1.Insurance companies are faced with ongoing operational challenges in handling death claims validation
Over the past decade, most states enacted laws requiring life insurance companies to conduct searches for deceased policyholders using the Social Security Administration’s (SSA's) Death Master File (DMF), a database file that contains information about persons whose deaths were reported to the SSA, and to engage in outreach efforts to beneficiaries. To adhere to these laws, insurers either built or outsourced processes to match their policyholder data to the DMF or similar databases; however, the match criteria outlined in state regulatory agreements necessitated a secondary death validation to confirm a policyholder death – often a manual and time-consuming process. To complicate matters, thousands of erroneous deaths are reported in the DMF each year, further necessitating a sound validation process that mitigates the risk of a falsely reported death from a single source.
A death validation process that cross-references deaths reported in the DMF against other death sources, such as state vital statistics and obituaries, can reduce or eliminate the manual process that insurance companies use to validate deaths. This streamlined process can be automated for millions of records and completed in a fraction of the time that it would require of a person or team. It should use common data elements available across multiple death sources, such as name and death date, to identify the same decedent, but also accommodate for slight variations (e.g., nicknames, misspellings) and false positive reduction (e.g., common names) that might otherwise result in no matches or mismatches.
2.To account for a degrading death master file, insurance companies must implement processes to identify more decedents and reconcile benefits
Based on a reinterpretation of the Social Security Act in November 2011, the SSA removed approximately 4.2 million records (~5%) from the DMF that were submitted electronically by states and not independently verified through SSA field offices. This reinterpretation also resulted in the exclusion of more than 1 million records (~40%) from the DMF each year, and that count grows as more states begin to use electronic death registration (EDR). As a result, the DMF now captures less than 50% of nationwide deaths based on mortality data from the National Vital Statistics System (NVSS).
To mitigate this gap in the DMF, state vital statistics offices are often the first point of collection for supplementary death data. Each state, however, adheres to different standards related to the maintenance and release of death data. Most states either do not make death data available for commercial purposes – even those related to regulatory requirements – or provide only for defined requests for death certificates, which is not useful when attempting to verify the status of many policyholders at once. Furthermore, each state varies in the frequency with which they update their death data, with some states only finalizing their death data months, or even years, after a death occurred.
A more promising source for death data is obituaries and funeral home notices. Obituaries and funeral home notices, while lacking social security numbers, often include enough personally identifiable information (PII) to identify a deceased policyholder with a high degree of accuracy. Most compelling is that obituaries and funeral home notices combined with the DMF and state vital statistics records capture more than 90% of nationwide deaths and almost entirely mitigate the gap in the DMF.
3.Faced with a policyholder death, insurance companies often find it difficult to locate and contact a beneficiary
Even when an insurance company is aware of a policyholder death, it is often a difficult task to locate a beneficiary. In some cases, an insurance policy provides insufficient information to identify the name of the beneficiary (e.g., wife, son); in other cases, the name of beneficiary is known, but difficult to locate because the policy was issued years, or even decades, prior to policyholder death. Because of this, insurance companies are often forced to either rely on beneficiaries to proactively contact them upon the death of a policyholder or to engage in a prolonged and expensive process to identify and locate beneficiaries, many of whom may not realize they are owed benefits.
The advance of data mining and machine learning techniques in recent years greatly reduces the operational cost of this once onerous process. Next of kin, for instance, can often be parsed from obituaries using a machine learning technique known as natural language processing and cross-referenced against policy information to narrow down a search for potential beneficiaries. Once the name of a beneficiary is known, a comprehensive location database can be used to identify the most recent address and phone number on record for a beneficiary at a fraction of the cost of repeated returned mailings from non-existent or outdated addresses.
4.Insurance companies struggle to link customers across the enterprise to drive claims efficiencies and a positive beneficiary experience
Having a thorough understanding of data across the enterprise can drive significant improvements to the beneficiary experience. Many policyholders own multiple products across business lines. A holistic claims process will help to identify these policyholders and create opportunities for common claims efficiencies across the enterprise. This process empowers the beneficiary to easily work through the claims process without the need to provide duplicative information and complete separate claims forms across products and businesses.
In addition, identifying a unique customer across the enterprise enables a more complete understanding of that customer’s product mix, financial assets, and potential unmet product needs. This holistic view can also make it easier to understand and cater to beneficiaries and retain business across generations while also ensuring a superior experience for both the customer and the beneficiary.