New federal regulations related to the Dodd-Frank Act arecurrently being rolled out across the financial services industry,affecting not only banks but insurance companies as well. The goalof these new regulations is to increase consumer protection byincreasing transparency. To accomplish this, new regulatorycommissions have been created to monitor all activities that arefinancial in nature. These activities include insuring,guaranteeing or indemnifying against loss, harm, damage, illness,disability or death.

Title V of the Dodd-Frank Act establishes the Federal InsuranceOffice (FIO). While the office has no regulatory powers, it doeshave the authority to request information. To prepare for thechanges ahead, IT departments are taking steps to update currentdata management systems in order to ease the burden anticipatedwhen the FIO starts requesting information. Currently, mostinsurance companies have multiple legacy systems spread across manydepartments. This means that data may be duplicated, incorrect, andin a variety of formats.

With increased levels of transparency, insurers will need toconsolidate information and report accurately to the new governingbodies. To improve data quality now, companies need to ensure thatdata is accurate, complete and standardized. Improving data in thisway will allow insurers to communicate accurately with regulatorsand also to improve customer service.

Sting of New Regulations

While the Dodd-Frank Act is still being implemented and theexact reporting information is being determined, most insurers areconfident that the level of required reporting willincrease. This will require insurers to modernize their currentsystems.

Data management systems are currently unable to easily searchthrough large amounts of data or handle the types of searches thatwill most likely be required by regulators. This is largely due tothe lack of centralized data management systems. Additionally,these systems are fraught with errors as there are few tools inplace to ensure accuracy.

One type of information that is commonly incorrect for insurersis customer contact information. Surprisingly, according to arecent Experian QAS study, 91 percent of financial organizationssuspect their contact data might be inaccurate in some way, and onaverage respondents think as much as 21 percent of their total datamight be incorrect.

With this level of inaccuracy commonplace, it is important thatinsurers recognize the need to improve contact data. Better datawill translate to better communicate with regulators by allowingstaff to consolidate data and have confidence in a higher level ofaccuracy.

Consolidation Requires DataAccuracy

Data consolidation will be an important part of new regulations,as insurers will need to report on customers and their policies.Since data is currently spread across multiple systems, insurersare investing in modern data management systems that willconsolidate data across the organization. While this is a stepforward in improving the accessibility of data, reporting willstill be inaccurate if data files are incorrect or duplicaterecords exist.

When organizations merge data, it is a common best practice tode-dupe information prior to consolidation. The first step in anymerge is to ensure that data is accurate and in a standard formatwhenever possible. Contact data can often be standardized throughback-end software tools, which also allows information to becorrected—if enough data is provided—and put into a standard formatfor comparison. Most tools allow users to configure the formatbased on specific business needs and preferences.

Once the contact data has been properly formatted, uniqueidentifiers need be chosen. These identifiers will be thedetermining factors in recognizing duplicates. Data points areoften the name, address, telephone number, or e-mail address. Sincedifferent information probably exists in each database, insurersneed to select elements that are common across all data, which iswhy contact information is so valuable in this process. It is oftena consistent field across databases. 

Without these two steps, duplicates may be difficult to spot.Software tools frequently require data to be formatted in a certainway so that information can be processed correctly, and manualreview can be time-consuming and inaccurate.

Ensuring Ongoing Data Quality

Once data systems are consolidated and existing data has beencleaned, it is important to guarantee the ongoing accuracy of data,especially contact data. Contact data not only affects a company'sability to remove duplicate records, but also contact customers,segment accounts, perform geographical analysis, and issueappropriate policies with accurate risk assessment.

Contact data should be verified before it enters a centralizeddatabase. Human error is the main reason financial servicesorganizations do not trust their data, according to a recentExperian QAS study. This means that verification should beautomatic, which virtually eliminates this risk.

Validation software-based tools should be put in place at eachpoint of capture. These include places like agent portals, policyservices and claims. That way, as information is provided by newpolicyholders or changed by existing policyholders, insurers canhave confidence in the quality of data.

In addition, the back-end scrubs that were described aboveshould be used on a continual basis and data should be run againstthird-party files. That way, if a customer moves or changes theirphone number, insurers have a better understanding of when thatmove took place, and can then reach out to the policyholder forupdated contact information.

To make sure that these tools are right for each business, ITdepartments should spend some time analyzing data before selectinga solution. This will allow them to find common data errors andmost frequently used information. This analysis allows projects tobe prioritized and solutions to be chosen based on proven needsrather than assumptions.

Realizing Benefits of AccurateData

Having an accurate, consolidated database allows insurers toimprove financial reporting and provides peace of mind asregulations change. Besides the benefits to regulatory compliance,accurate data also improve many other departments across aninsurance organization.

Each time contact information is entered incorrectly, that errorcould lead to many others, including an incorrect quote, a slowedclaim or a communication being returned. When any of these eventsoccur, staff members must spend extra time finding correctinformation. This slows down the processes and affects the bottomline and overall staff efficiency. Additionally, data errors hindercustomer service efforts, as customers may have to wait longer forclaim checks or may not receive required communications.

By improving the accuracy of contact data, processes can bestreamlined and customers receive a higher level of service. In abusiness with such high turnover, insurers can give policyholdersone less reason to leave and attract a continued renewal.

Accuracy Eases Regulatory Burden

By improving data accuracy now, insurers can be positive thatheavy investments in new data management systems will not bewasted. These new systems will allow insurers to report toregulators efficiently, but without accurate data, the new systemswill be ineffective.

While exact reporting structures are still being determined,taking steps today to consolidate data and improve accuracy willhelp improve efficiency and customer service now, while alsohelping insurers to prepare for the regulatory changes to come.