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Managing claims in insurance is a vast process. It involves taking care of all activities important in the claims processing cycle. Efficient claims processing helps in many ways. They include customer retention and improving the quality of service etc. The common challenges faced in processing insurance claims may hamper the customer experience. The risks involved may tamper the company reputation, if not handled efficiently. These may include

  • Improving customer outcomes
  • Understanding and Improving operations
  • Improving claims outcomes
  • Superior customer service and support etc.

While companies may try to lower these challenges in claims processing. Data analytics is emerging as a prominent solution to all the woes. Actuarial prediction or assessment of insurance risks is not based on gut feeling. It is a precise and foolproof method that involves complex calculations. The machine learning tools are built on strong theories. The concepts can be from financial economics, statistics, and mathematics mostly. Insurance companies are able to undertake their actuary and underwriting tasks. They are not able to overcome the insurance claims processing challenges in an efficient manner. This is where Data Analytics tools can help them simplify processes.

The role played by Data Analytics to safeguard insurers

It is not just underwriting that has become easier with the help of analytics. Insurance companies use Key Performance Indicators (KPIs) to track the efficiency. The KPIs measured are pricing, marketing and sales, customer relationships, and insurance claims. They also need to know whether these strategies are leading the company. The goal of utilizing is to bring towards its goals and objectives.

Adjusters find it difficult to sift through vast amounts of insurance claims. It becomes tedious having to handle a multitude of policies and claims at the same time. Besides, if they miss a key piece of information, they cannot make the best decision. In the old days, decisions were based on gut feeling. This was the case because of the paucity of information. But with the help of Data Processing tools, adjusters can now identify claims that need in-depth inspection. They can identify them before settling, those that are important are executed first.

Listed below are crucial areas where Data Analytics has helped. These solutions are the proof that Data Analytics can overcome insurance claims processing challenges. They are

Insurance fraud

Spotting a fraudulent one among hundreds of insurance claims is tough. Expert con artists find ways to dupe the insurance companies into settling the claim. But, with predictive analysis, the algorithms can identify fraudulent claims. The analysis are faster because they are a combination of the old rules and new tools. The tools run on real-time data mining, testing for the search-and-exception scenarios. This information is collected across all stages of the insurance claim cycle.

Subrogation

Substitution of one person for another and claiming is fraud. The impostor can claim against the actual person against any claim. This can be prevented effectively with Artificial Intelligence data management systems. In this case, the predictive analysis tracks text messages and similar unstructured data. The analysis involves finding certain words or phrases. This that could hint to a subrogation claim. Early detection minimizes loss and reduces operational costs.

Instead of identifying the fraud at the later stages of processing, by using the AI data management we can create a chat bot during the First Notice of Loss (FNOL) stage and make it to interact with the customer and record their responses. Then predictive analysis tracks the text messages and compares them with the similar unstructured data and based on that it can notify that this interaction is fraudulent.

Settlement

Overpaying on an insurance claim is a big loss for the company. Considering the volume of concurrent claims, it will be a major disaster for the insurer. Predictive analysis with an efficient machine learning model will optimize the settlement. The track claims history, similar claims, and other parameters are captured. This helps in settling the amounts in an efficient manner.

Future claims

It is known as “loss reserve”. At the time of processing insurance claims, the insurers have to ensure this. The insurer should be free of any liabilities against future claims. This is especially important in the case of insurance claims towards employees’ compensations. The Business Intelligence tool will cross-check the new claim with other similar claims. The model will assess the loss reserve. It then finds out how much money needs to be paid right now to meet future claims.

Disputed claims

Insurers have to keep a significant portion of their company’s resources to defend the litigation. But with predictive analysis, disputes will come down dramatically. With the help of Data Scientists, the insurers can determine essential ratios. They can compute the percentage of disputed claims and resources spent for defending. Thus, the probable disputed claims can be identified in advance. These policies can be given to more experienced adjusters. The experienced adjusters might be able to settle the claims at lower amounts at a faster rate.

Customer relationship

Settling claims on time is a vital part of the insurance business. The insurers’ reputation is based on this key factor. Furthermore, access to limited data will result in reassignments and delayed settlements. This will lead to lower customer satisfaction. Whereas, Data Analytics tools will cluster the loss-characteristics. In order to assign claims to the adjusters accordingly. They have grouped adjusters who have experience in similar claims and loss-categories. This will speed up the process of settling claims and lead to the better customer experience.

Challenges in Insurance Claims Processing

Modification to the Norms and Regulations of the Government

Rules and regulations will be changing repeatedly after a period of time. This situation occurs in every country. This is the major challenge faced during insurance claims. The policy is different for every country and that would go on to impact the customer satisfaction. So every agency should focus on how to overcome this challenge to efficiently manage the claims processing and to improve the customer satisfaction.

Documentation Process

Insurance industry piles up a large volume of documents and managing them will be a tedious process and time consuming too. This time-consuming process, in turn, consumes the speed of insurance claims processing and leaving customers frustrated. Incorrect claims result in it being rejected and the customers do not re-appeal because they think it as a waste of time and this, in turn, reduces the revenue generated by the industry.

The possibility of Insurance Fraud

This is mainly linked with the previous challenge. Due to stack up of the papers and excess workload, errors will occur during insurance claims processing. Consequently, if submitted documents are not verified properly it leads to a con.

How Claims Processing is Easier with Data Analytics

Accessing Big Data to eliminate irrelevancies and redundancies is how Data Analytics helps. However, to beat the competition many insurers also employ usage-based methods. The incentivized insurance like no-claim-bonus for policy renewal is one way to do so. Furthermore, outsourcing customer service is another way insurers try to reduce their workload? Therefore, they can focus on core insurance solutions. But it is the companies that are using the professional Data Scientists become big. The companies can rise from small-time companies into large corporations with the help of analytics.

Whether it is property-related insurance or life insurance, the companies detect fraudulent claims. They can now detect potentially fraudulent or disputed claims through data mining. The framework is sophisticated that and analysis predicts before the event occurs. Identifying claims that are fraudulent has to be 100% accurate. The insurers will lose a lot of money if they settle a truly fraudulent claim. On the other side of the spectrum, the insurer cannot penalize a genuine claimant. This can happen if he is erroneously identified as fraudulent. Machine learning models will make the insurer’s decision correct for every claim. Cross-selling and acquisition of policies is another activity that insurance companies indulge in. Predictive analysis always plays a major role in multi-channel marketing strategies. Therefore, insurers can expand their customer bases and enhance conversion rates.

How are Data analytics tools applied in the insurance industry?

When machine learning models are fine-tuned for the insurance industry, they have the following key features:

  • Underwriting: DA tools mine data and carry out business analysis, policy renewal analysis. They help in the prediction of a premium amount by the term of policy and other relevant factors.
    It also considers risk factors, professional risks of the insured, etc.
  • Operations: Operations involves data mining for workflow analysis. Certainly, it would help the insurer to process the claims faster and in a more accurate manner. In contrast, they include missing but relevant submissions as well.
  • Aggregation: This is usually applicable in the healthcare insurance sector. When a family is insured for a certain amount as a collective whole, the excess amount is not honoured. Therefore, if the claims for the year exceed the limit, an aggregation limit has to be set. This aggregation limit has to be set in advance and must be the optimum figure to reduce losses.
  • Catastrophe: Machine learning models to predict catastrophic claims in advance in group insurance. This is similar to the aggregation. Similarly, they are involving employer-employee situation, instead of a family scenario.
  • Premiums: When DA tools analyse historical evidence of claims, the reports help the insurers. They help to set the premiums for renewals as well as future policies of the similar types. It helps in finding the optimum premium after taking all factors into consideration.
  • Brokers: The performance history of the brokers also affects the success of the business. Losses can be reduced considerably by studying the performance of different brokers.
  • Forecasting: Predicting whether a particular claim is going to be fraudulent or will lead to disputes is new. This predictive analysis alerts the insurer if it suspects that there might be a fraud in the claim.

CONCLUSION

The life-cycle of an insurance policy starts with setting the premium and drafting the parameters of the policy. In between, there are many factors that affect the processing of claims where DA tools help. The Business Intelligence model analyses data pertaining to property loss or repairs claims. It sorts out complex claims and helps in loss adjustment. The tool tracks the activities of brokers, claimants, beneficiaries, licensed adjusters. Any player who would affect the amount of claim is tracked. Insurance companies need to balance between customer satisfaction and loss adjustment. A Data Analytics model will mine real-time, complex, and vast amounts of data in the workflow. Therefore, the allocation of the claims processing can be handed over to the most experienced adjusters. This can help them overcome insurance claims processing challenges.

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