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Insurance claims adjusters face a lot of challenges as they execute their responsibilities for verifying and accurately documenting the claims. The experienced ones are often over-burdened with the caseload, making the settlement cycle longer. Such delays could lead to dissatisfaction among customers. On the other hand, some claims adjusters have a lighter load and would have been able to clear more claims cases had they been assigned to them.

Claims adjusters have to check the authenticity of each claim to ensure that there is no fraud. After all, fraud is one of the biggest drains on the revenue of an insurance company. Often, the authentication process requires a long and detailed investigation, which further delays the claims settlement process.

The key to reducing cycle times is to identify the claims cases that can be settled with historical data and establish patterns that can determine STP (Straight-Through Processing) cases. This way, only the claims that need authentication to get passed on to the claims adjusters and their expertise is utilized more effectively. AI-driven claims settlement solutions offer many advantages and can help claims adjusters be more efficient.

Better classification of claims: Identifying and assigning claims is where an AI-driven claims settlement solution can help tremendously. An AI solution designed with advanced machine learning capabilities can get trained on insurance claims data and help speed up the claims settlement process. The algorithms can help sort complex cases and assign them to adjusters who have matching expertise. By analyzing the patterns from the historical data, an AI-powered claims solution allows for faster authentication, leading to the payout of genuine claims without delay.

Better fraud detention due to central access to all relevant incident data: Claims adjusters are always looking to establish the validity of the claim quickly. With all the incident data available centrally, claims adjusters are able to make the right calls quickly and with increased accuracy. Deep learning capabilities of the solution bring together all the pertinent incident data to one place. This action allows claims adjusters to choose the claims that need investigation and those that need to be deferred to the SIU (Special Investigative Unit). Better fraud detection also directly results in an improved loss ratio.

Better customer experience due to faster settlements and accurate estimates: With faster and more accurate claims processing, cycle times become shorter. Claims adjusters can use predictive insights for faster closure of genuine claims. This ability leads to a reduction in expenses, which, in turn, delivers better NPS (Net Promoter Score) for the insurers. Better customer experience also leads to better customer retention.

With AI claims solution, claim adjusters can also help identify patterns from data to catch repeat offenders and fraud networks.

Settling insurance claims is a complex process, and claims adjusters are a vital part of it. They are responsible for authenticating the claims and make sense of all the incident data to arrive at an estimate. They are overworked and under a lot of pressure to settle claims quickly. At the same time, they have to ensure that no fraudulent claims are paid out. AI-enabled claims settlement solutions can make their job easier and help them streamline their claims caseloads.

BI (bodily injury) claims leakage is a major drain on an insurance company’s resources, mainly because it is difficult to identify all the symptoms of the injury caused only due to the collision or occurrence. Since not all symptoms can be identified during the initial assessment, triaging these claims consumes more time when compared to other types of claims. Even experienced adjusters cannot accurately predict and reserve the payouts needed to handle BI claims.

WHY THE BI CLAIMS ARE SO COMPLEX Too many factors to compute for payout: Most of the BI claims are high payout claims due to the severity of the injury to the claimants. The volatile nature of BI claims settlement makes it very difficult to reserve for BI exposures, due to the range of factors that need to be assessed for reaching a payout figure such as age, gender of the insured, type of the collision, position of the insured during the collision, any known or unknown pre-existing health conditions, and several other aspects.

Multiple parties involved: To make matters more complex, most of the BI claims involve multiple parties – insured and third-parties, leading to an increase in time required for settlement. These delays in settlement cause customer dissatisfaction and failure to quickly and correctly identify the nature of claim triggers an increase in exposure to litigation.

More time required to process claims: BI claims require more time to process as these type of exposures require not only an initial assessment, but also several sessions of treatments and continuous monitoring by medical professionals. A specialist insurance adjuster is needed to confirm that the required injury is handled and the insured or claimant is rehabilitated.

HOW TO TACKLE THE PROBLEM OF BI LEAKAGE BY LEVERAGING AI 1. Get the data

Apply AI models to catalogue incident details: Artificial Intelligence can be applied using various models like Random Forest, Bayesian Networks, or a combination of multiple models, to scan through claim notes and analyze images or videos of the accident to catalog details of damages and the injuries as the claim progresses, and provide intelligent predictions.

Leverage historical data: Compare the current claim’s FNOL and initial assessment notes to historical records and draw up a list of potential early predictions of all the symptoms and hidden or derivative injuries that could crop up at a later stage.

2. Streamline the process

Assign the right adjuster: By having the claim assigned to the right adjusters’ group during the initial stages of the claim and as the claim progresses, insurers can look forward to timely triaging of the claim.

Manage vendors efficiently: With the ability to identify the symptoms during the initial stages of the claim, insurers can now make early referrals to the right medical vendors and rehab facilities to take care of the injuries faster.

3. Predict payouts accurately

Predict payments: AI models can provide consistent reserves and payment estimates for the respective BI exposures with similar collision conditions, location, age, gender, position and number of claimants involved, the severity of the collision.

Make an early settlement offers: With early insights, claims adjusters would be able to negotiate with the claimants by providing early lump-sum payment options, which is a desirable outcome for the claimants, third parties, and also the insurance organization. Claimants and third parties get their much-needed payout early to settle expenses, and the insurance company can minimize its exposure to litigation and reduce costs by closing the claim fast.

HOW AI HANDLES THE COMPLEXITY OF BI CLAIMS AND REDUCES THE IMPACT ON INSURERS Disadvantages the insurers face due to BI claims complexity:

Susceptibility to soft fraud: Chances of soft fraud by a claimant or third parties increase for the claims involving bodily injuries since it is extremely difficult to prove whether the symptoms of the claimant are due to a pre-existing health condition or due to the injury. As BI payouts are generally higher than other types of claims, not being able to identify potential frauds adds to the insurer’s losses. Potential exposure to litigation: The insurers are more vulnerable to lawsuits involving BI, as early detection of the symptoms and impact is often missed. In several BI claims, some of the key symptoms might only crop up at later stages due to the inability of the claims processing team to identify all the underlying symptoms of the damage during the initial assessment. In such cases, the insurance company becomes exposed to litigation on negligent grounds. Negative impact on the bottom line: Insurance companies end up paying more for settling BI claims due to process inefficiencies and failing to detect underlying symptoms at an early stage, which negatively affects their bottom line. Benefits due to the application of AI:

Reduced processing time: The AI solution is capable of going through thousands of claim notes and provides key insights from prior claims quickly and accurately, which is otherwise very time consuming even for experienced adjusters, and absolute accuracy is never guaranteed due to the human factor. Increased customer satisfaction: Taking care of the claimant’s monetary needs faster increases the claimant’s satisfaction, which helps with the NPS. Further, since the overall claim processing time and claim payments are reduced, this provides the insurance organization with the opportunity to reduce the premium amounts, which also increases the insured’s delight and works toward earning their loyalty. Reduced or optimized payments: The ability to predict and handle injuries and hidden symptoms at an early stage before they get aggravated, helps with faster healing and recuperation of the claimant, which can significantly avoid additional payments for the treatment of hidden symptoms. Settling claims is a resource-intensive process that requires great attention and care, and cost insurers a significant part of their revenue. The more complex a claim, the more resources and time it requires for settlement. With better categorization of injury data, it is also possible to authenticate and indicate red flags more accurately.

By leveraging AI technologies, it is possible to reduce significant resource drain for most insurance companies that occurs due to BI claims leakage. A neural network trained on robust data offers better pattern recognition and delivers more actionable insights.