Artificial intelligence has made incredible strides in nearly every industry in the last year alone. AI is used for everything from self-driving cars to virtual assistants like Siri and Alexa, but data extraction is one of the lesser-known AI applications. While it isn’t super exciting, the implications of AI data extraction are incredibly important for the insurance industry. From claims processing to risk assessment, AI data extraction will impact almost every area of the insurance business. Let’s look at what you need to know about AI data extraction in digital claims processing.

What is AI Data Extraction?

Artificial intelligence data extraction is a process through which data is automatically scanned and interpreted. AI data extraction is sometimes called “automatic content processing” or “auto-categorization.” The idea behind the technology is to remove human bias and make data analysis more objective. For example, when analyzing insurance claims data, you might notice that agents in a certain area tend to submit more claims on rainy days. This suggests they could be driving more carefully on dry days and submitting fewer claims, but they are submitting more claims on rainy days because they cannot drive as carefully. With AI data extraction, an algorithm could automatically scan the data from those claims and find that they were filed on rainy days with no significant differences between the agents in that area. This would provide more objective data, which could help improve claims processing.

Why is AI Data Extraction Important?

AI data extraction is important because it can help significantly improve several areas of the insurance business. For example, claims processing is often a lengthy and complicated process. The average claim takes about 60 days to process. In addition to taking a long time, the process can also be efficient and accurate. In the U.S., the average cost of settling a claim is around $500, and the industry loses an estimated $80 billion in potential revenue each year thanks to inaccurate claims processing. AI data extraction in claims processing can help to speed up the process, reduce costs, and make the system more accurate.

Why Is AI Data Extraction in Claims Processing Important?

As we’ve discussed, claims processing is an important part of the insurance business, but it can be complicated and inefficient. AI data extraction in claims processing can help by making the process more accurate and efficient. For example, if an adjuster uses AI data extraction to review a claim, the adjuster might notice that the claimant’s description doesn’t exactly match what was reported. This could be due to human error or the claimant not being careful when filling out the paperwork. With AI data extraction in place, the data from the claim would be scanned and automatically categorized in a way that is both accurate and consistent. This data would then be used to make a decision about the claim without any human bias or error.

How will AI Data Extraction Benefit Claims Processing?

In addition to helping to make the process more accurate, AI data extraction can also help to speed up the claims process. For example, when a claim is submitted, it is often necessary to gather additional information from the claimant to ensure all necessary details are included. This can add to the length of the claims process because the additional information can’t be processed until it is received. AI data extraction can help to speed up the claims process by automatically scanning the initial claim and sending a request for the necessary details to the claimant. This would allow the claims team to process the claim while they wait for the additional information to be provided. This would not only help to speed up the claims process, but it would also help to get claims paid more quickly.

How Will AI Help With Risk Assessment?

Another area in that AI data extraction can make an impact is risk assessment. The risk assessment determines an insurance policy’s premium based on factors like the insured’s age, location, and driving history. Risk assessment is often subjective, and insurers are sometimes accused of unfairly increasing premiums or denying coverage based on personal biases. AI data extraction can be used to make risk assessment more objective. For example, an insurer could use data from its claims to determine the level of risk in certain areas. In addition to helping with risk assessment, this data could also be used to improve claims processing. For example, if an insurer knows that an area is particularly risky, it might want to send agents to that area as soon as possible to help reduce the number of claims. With AI data extraction, the insurer could scan the data from claims in the area to see how serious the claims are. If the claims aren’t too serious, the insurer could wait to send inspectors, but if the claims are serious, the insurer could send inspectors sooner.

Conclusion

Artificial intelligence data extraction promises to speed up the claims process and make risk assessment more objective. To do this, AI data extraction needs to be implemented in digital claims processing. Digital claims processing allows teams to process claims digitally instead of on paper. In addition to making the claims process more efficient, digital claims processing allows insurers to use data to make risk assessments more objective. By scanning data from the claims and extracting the relevant information, insurers can make risk assessment more objective while speeding up the claims process. For additional details, contact us at ask@deepdatum.ai or visit us at www.deepdatum.ai.