7 Ways Data Management and Analytics Improves Insurance Claims Processing

7 Ways Data Management and Analytics Improves Insurance Claims Processing
7 Ways Data Management and Analytics Improves Insurance Claims Processing

The foundation of success of every business and service sector is the quality of the customer service they provide. High quality customer service will drive up customer acquisition rates and enhance sales leads as well as lead to increased customer retention rates. The insurance industry is not an exception to this trend. Insurance claims processing is one amongst the three core elements of the insurance value added chain. The quality of insurance claims processing offered by companies can make or break the relationship between the insurance purveyor and its customer.

A superlative claims processing experience for customers can help re-define the existing insurance company-customer relationship. This would help to enhance customer retention as well as drive more customers to purchase insurance from the company, thus building their reputation and the market share. In addition, improvement and enhancement of existing claims processes as well as introduction of new practices can serve to bolster profitability.

One major avenue of fostering insurance claims processing improvement is by availing the use of data management measures and analytics to study the data and to evaluate it to judge its suitability for processing.

Here we describe 7ways in which data management and data analytics can help improve the efficiency of insurance claims processing.

7 Ways Data Management and Analytics Improves Insurance Claims Processing

  1. Identification of Good Data and Bad Data:

    Much data is generated on a daily basis, on account of various insurance related processes such as claims, from databases, data from fraud lists etc. It is important to sift through this huge amount of data and identify the useful and relevant data. Ensuring that the data obtained from different sources is screened, aggregated and then used for subsequent data analytics steps is the first thing that needs to be done. This may be a time consuming process but when accompanied by data cleansing strategies can help in cleansing the insurance claims database, facilitate the identification of genuine claims and focus on them.

  2. Identification of Fraud:

    A very high proportion of insurance claims made are fraud. Hence, it is essential to distinguish fraud claims at the earliest possible level, before it gets time to spread downstream and lead to unrecoverable costs to the company later in the form of huge payouts. Predictive analysis, that includes a variety of techniques, can be used to identify fraud at each stage of the insurance claims life cycle.

  3. Identification of Subrogation Cases: 

    Analytics can be used to identify subrogation cases from the huge piled up data distributed in the form of forms, police reports, and adjustor notes. Such cases can be identified using text searches through this data. Identification of subrogation cases can help in maximizing loss recovery.

  4. Identification of Litigation Cases: 

    Many times, insurance companies suffer from losses while defending claims which are being disputed. The use of data analytics on the dataset can be used to identify cases, in which the claims are more likely to lead to lawsuits. Companies can then manage their staff in such a way that these claims can be handled effectively and at lesser costs to companies.

  5. Quicker Settlements: 

    Fast track settlements are of great use and are often resorted to by insurers during emergencies such as natural disasters. They help to settle claims immediately and thus, the claims are not carried over. But there is problem here; doing this rashly can lead to inaccurate calculations and overpayment to the claimant. This can be more damaging to the company when a huge number of such claims have to be settled in a short span of time. The use of proper data organization, management and analytics procedures can help you set limits on the extent to which payment can be made. Outsourcing to an expert insurance claims processing service provider can also help you manage your cycle times for quicker resolution of the issue and lower costs.

  6. Improved Loss Reserving and Claims Forecasting: 

    Use of data analytics can lead to improved loss reserving and claims forecasting, on the basis of data from existing claims. This would help one ensure that cash reserves are on hand to meet claims made in the future.

  7. Data Mining Techniques: 

    Data mining techniques can be used to distinguish between claims on the basis of their complexity and would help to ensure that appropriate adjusters are allotted to handle the specific claim.

In particular, insurers can radically improve the claims processing by enabling claims processing integrated with the use of data management, business intelligence, and data analytics technology. Such an approach helps to increase the effectiveness of claims processing, as well as improve their operation as it leads to reduced claims costs, and serves to drive customer retention and acquisition. Outsourcing to an expert Finance and Accounting BPO Outsourcing Service Provider can assuredly provide valuable support in this direction.

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For information on how Invensis Technologies can deliver value to your business through Insurance BPO Services, please contact our team on US +1-302-261-9036; UK +44-203-411-0183; AUS +61-3-8820-5183; IND +91-80-4115-5233; or write to us at sales {at} invensis {dot} net.

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