Data Accuracy for Insurers – The Cost of Getting it Wrong

This blog was last updated on January 11, 2024

With tax calculations being mainly data-driven in the insurance industry, it’s imperative that data provided is complete and accurate. Yet gaps in datasets and missing information still happen from time to time presenting a challenge for insurance companies. These gaps can cause a delay in filings which in turn, can lead to penalties and interest imposed by tax authorities. In addition to this, inaccurate premium amounts may be seen, in worst case, as tax evasion which can have dire consequences for all organizations involved.

Back to the source

Inaccurate data can stem from various sources including human error, lack of an appropriate data collection tool, inadequate data management processes etc. Having the wrong data can have an adverse effect on insurers in so many ways – inaccurate pricing which can lead to loss of business opportunities, damaging to brand reputation, under-delivery on customer expectations, and fraud claims. Insurers seeking to price risks correctly, to improve customer loyalty and satisfaction and check fraudulent claims must ensure every effort is made to improve data accuracy.

As legislation in different countries changes frequently, there is the requirement for additional information from the various tax authorities. In Spain, for instance, Consorcio surcharges cannot be settled without mandatory information like tax point (inception, cash received dates), insured addresses, insured amounts (for extraordinary risks) for example. Portugal is no different with the broker’s commission, and insured tax identification required as crucial information. For Italy, data completeness is needed to keep the insurance premium tax (IPT) books up-to-date.

These are just a few examples of the importance of having complete data for IPT calculation purposes. However, across various territories in Europe, the following key information should be provided to ensure accurate tax calculations and timely settlement of liabilities.

• Tax points (invoice date, inception date, cash received date, maturity date etc)
• Class of business (PDBI, liability, goods in transit)
• Invoice currency
• Taxable basis / premium (gross premium, net premium)
• Premium tax calculated (insurer and insured taxes)

It’s worth noting that, when negative premiums are involved, it’s important to provide a detailed explanation on the reason for the negatives – correction of errors, cancellations, mid-term adjustments – to mention just a few. Failure to provide this can lead to a rejection of the negatives by tax authorities.

Improving data quality

With corrections and refunds being questioned now more than ever, it’s crucial that insurers take steps to improve data quality by identifying and understanding which data is mandatory for tax calculations. Some ways this can be achieved include:

• • Automating data collection, verification, and cleaning processes to reduce the risk of human error. In addition to this, cleaning data periodically ensures the effectiveness of IPT review and calculation processes
  • Reviewing data management processes to ensure the data obtained from customers meet tax authorities’ requirements
  • Regularly validating customer information to provide more accurate and up-to-date information

Issues with inaccurate data must be reduced to a negligible minimum for insurers to gain more meaningful actionable insights. Good quality data also means improved customer interactions, easier implementation of the data and improved productivity. Ad-hoc attempts to remove inaccurate data are of little value today because the volume of data and the pace at which it needs to be accessed and analysed are dramatically and frequently changing. Lastly, robust communication procedures and tools should be prioritised across organisations to get it right from the beginning of the insurance underwriting process to the tax filing to local authorities.