Automation bias has become a challenge for industries around the world. With the emergence of AI and other machine learning processes, it’s easy to become too reliant on automation. It boils down to the fundamentals of AI and how these systems learn and make decisions. The problem is that algorithms operate under the assumption that data won’t change. Since the world constantly evolves, training data grows stale and becomes less reliant. Automation that relies on older data can’t keep up with an ever-evolving world.
Among affected industries, insurance providers are susceptible to automation bias. Insurance decisions are heavily reliant on making decisions based on unique criteria. This is challenging insurance providers to find innovative ways to merge AI with human interaction to create more robust and unbiased systems.
What is Automation Bias?
Automated processes generate risk assessments and respond to claims. However, providers can’t rely solely on these decisions since machines don’t account for every factor. AI understands the data but doesn’t understand its context. As a result, it overlooks essential details that would be picked up on by a human underwriter.
Additionally, automated systems are unable to handle complex scenarios. With human intervention, automation bias will lead to correct denial and approval of claims and placement into the wrong policies.
We can look at predictive modeling in healthcare as a great example of the type of automation bias experienced in the insurance industry. These models allocate resources using vendor risk scores and “if-then” scenarios. If a disease doesn’t impact a large segment, artificial intelligence models won’t have enough information to make the proper decisions. So, it misdiagnoses these patients with a more common ailment with similar symptoms.
In the insurance industry, these same issues come up with risk assessments. For instance, if data for a specific demographic is insufficient, the algorithm could wrongly deny coverage or place the individual into the wrong policy.
The Role of Artificial Intelligence (AI) in Insurance
Insurance companies use data modeling, predictive analysis, and automated insurance underwriting to make their process more efficient. However, human intervention is still an essential step in the process.
As we saw in the previous section, the predictive analysis should be addressed only partially to AI. Think of automation as a tool. It’s great at filtering candidates and providing detailed analysis, but only humans can give enough context to make the right choice when making decisions.
We’re also seeing artificial intelligence excel at flagging potential fraud. Since machine learning algorithms can process and compare information quickly, they can find continuity gaps.
Factors Contributing to Automation Bias in Insurance Providers
Automation bias shows its ugly face when companies become too dependent on technology. We see this in several industries, which has become a growing problem. Wrong decisions can lead to lawsuits and other disastrous outcomes in the insurance industry.
Lack of human oversight is another factor that contributes to automation bias. Insurance providers must have steps in place for information to be verified by real people. AI should be used to gather data and compile it into user-friendly reports. Then the system hands those reports to a real person for the final decision.
Another factor is a need for proper employee training on automated systems. Employees who have worked with older systems are resistant to more modern data entry methods. This resistance is due to employees needing to receive the proper training.
Effects of Automation Bias on Insurance Providers
Insurance providers are among the top industries that feel the full effects of automation bias. There are too many variants of data for AI to make the right decisions every time. Automation bias creeps up when these platforms misinterpret data and file incorrect claims. There are too many unpredictable variables in life for a logic-based system to understand at this stage.
Misinterpreted data leads to incorrect claims processing. This is generally because of an uncommon variable that makes one case slightly different from another. Without human intervention, automated algorithms cannot understand how a unique variable affects the claim.
If consumers can’t count on their insurance provider to make the right decision in their moment of need, they will seek services elsewhere.
Preventing and Managing Automation Bias
Identifying the various causes of automation bias is the first step to preventing it. This is more difficult than it sounds. Even if every flaw in training data is corrected, AI is still unable to make accurate decisions because it lacks context. That’s where the human factor is vital.
Businesses are finding that to take an entirely hands-off approach; they need to outsource the human side of their automation verification practices. Insurance validation outsourcing services serve as gatekeepers between automated processes and final decisions. Thus, the risk of automation bias is reduced significantly.
When utilized correctly, artificial intelligence can provide tremendous benefits to your business. Several big brands have already begun implementation to streamline their insurance processes.
Liberty Mutual utilizes a powerful Auto Damage Calculator to assist in claims. This AI tool uses data inputs like comparative analysis and claims photos to assess vehicle damage. It can provide an estimate of the total repair costs. A claim expert then validates this information.
Clearcover boasts an AI system that processes claims quickly. Users submit a questionnaire and pictures of the damage to start the claims process. This removes a timely step and jumpstarts the filing process. A claims expert then validates the findings and finishes submitting the claim.
In both examples, human intervention validates the automated results to ensure that automation bias isn’t a factor.
Final Thoughts
Automation bias is one of the biggest challenges facing the use of artificial intelligence in the insurance industry. The bottom line is that AI is great at gathering and processing data, but human intervention is required to add context to this information.
Insurance providers must know these limitations and ensure that humans validate important decisions. Companies can combat automation bias by partnering with an outsourcing provider like Insurance Back Office Pro to oversee and validate automation outcomes.
