Critical Compliance Technologies

To say that the insurance industry is tightly regulated would be an understatement. The compliance challenges insurers face are uniquely complex in that every U.S. state maintains its own distinct insurance regulations, and these laws and policies vary widely across different lines of business. Additionally, many states maintain regulations that govern virtually every aspect of insurance company operations, including the amount of financial reserves a given company needs to conserve; how it can market its products; and how much brokers and agents can charge for their services.

Moreover, most states impose strict reporting requirements by which insurers must regularly document their compliance with the various statutes. Depending on the policy type — life, home, auto, health coverage, and so on — reporting guidelines can require a confusing array of quarterly and annual reports, such as audited financial statements, unaudited financial statements, actuarial opinions, claim data, evaluations of securities on deposit, and disclosure of material transactions, just to name a few.

Additionally, insurers now face new federal compliance reporting requirements regarding workers' eligibility for Medicare. Department of Health and Human Services policy has long held that individuals over the age of 65 (that is, eligible to receive coverage under Medicare) who are covered under an employer insurance plan must first use those employer-provided health benefits before seeking Medicare reimbursements for healthcare expenses. In other words, if you're 65 or older and still have employer-provided healthcare benefits, that employer plan is your primary plan. With the Medicare, Medicaid and SCHIP Extension Act of 2007, which went into effect in July 2009, insurers must now document the eligibility status of their Medicare-eligible beneficiaries, whether they are drawing on Medicare benefits or not. These compulsory quarterly filings to the Centers for Medicare and Medicaid Services add yet another reporting requirement to insurers' already significant load.

Decentralized Data Can Hamper Compliance Reporting

The root of the compliance reporting problem is that many insurers rely on a decentralized IT infrastructure, with different systems for different lines of business, or a patchwork of systems built up over time as a result of mergers and acquisitions. The typical IT approach within most large insurers is to center systems infrastructure around agent/broker needs or specific policy offerings. For instance, it's common for firms to organize their IT infrastructure and data sources according to line of business — home, auto, commercial and surety — with each line maintaining its own system for claims, billing, customer service, and so forth. The benefit of this approach is that insurers can maintain a clear understanding of the hierarchy and history of their agent and broker networks. However, the drawback is that compliance officers have to create mandated reports by compiling data from multiple systems across the organization, rather than from a single source.

From a compliance reporting standpoint, this approach causes data quality issues because information often gets duplicated from system to system. A customer who maintains both homeowners' and auto policies with the company would have records residing in two separate systems. Similarly, investment account information might be housed in multiple data stores. This means that when it's time to compile reports, managers could have a difficult time determining which systems contain the correct, up-to-date records. IT systems that are organized around separate lines of business complicate regulatory reporting. This makes it difficult to map operations, processes, and information to specific reporting requirements. Under these circumstances, how can insurers best prepare themselves to ensure compliance with stricter regulations and to manage risk appropriately?

Master Data Management to the Rescue

Smart insurance company leaders are now seeking technology investments to help establish good governance models that will in turn make it easier to document and maintain regulatory compliance and lower operational risk. Master data management (MDM) is exactly this kind of investment. It ensures that critical enterprise data is validated as correct, consistent, and complete when it is circulated for consumption by internal or external business processes, applications, or users. MDM is an effective solution for compliance challenges because it can provide insurers with complete and accurate data about customers, products, operations and financials — even when that information is captured and stored in different systems. In effect, MDM makes it possible for insurance firms to overcome error-laden, redundant or siloed data to create a single correct version of the "truth."

It is important to keep in mind that not all MDM technologies can address the various compliance requirements facing today's businesses. If the MDM system is rigid in its functionality — meaning that it has a fixed data model — then you may end up compromising compliance initiatives to adapt to the limitations of the technology. Further, such systems may inhibit the expansion of your compliance efforts to other lines of business or geographies.

Given these precepts, an integrated, model-driven, and flexible MDM platform that is easily configurable is the preferred approach. In screening MDM options, it is important to consider key business data requirements across several critical business functions including sales, marketing, customer support, and (of course) compliance. By ensuring your MDM technology supports the following ten requirements, you will be well on your way to laying the foundation for a complete compliance program. Further, you will have the ability to evolve your MDM implementation to address unforeseen future requirements across the organization.

Your company's MDM technology should:

Manage multiple business data entities within a single MDM platform.
Permit data governance at both the project and/or enterprise level.
Work with your standard workflow tool.
Handle complex relationships and hierarchies.
Provide support for Service Oriented Architecture (SOA).
Allow for data to be cleansed inside of the MDM platform.
Enable both deterministic and probabilistic matching.
Create a "golden master" record containing the best field-level information to be stored centrally.
Store history and lineage.
Support both analytical and operational usage.

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