Data is the backbone of insurance, and any discrepancy in the same can incur unwanted business costs. The customer data is often found in databases, spreadsheets, and End User Computing (EUC) files. It contains a lot of sensitive information and financial data, the security of which cannot be compromised at any cost.
Insurance is provided on the basis of the accuracy of this data and only then can the companies provide the promised coverage. While most insurance companies have standard procedures in place for assessing the individual for insurance, they often ignore how important data accuracy actually is.
There have been cases where even the basic information entered was wrong thus resulting in a loss of time and effort. Therefore, there is a dire need for data accuracy in the field of insurance. It will enable companies to assess current as well as future risks that will help them build precise predictive models.
Role of Data Accuracy and AI in Insurance
Insurance companies often underestimate the importance of data quality, failing to understand how big a role it plays in the overall assessment. However, the truth is that reliable data will only enhance the business processes and help in serving the customers better. Being negligent about the same costs companies millions of dollars which they can easily save by being vigilant about data.
Needless to say, the functionality of the Claims Department will exponentially improve with accurate data in place. Customers come to the insurance companies in their toughest times and if their claims are not addressed then they have nobody to turn to. As a responsible insurance company, it is crucial for them to prioritize data accuracy.
Top 9 reasons as to why data accuracy in Insurance is a must
The following are some of the reasons highlighting why data accuracy is a must in the field of insurance.
1. Prediction based on data collected
One of the primary things any insurance company must have is access to reliable data as it is the foundation of prediction and analysis. With the help of the exact data, insurance companies can run predictive models and use them to make business decisions. The information fed into the databases should be error-free and concise.
A case in point would be the US Mortgage industry which underwent an upheaval of sorts (2007-2008), caused by both Fannie Mae and Freddie Mae (housing loan). The housing bubble collapsed due to various factors but one causative factor was ‘bad data’ and the fact that neither of these two companies was able to run accurate predictive models and ended up with a loss of $360 billion USD.
This only underscores the need for accuracy of data as it is a key to running perfect predictive models and using the same for business analytics.
2. Price fixation
It goes unsaid how vital pricing is for a customer while choosing an insurance plan. It is indeed a decisive factor based on which customers usually shortlist the prospects. Insurance companies offer myriad insurance plans for customers based on their requirements. They also tweak the costs provided, depending on what the competitors are offering.
However, with good quality data, the insurance company would be able to accurately assess the core features, customer affordability, and functionality of the said insurance plan. This helps the company in coming up with a price that ensures profitability. Naturally, an insurance firm would be able to achieve the same with accurate data.
3. Dependency of price on data
Customers are always browsing the web and looking for insurance plans. They compare the premiums to choose the best option as per their budget. All this data can be easily captured by insurance companies through data analytics. It helps in determining which customer has expressed interest in their plans and how far they are in the sales funnel.
Not having access to this data makes companies lose the valuable opportunity of utilizing this information. They can develop effective marketing campaigns with the help of such insights. Moreover, they can also track their prospects and convert them to become customers only if they have access to the data. This makes it evident that the importance of data accuracy prevails.
4. Helps to conclude distinctions
Most of the companies in the insurance sector charge higher premiums for individuals with unhealthy habits like smoking and drinking. The logic used here is that those who consume alcohol and smoke regularly are prone to diseases and so should naturally pay high premiums.
However, companies seldom consider the genetic makeup of customers, which is undoubtedly a contributory factor for illnesses. Artificial Intelligence has now made it possible to develop better analytics that also takes into account the genes. This would enable companies to assess the risk in a better way. Moreover, it is a holistic way of evaluating the health of an individual and then determining the premium accordingly.
5. Breakdown fraud
Insurance fraud is no news, and it has been around ever since the inception of insurance companies. There are several fraudulent claims which are successfully filed each year. Just to put it in perspective, insurance companies lose nearly $30 Billion each year as a result of fraudulent claims.
These false claims are often filed on account of incorrect data, which is either entered initially during registration or at a later stage. Such instances reveal how insurance companies fail to detect false cases.
However, if the insurance companies had access to accurate data, then they would be able to run better analytics. This means identifying suspicious patterns that help in getting to the root of the problems way before the loss occurs. Data analytics and AI make it possible to steer clear of such incidents, thus saving huge amounts of money.
6. Investigation against malpractices
As mentioned above, fraudulent cases are rising in all insurance segments like auto insurance and home insurance. It is evident that insurance firms find it challenging to secure their models from such frauds and even prevent them in the first place.
Fortunately, with the help of data analytics, insurance firms can now blacklist specific profiles and run an insurance investigation on them as soon as data discrepancy is detected. This also helps them in taking corrective measures and being aware, thus ensuring higher data security. Investing in data analytics might seem like an expense, but it is nothing as compared to the millions of dollars spent on fake claims.
7. Creating customized marketing strategies
Another benefit of accurate data is the ability to develop effective marketing campaigns that help insurance firms in getting more customers. Most insurance companies rely on generic marketing strategies that are not data-driven. Such approaches do not give the desired results.
Whereas, with the help of quality data, the insurance firms can analyze customer information, understand them better, and then design products that are relevant. Social media data also helps in knowing user behavior and determining what customer wants exactly.
This information enables companies to develop perfect and more effective marketing campaigns that would reach the target audience better. This should help insurance companies sell their plans more effectively online.
8. Easy comparison
The analysis is the key to improvement and accurate data helps with the same. Insurance companies can revisit their policies and change them to mitigate risks. Analytics helps the firm in streamlining information in a better way and incorporating robust security measures.
This naturally results in an enhanced customer experience. When the customer delight is improved, there are bound to be more referrals thus bringing in more business for the company. The reason why insurance companies should consider data analytics and AI is because it helps in comparative analysis.
9. Identifying errors
Most insurance companies often consider errors during registration to be normal. It could be a misspelling of a name, DOB, address or age, and the like. This might seem like a small error but it has gruesome repercussions. For instance, people cannot claim insurance if their name itself is incorrectly fed into the system. As a matter of fact, such errors have costed companies in Millions of USD. Not to mention how soon the company loses its reputation and trust among customers due to such incidents.
These are not insurance frauds but have happened on account of human error. A way out would be for companies to hire proofreaders who ensure that the data entered in insurance forms is accurate. Another alternative is to go for an automated program that checks all data for accuracy without the need for human intervention.
This is where AI comes into the picture. Insurance firms can save the costs of hiring proofreaders by paying a one-time charge of this software. Spending a little on technology for preventive measures is anytime better than incurring considerable losses in the long run.
Data Accuracy Tools-The Need for the Same
Insurance data accuracy is one step ahead of a person. Insurance companies can use the DVSUM tool as it has the potential to bring about positive changes. It can also assist the companies to get accurate data from the customers and to provide them with better services. This is the best step to put a proper price tag on the premiums and to increase the chances of more profit. Even the fraud use of data can be brought down with the help of insurance data accuracy derived from DVSUM.
