How can insurers increase their business efficiency with the right insurance claims analytics solution?

What is insurance claims analytics?

Insurance claims analytics uses health insurance claims data and insights to refine insurers’ processes. Analytics is not restricted to the claims process alone; it is an overarching holistic methodology. The core aim of the process is to save insurers’ costs, improve efficiencies, increase customer satisfaction, and increase business.

Insurance claims analytics is part of a broader spectrum of methodologies that come under insurance analytics. Fundamentally, any analytics in an insurance process helps the insurer with many data and decision points. Insurers can process claims faster and hence reduce processing costs, and they can identify externalities that could affect claim outcomes and potential frauds.

What are the ways insurance claims analytics improves the claims processing of insurers?

Below are the ways insurance claims analytics improves insurers’ claims processing.

Fraud detection

Examples of insurance frauds are:

  • Claiming cover for situations that are not covered under insurance, such as drunk driving
  • Misrepresenting the incident, transferring the blame to another person, or failure to use prescribed precautions
  • Inflating the incident’s impact to increase the claim estimate

Insurance claims analytics uses data, machine learning models, and several other proprietary processes to determine the level of fraud in the claim. Data will be subject to several layers of analysis, including transformation, tokenization, business rules application, ML algorithm modeling, and reporting.

The dimensions of data, fraud probability, fraud categorization, etc., are reported by the healthcare claims analytics solution. Such information provides a clear perspective for adjusters to handle the claim process.


Subrogation opportunities can be identified in insurance claims using analytics. At the basic level, predictive modeling and text analytics can identify such opportunities. Once the insurance claims system identifies, arbitration is chosen to resolve outstanding claim disputes. After this ascertainment, the insurer has the legal proof and valid data to pursue compensation for the insured from a third party.

The compensation recovered from the third party will be the claim amount of the insurer. Amounts recovered through subrogation go towards an insurance company’s bottom lines. Therefore, it is a critical component of business success. Using healthcare claims analytics helps insurers determine whether it is feasible to go for subrogation or not.

Loss reserve

Loss reserving is the process of estimating an insurer’s projected future liability. The liabilities may arise from future claims. An insurer can reserve liquid assets to account for future liabilities or payouts if they arise or are required to be made. But traditional ways of estimating future payouts are not effective. This is where insurance claims analytics comes in for the rescue.

Claims insurance analytics use advanced modeling techniques to assess the odds of claims being raised, when claims can be made, the claims amounts, insurance product types and their corresponding estimated claim amount, etc. Analytics models automatically adjust loss reserve computations as per the changing dynamics of the business.

Settlement optimization

Settlements made rashly and without much thought will slowly pile up losses for an insurance company. All insurers have one fear of overpaying on poorly vetted health insurance claims data. For example, it is difficult to ascertain the true value of loss in a region affected by arson or riot. An insurer could be under pressure to pay out claims by the affected. Due to lack of time, the insurance company could overpay the claimants in the process.

Insurance claims analytics can help insurers assess claim size and optimize settlement amounts. The automated solution can provide valid rationales to counter when the claimants require computation reasoning.

Automated claims adjuster assignments

Claims adjusters scrutinize and investigate claims. They determine the validity and viability of the claim and assess the types and nature of documentation furnished by the claimant. There are various types of insurance adjusters. Complex claims need expert insurance adjusters, and simple claims can be assigned to less experienced adjusters or general adjusters.

But due to a lack of claim analytics in insurance, it is difficult to ascertain the nature of claims. So complex claims could land up in the queues of less-experienced adjusters, and the expert ones could handle simple ones. Using insurance claims analytics, data insights can yield decisions that will automatically assign claims to the right insurance adjusters.

Litigation propensity score calculation

Claims management costs can escalate if disputes are raised on the claim amount disbursed. A lawsuit filed against the insurance company by the claimant will result in expenses for the insurer. Defending the claim amount disbursed in the court of law would involve legal expenses. Using analytics, insurers can estimate the litigation potential of a claim.

The analysis by an insurance analytics solution can help insurers estimate the loss reserve ratio to earmark for defending any potential lawsuits. Analytics can yield insights such as the length of the dispute, legal fees involved, and the complexities therein. Using this analytics data, insurers can plan their claims management accordingly.

How to choose a good insurance claims analytics solution or solution provider?


Check for the features of the insurance claims analytics or healthcare claims data analytics solution from the insurance claims analytics solution provider. The features it can have are:

Predictive analytics: Using data to provide accurate claim amounts, duration, litigation propensity, loss reserving, etc.

Fraud detection: Claim analytics in insurance identifies suspicious claims, data, and claimants. Automatic upgrades to risk alerting, fraud alerting, and risk mitigation processes based on internal deep learning mechanisms.

Loss ratio analytics: Expenses related to claims management should ideally be at a minimum compared to the premiums earned for an insurer. Loss ratio analytics uses data to get this computation.

Contributory database analytics: Analyzing health informatics data generated by members of the insurance market builds competitive, marketing, and economic intelligence.

Telematics:  Insurers can use telematics data to assess the risk potential of their insured customers. It helps insurers profile customers based on their lifestyle, driving, and consumer habits.

Dashboards and reporting: The insurance claims analytics solution should have interactive dashboards, comprehensive reporting, report integration, and customized reporting options.

Client Reviews

Understand client testimonials. Ask for references. Assess the type of customers that are using the insurance claims analytics solution. Importantly, try to handle the way the solution is being used. There are several out-of-the-box capabilities of any solution.

But a client could require customizations. The insurance claims analytics solution provider could have made these customizations. So after purchasing the solution, implementing these customizations could be a good idea, given that the implementer has already done it for another client.

Often, an insurance claims solution provider could also have several other related solutions or a suite of solutions. This company could provide consulting services and implementation services. They can guide insurers on the best course of action to implement the solution to address the insurer’s business problems.

Option for solution enhancements

An implemented solution is evolving, and technological solutions have to adapt to keep pace with that change. The enhance-ability of an insurance claims analytics solution should be non-disruptive, backward compatible, and future-proof.

The quality of a solution such as this should be that it should support upgrades, integrations, modularity, and even vendor-neutrality. The solutions provider must have a history of conceptualizing, implementing, and deploying enhancements.

Option for obtaining consulting services

It helps to have an insurance data analytics solutions provider that can also provide consulting services. Often these are two sides of the same coin. A solutions provider who understands the industry dynamics well is adept at providing solutions that address customer problems completely. Such a solutions provider is customer-centric, market-centric, and problem-solving-centric.

More often than not, purchasing an insurance analytics or claims analytics solution from this solutions provider is a sustainable option. This is because the solutions provider is vested in this industry and is also trying to improve business outcomes using the consulting capability.

Option for obtaining custom application development

Although the insurance sector is fairly homogenous, insurance companies are not. This creates a need for custom software application development. So an insurance claims analytics solution for one company may not be fully pluggable for another. A solutions provider should be able to customize a solution or offer custom software development services in insurance claims analytics.


The above article demonstrates how critical it is to have insurance claims systems. An insurance claims analytics from an insurance products solution provider is the best option for insurers. This analytics solution for the insurance industry is cutting-edge and part of a holistic ecosystem of related and peer products. Also, the fact that they provide consulting and custom application development services completes the circle.

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