaccurate predictions that could falsely flag legitimate claims or miss actual fraudulent activity. 

However, ensuring accuracy at massive scales is a great technical challenge. Data errors or model development, training, or deployment issues could impact results. Additionally, as fraud tactics evolve, older models may struggle to maintain accuracy without more training using new data.

Evolving Fraud Strategies

Just as insurers can leverage open-source AI techniques to benefit their fraud detection, the same tools are freely available to those seeking to commit insurance scams. Fraud rings may attempt to analyze claims patterns and even train their models using public data to conceal criminal operations better and avoid red flags. 

There is also a risk that criminals could launch AI-powered social engineering attacks or generate synthetic claims. To counter these increasing risks, insurers’ AI systems need constant optimization, new defenses, and close monitoring of new fraudulent techniques. The potential for an “arms race” between insurers’ AI and that used by criminals must also be recognized and prepared for.

Ethical Concerns

When AI systems are involved in sensitive processes that impact customers, the potential for bias, unfair treatment, or privacy issues is a serious concern. 

Models may reflect or even increase unwanted biases within the data. Inaccurate or unjustified actions could harm honest policyholders. And the extensive profiling required for predictive analytics raises significant data privacy questions. Insurers must create accountability measures like AI impact assessments, oversight, and transparency in decision-making.

The Future of AI Fraud Detection in Insurance

In 2024, with the massive adoption of AI in almost every industry and AI’s continued technical progress, what does the future hold for artificial intelligence in insurance?

Will the new criminals adapt further?

Criminals are motivated to bypass barriers to continue exploiting vulnerable victims. They may develop deep fakes, synthetic identities, or sophisticated social engineering tricks enabled by new technologies. Insurers must continue to leverage strategic partnerships, intelligence sharing between companies and regulators, and advanced training strategies to test model strength against emerging threats. 

More Insurers to Adopt AI

Major insurers achieving 15-25% fraud loss cuts through artificial intelligence will set new standards. Customers will demand that insurance companies leverage data responsibly and offer transparent, personalized service. 

By 2030, AI and machine learning models trained across healthcare, auto, and property/casualty data using federated learning could detect 90% of fraud for a fraction of current costs.

Conclusion

AI in insurance has come a long way to where it is now—a powerful and intelligent companion that can significantly help insurers and bring many valuable benefits. 

While AI still faces multiple challenges to maximize its accuracy, privacy, and ability to outsmart evolving criminals, the potential of artificial intelligence appears limitless with proper oversight and governance. 

However, as with any tool, AI must be skillfully used to transform insurance operations. Did you know that CoverGo is using AI to power its latest distribution platform? 

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