Improving Data Quality for the Insurance Industry Why and How

How does an insurance company determine who is and who isn’t eligible for a policy? How do they decide on the premium that must be paid? How do they know which claims should be settled and which are fraudulent?

The answers to all these questions can be unlocked using data.

As we all know, insurance is a customer centric industry and highly dependent on data. It would be fair to call data the foundation for the industry. Just as how a house built on a weak foundation will collapse, an insurance company that uses poor quality data cannot be expected to be successful. Let’s find out how data quality can make or break your insurance business.

4 Ways poor data quality affect the insurance industry?

Well, improving data quality is a fundamental challenge for this sector. Some of the ways it can create roadblocks for the insurance industry are:

1. It Compromises Customer Experience

Apart from the policies offered, it’s the ease of applying for an insurance policy and the customer experience that keeps customers loyal to a company. If the company uses poor quality data, communication between them and the customer will be flawed. This could be as trivial as misspelling the customer’s name or as serious as incorrectly judging his/ her eligibility for an insurance policy. On the other hand, good quality data could improve the customer’s overall experience. For example, having reliable data about an individual’s personal details can shorten forms and simplify the onboarding process.

2. It Hides Opportunities

Today, insurance isn’t sold only through agents; many people go online to find the best insurance policy for them and their families. Thus, there is a considerable amount of data available on the type of insurance and premium prices they are looking for, where these people are based, etc. If a company is unable to access this data, they do not get a holistic view of their customer base. For example, if their data is limited to potential customers in city A, they may miss out on opportunities available in the neighboring city B. According to a report, companies that use data analytics can generate twice the average profits.

3. It Impacts The Underwriting Process

Underwriting is one of the crucial steps of finding the right insurance. An underwriter’s evaluation of people and their assets is wholly dependent on the data available to him. An underwriter must have data about the individual’s lifestyle finances, medical history, etc. Not having the right data can lead to applications being rejected even though the applicant was eligible for insurance. On the other hand, having the right quality data can help insurance companies expand their customer base and simplify underwriting. For example, reliable data about the customer’s health could reduce the number of tests needed to prove eligibility for insurance policies and thus simplify the process.

4. It Slows Down Claims Settlements

How a company settles claims is one of the most important factors influencing their reputation. Not having credible data can slow down the claim settlement process and cause frustration for customers. Poor quality data also makes it difficult for insurance companies to differentiate between genuine and fraudulent claims. In both cases, it affects the company’s credibility as well as their margins.

Improving Data Quality For The Insurance Industry “ Top Tips

Insurance companies are beginning to realize the importance of good quality data. Improving data quality is not very difficult but it must be an ongoing process. Some of the steps that could help are:

· Check Source Reliability

The first step to improving data quality is to ensure you have access to complete data and that this data has been taken from reliable sources. Data should be cross checked against multiple sources to ensure its authenticity. Government records are typically the most reliable source. Breaking down corporate data silos and making them compatible with each other can also help bring data together and create complete personas for each customer. AI and machine learning have proved quite helpful in this area.

· Incorporate External Data Sources

There are a number of new data sources available that can transform the insurance industry. For example, telematics sensors on automobiles and mobile apps can automate claim management. Data from such sources reduces the company’s dependence on information shared by the involved parties to determine liability. AI and machine learning can further help resolve claims faster as well as identify fraudulent claims and protect the insurer. Smart sensors can also help lower the potential losses that insurance companies may need to cover each year by alerting the agency and customer about maintenance issues that need addressing. For example, sensors can detect fires in their initial stage and alert agencies to put them off before it becomes uncontrollable.

· Use Software To Enrich Data

Data enrichment software can play a crucial role in improving data quality. For example, a customer may have changed email addresses since the time they took up a policy but failed to update their details. Data enrichment software can identify inactive email addresses and prompt agencies to update their records. Similarly, they can help complete addresses and format them correctly so that none of the information is lost. This software can also help determine connections between different files and help put together a more comprehensive customer profile.

· Design Data Governance Processes

Companies also need to design data ownership and data governance roles. Many companies already have a data protection officer but this alone is not sufficient. Quality managers must also be brought in and upper management must take ownership of the company’s data. Access and data transfer processes must be defined and approved to ensure that the quality is maintained.

In Conclusion

Information is money and this statement holds
true for the insurance industry. Having access to the right data at the right
time is what differentiates a successful insurance company from a
not-so-successful one. Today, investing in data quality improvement
measures and data driven technology is a must. It is only with this that
insurance companies can offer better customer services and make their processes
more efficient while