In the property & casualty insurance market, the pressure toreduce costs, increase market share, comply with changingregulatory and financial reporting requirements, and meet risingcustomer service expectations—all in an efficient and relevantmanner—has never been greater. Because claims reaches into so manyareas of an insurer's operations and is its most highly-visible,customer-facing service, many P&C carriers have undertakenclaims transformation initiatives to help them meet market andcustomer demands.

Insufficient change management and non-integrated, stand-alonetechnology solutions are among the common challenges an insurerfaces when undergoing a claims transformation project. Anotherfrequent and potentially more serious challenge is the inaccurateor incomplete conversion of large amounts of data to new claimsplatforms, opening the door to budget overruns and potentiallyimpacting the performance and benefits anticipated from thetransformation initiative.

In the majority of claims transformation projects, large-scaledata conversions are required to decommission old claim systems andto ensure that claims data from old systems is migrated to the newclaims platform. When this critical step breaks down, it istypically due to a poorly-defined conversion strategy andreconciliation criteria, lack of clarity in defining the futurestate of the claims operation, insufficient alignment of claimsdata conversion strategy with the overall deployment strategy andlimited subject matter expertise across the program. Many insurerssimply underestimate the level of effort required to complete thecomplex task. This is especially true when the overall program issubject to very tight project timelines.

If data is not converted accurately and effectively, futureclaims handling can be significantly compromised and the quality offinancial reporting may be called into question. In this sense,successful data conversion is a baseline requirement for achievingthe full value proposition associated with successful claimstransformation. The stakes are high, which is why data conversionrisks must be clearly identified, taken seriously and matched torobust mitigation plans. Business and technology leaders must cometogether to ensure their data conversion approach is well designed,properly resourced and built into plans for the entiretransformation effort.

Why It Matters

Given the highly visible and strategically critical nature ofclaims transformation programs, insurers must develop robust andcomprehensive plans covering a range of areas, from softwareevaluation and selection to organizational change management. Theseare all key drivers of success. One commonly overlooked area isclaims data conversion.

The primary objective of claims data conversion is to transformlegacy claims data to new claims platforms in such a way thatlegacy claims can continue to be processed within new businessprocess configurations. Migrating data from a legacy system to anew platform may sound like a simple enough process, but inpractice the challenges are many and potentially significant.Companies that get it right, however, will give their claimstransformation programs a significant head start.

Fundamentally data conversion matters because insurers simplycannot modernize their claims operations or adopt advancedanalytical capabilities without moving onto new platforms thatprovide reliable access to important historical data. The impactcan be felt across numerous functions and in discrete operationalways.

Claims staff must be able to access necessary historical claimsrecords to effectively adjust and settle the claims. Customerservice reps must be able to see full claims histories and statusinformation if they are to answer customer questions and improvequality service. For finance, successful data conversionshelp ensure accurate financial reporting. Agents and brokersand policy administration systems need access to historical claimsrecords too. And, there are regulatory impacts to consider aswell.

Carriers that successfully convert claims data and transformtheir claims operations have a distinct advantage in the cost andefficiency of their compliance programs. For all of these reasons,it is critical for insurers to keep in mind the implications oflarge-scale data conversions as they plan for broad-based claimstransformation.

Common Challenges

Given the volume of claims data, age of the systems andcomplexity of the environments involved, it is no surprise thatmany of the common challenges in large-scale data conversions arerelated to identifying the correct scope of conversion.

The scoping of data conversion is highly dependent upon theoperational and technical landscape of the claims organization.Typically, the scale of data conversion is influenced by factorssuch as the number of lines of business, the number of claimsoffices and users, the number of claims systems in use, and thearchitecture and availability of data in the operationalenvironment. In general, the more systems and data repositoriesthat are involved, the more complex, time-intensive and risky thedata conversion process will be.

While some insurers underestimate the number of datarepositories and specific data volumes that must be converted,other carriers overestimate them. Most insurers possess hugevolumes of legacy data, but not all of it must be converted forsuccessful claims transformation. In some cases, large insurers mayconvert more data than is necessary. This may occur when many linesof business and internal functions use the same or similar datarepositories. Converting duplicate data from redundant data sourcescan unnecessarily expand the scope of conversion and slow theoverall conversion process. However, data sources that should beconverted may be overlooked in particularly complex environments.If that data is not converted, it may not be available for futureuse.

Another potential complication for scoping efforts: previous andincomplete attempts to migrate legacy data to other data models.These unsuccessful conversions may present significantcomplications as project planners and data architects attempt todefine precisely the systems or versions of the data sets that needconverting. The fact that half-completed conversions are fairlycommon across the industry suggests the many difficultiesinvolved.

During the scoping phase, data conversion teams must seekvisibility into interdependencies across projects. Insufficientinsight into and planning for the impact of in-flight legacyprojects on existing claims processes and downstream reporting canlead to unpleasant surprises during data conversion.

Because many legacy claims systems are old, there may be a lackof detailed technical knowledge about underlying data models. Theremay be only a few individuals who understand how legacy systemswork and where the data is housed. Further, the documentationrelating to legacy systems is often incomplete or out of date. Withan aging technology workforce at many carriers, these issues arelikely to become more serious in the coming years.

The lack of insight into these core systems means dataconversion requirements may not be correctly defined, with the endresult of lower quality and accuracy. If all the necessary datacannot be extracted from the legacy systems, the data and financialreconciliation between the old and new system may not be accuratewhich could lead to a poor quality of data being converted to thenew systems.

The quality of older claims data is typically lower than thequality of newer claims data, due to the gradual addition of moredetail to the data model over time. This variance can make dataconversions more complex and challenging, especially in terms ofsetting the right approach.

Planning for Success: A ProvenApproach

While there is no single path to successful claimstransformation, there are clear principles and proven practicesinsurers should undertake as they set out to conduct large-scaledata conversions. Individual carriers will determine the exactrequirements for converting historical data based on data retentionpolicies, processing and informational needs of the relateddepartments and overall transformation goals, including thedecommissioning of legacy systems and planned operational benefits.In most cases, the amount of data that needs to be converted isinfluenced by the carrier's data archiving and data retentionguidelines and acceptable level of historical data necessary tohandle future claims.

Further, insurers will want to consider the following steps andphases as they plan data conversions in the context of broaderclaims transformation initiatives.

Design a blueprint: The data conversionblueprint should define key data conversion milestones aligned withthe program transformation roadmap. The conversion blueprint shouldsupport the business objectives of the program and influence theroadmap only in cases where there are significant data conversioncomplexities that need to be addressed to de-risk the program.

Assess the Impacts: In looking more closely atthe decommissioning of specific legacy systems, project teamsshould conduct a detailed process and data impact analysis. Theprocess impacts typically include issues related to downstreamreporting systems and interim changes to the legacy systems thatmay be required to enable smooth conversion. The data impactanalysis may identify data sources that must be decommissioned orconsolidated. It can also be used to derive a target claimsinformation architecture, which identifies all claims data sourcesand the nature of the data to be extracted and converted to the newclaims platform.

Devise a Deployment Plan: Large-scale claimstransformation projects should have a deployment plan that defineshow the new claims platform will be rolled out to field users.Typically, the deployment of new applications is accomplished inmultiple phases to minimize the risk, based on how, when and howmuch claims data will be converted to the new claims platform. Thatrequires clear plans addressing the extractions, cleansing, loadingand reconciliation of claims data in moving from the old systems tothe new platform. A multi-level testing plan should be included toverify individual data elements, simulate “mini-conversions” andidentify ways to reduce the overall time required to convert theentire data load during cutover.

Define Success Criteria: Given the many faileddata conversion projects in the past, it is critical that dataconversion programs clarify what success looks like. Typically,that means end-to-end testing of converted claims data andprocesses as identified during the impact analysis. Key successcriteria include:

• The reconciliation of converted claims data, transactions andfinancials between old and new claims systems;
• The production of data and financial reconciliation reportswith no discrepancies;
• The completion of comparison testing of the claims data on thenew platform with claim data from legacy systems, and
• Maintenance of a proper audit trail of the data conversionprocess.

Assemble the Right Tools: As insurersmove into deployment and cutover phases, they must ensure they havethe necessary technical tools and artifacts (such as testing andconversion scripts) required to successfully extract, cleanse andload required claims data onto the new platform. Two other assetsare worth mentioning. Detailed mapping documents specify all of thedata elements that must be converted to the new claims platformdata model and any gaps between the old and new data models. Assuch they serve as the basis for conversion scripts. Reconciliationreports for data loads and finance ensure that data counts from theextracted legacy data match the loaded legacy data in terms ofclaims payments, reserves and recoveries.

Lessons Learned

Successful data conversions provide a number of strategic andtactical lessons learned that can be used by insurers planningclaims transformation initiatives.

1. Perform data analysis as early as possible in theprogram: Studying legacy data to identify the full varietyof data—including claim types, policy types, coverage codes andfinancial codes specific to various lines of business—can helpidentify potential risks and bottlenecks early on and define theoptimal mapping to the future state.
2. Institute a data governance process: A robustgovernance approach provides “guardrails” for conversion bydefining the data model on the claims platform, typically driven bybusiness process configuration, legacy data models downstreamreporting needs, and the overall solution architecture.
3. Separate and segment legacy data: Tostreamline the conversion process, technical teams should segmentlegacy data based on it use. For instance, data needed fordownstream reporting should be separated from data required forongoing claims processing on the new platform.
4. Align to business needs: The scope of dataconversion should be aligned with the requirements of the claimsfunction, users of claims data and data retention needs.
5. Define broad, forward-looking requirements:Data conversion strategies should consider future business needs.For instance, on-demand conversion of legacy claims data into thenew platform may be necessary in the case of a large lawsuit. Thus,data conversion strategies should reflect the need for rapidlyextracting and loading archived or non-converted legacy claims datato the new claims platform.

In Conclusion

As claims transformation has become a competitive imperative formany types of insurers, the importance of successful dataconversions has only grown. The bottom line is that carriers simplycannot realize the full range of benefits associated with claimstransformation such as process efficiency, decommissioning oflegacy systems and increased analytics capabilities unless they caneffectively negotiate the challenges related to large-scale dataconversion. The significant number of transformation initiativesthat fail to produce the expected benefits underscores theimportance of mastering data conversion. Many of the breakdownsoccur when insurers don't properly gauge the level of effortrequired for success.

Further, a full return on large-scale investments in new claimstechnologies can never be realized unless vital claims data can betransferred successfully and legacy systems decommissioned. That iswhy carriers that adopt a proven approach to data conversion have adistinct advantage. They not only increase the likelihood oftransformation success, but also avoid the common risks andthreats, which invariably present a range of financial, operationaland regulatory compliance consequences.