Why It Matters

Given the highly visible and strategically critical nature of claims transformation programs, insurers must develop robust and comprehensive plans covering a range of areas, from software evaluation and selection to organizational change management. These are all key drivers of success. One commonly overlooked area is claims data conversion.

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

Fundamentally data conversion matters because insurers simply cannot modernize their claims operations or adopt advanced analytical capabilities without moving onto new platforms that provide reliable access to important historical data. The impact can be felt across numerous functions and in discrete operational ways.

Claims staff must be able to access necessary historical claims records to effectively adjust and settle the claims. Customer service reps must be able to see full claims histories and status information if they are to answer customer questions and improve quality service. For finance, successful data conversions help ensure accurate financial reporting. Agents and brokers and policy administration systems need access to historical claims records too. And, there are regulatory impacts to consider as well.

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

Common Challenges

Given the volume of claims data, age of the systems and complexity of the environments involved, it is no surprise that many of the common challenges in large-scale data conversions are related to identifying the correct scope of conversion.

The scoping of data conversion is highly dependent upon the operational and technical landscape of the claims organization. Typically, the scale of data conversion is influenced by factors such as the number of lines of business, the number of claims offices and users, the number of claims systems in use, and the architecture and availability of data in the operational environment. In general, the more systems and data repositories that are involved, the more complex, time-intensive and risky the data conversion process will be.

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

Another potential complication for scoping efforts: previous and incomplete attempts to migrate legacy data to other data models. These unsuccessful conversions may present significant complications as project planners and data architects attempt to define precisely the systems or versions of the data sets that need converting. The fact that half-completed conversions are fairly common across the industry suggests the many difficulties involved.

During the scoping phase, data conversion teams must seek visibility into interdependencies across projects. Insufficient insight into and planning for the impact of in-flight legacy projects on existing claims processes and downstream reporting can lead to unpleasant surprises during data conversion.

Because many legacy claims systems are old, there may be a lack of detailed technical knowledge about underlying data models. There may be only a few individuals who understand how legacy systems work and where the data is housed. Further, the documentation relating to legacy systems is often incomplete or out of date. With an aging technology workforce at many carriers, these issues are likely to become more serious in the coming years.

The lack of insight into these core systems means data conversion requirements may not be correctly defined, with the end result of lower quality and accuracy. If all the necessary data cannot be extracted from the legacy systems, the data and financial reconciliation between the old and new system may not be accurate which could lead to a poor quality of data being converted to the new systems.

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

Planning for Success: A Proven Approach

While there is no single path to successful claims transformation, there are clear principles and proven practices insurers should undertake as they set out to conduct large-scale data conversions. Individual carriers will determine the exact requirements for converting historical data based on data retention policies, processing and informational needs of the related departments and overall transformation goals, including the decommissioning of legacy systems and planned operational benefits. In most cases, the amount of data that needs to be converted is influenced by the carrier's data archiving and data retention guidelines and acceptable level of historical data necessary to handle future claims.

Further, insurers will want to consider the following steps and phases as they plan data conversions in the context of broader claims transformation initiatives.

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

Assess the Impacts: In looking more closely at the decommissioning of specific legacy systems, project teams should conduct a detailed process and data impact analysis. The process impacts typically include issues related to downstream reporting systems and interim changes to the legacy systems that may be required to enable smooth conversion. The data impact analysis may identify data sources that must be decommissioned or consolidated. It can also be used to derive a target claims information architecture, which identifies all claims data sources and the nature of the data to be extracted and converted to the new claims platform.

Devise a Deployment Plan: Large-scale claims transformation projects should have a deployment plan that defines how the new claims platform will be rolled out to field users. Typically, the deployment of new applications is accomplished in multiple phases to minimize the risk, based on how, when and how much claims data will be converted to the new claims platform. That requires clear plans addressing the extractions, cleansing, loading and reconciliation of claims data in moving from the old systems to the new platform. A multi-level testing plan should be included to verify individual data elements, simulate “mini-conversions” and identify ways to reduce the overall time required to convert the entire data load during cutover.

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

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

Assemble the Right Tools: As insurers move into deployment and cutover phases, they must ensure they have the necessary technical tools and artifacts (such as testing and conversion scripts) required to successfully extract, cleanse and load required claims data onto the new platform. Two other assets are worth mentioning. Detailed mapping documents specify all of the data elements that must be converted to the new claims platform data model and any gaps between the old and new data models. As such they serve as the basis for conversion scripts. Reconciliation reports for data loads and finance ensure that data counts from the extracted legacy data match the loaded legacy data in terms of claims payments, reserves and recoveries.

Lessons Learned

Successful data conversions provide a number of strategic and tactical lessons learned that can be used by insurers planning claims transformation initiatives.

1. Perform data analysis as early as possible in the program: Studying legacy data to identify the full variety of data—including claim types, policy types, coverage codes and financial codes specific to various lines of business—can help identify potential risks and bottlenecks early on and define the optimal mapping to the future state.
2. Institute a data governance process: A robust governance approach provides “guardrails” for conversion by defining the data model on the claims platform, typically driven by business process configuration, legacy data models downstream reporting needs, and the overall solution architecture.
3. Separate and segment legacy data: To streamline the conversion process, technical teams should segment legacy data based on it use. For instance, data needed for downstream reporting should be separated from data required for ongoing claims processing on the new platform.
4. Align to business needs: The scope of data conversion should be aligned with the requirements of the claims function, 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 the new platform may be necessary in the case of a large lawsuit. Thus, data conversion strategies should reflect the need for rapidly extracting and loading archived or non-converted legacy claims data to the new claims platform.

In Conclusion

As claims transformation has become a competitive imperative for many types of insurers, the importance of successful data conversions has only grown. The bottom line is that carriers simply cannot realize the full range of benefits associated with claims transformation such as process efficiency, decommissioning of legacy systems and increased analytics capabilities unless they can effectively negotiate the challenges related to large-scale data conversion. The significant number of transformation initiatives that fail to produce the expected benefits underscores the importance of mastering data conversion. Many of the breakdowns occur when insurers don't properly gauge the level of effort required for success.

Further, a full return on large-scale investments in new claims technologies can never be realized unless vital claims data can be transferred successfully and legacy systems decommissioned. That is why carriers that adopt a proven approach to data conversion have a distinct advantage. They not only increase the likelihood of transformation success, but also avoid the common risks and threats, which invariably present a range of financial, operational and regulatory compliance consequences.