3 Top Ways AI Can Minimize BI Claims Leakage

By August 14, 2019 February 17th, 2020 Blog
BI Claims leakage

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.


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.


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.


Disadvantages the insurers face due to BI claims complexity:

  1. 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.
  2. 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.
  3. 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:

  1. 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.
  2. 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.
  3. 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.

Sheetal Kumar

Sheetal Kumar

Sheetal Kumar is a Technical Architect at InsurAnalytics. Read More Posts

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