Master Address Data Management with Building-Based Geocoding

Master data management is the future of insurance

Developing a gold standard for property intelligence is top of mind for today’s insurance companies. With mission-critical workflows like underwriting, claims management, and property analytics beginning with an address, getting this foundational information right is essential to the integrity of an insurance database.

Today’s insurers face two major challenges when it comes to address data. First, addressing is a science, and mastering address data management is complex. Many addresses have aliases, represent multiple buildings, or coexist on the same parcel of land - relationships that are difficult to reflect in a database. Second, many insurance companies have disparate sources of address data throughout their various departments which can lead to operational inefficiencies and inaccurate analytics.

Both of these challenges impact an insurer’s analysis of a property and resulting policy prices, response to claims, and internal strategizing. Ultimately, if bad address data is the foundation of insurance analytics, results cannot be trusted for strategic decision-making.

Ecopia’s Building-Based Geocoding provides accurate, rooftop-level geocodes for over 270M primary and secondary addresses across the US, more than 176M high-precision building footprints, and a system of unique and persistent identifiers to reflect how addresses, building footprints, and parcels are all related. The resulting dataset is the first and only complete map of buildings in the US, and serves as the gold standard for insurance companies as they grow and maintain their book of business.

When implemented across entire organizations, Building-Based Geocoding and its built-in master data management provides insurers the accurate, comprehensive, and up-to-date property information needed for the foundation of precise analytics. With this digital source of truth for the physical world, insurers are eliminating data redundancies in their organizations, improving their bottom line with accurate analytics, and avoiding the geospatial fatigue many P&C carriers are experiencing today.

The significance of addressing in insurance

As the insurance industry becomes increasingly data-driven, property and casualty (P&C) insurers are relying more and more on address-based property analytics to conduct and grow their business. Insurance workflows from underwriting to claims management to internal analytics and everything in between typically begin with a geocoded address of a property. Once an address is located, insurers can layer on additional information related to natural hazards, property attributes, or proximity to other buildings as they assess and determine risk profiles.

However, if the foundational address information is inaccurate, incomplete, or stale, any of the resulting analytics will be skewed. Risk profiles derived from an incorrect, duplicate, or generalized address location can lead to poor decision-making across departments, and ultimately impact an insurer’s bottom line.

In this blog, we explain the complexities of address science, the potential impacts to insurance workflows, and how master data management can provide insurers with a gold standard for property intelligence.

The Science of Addressing

Complexities and challenges every insurer knows

Addresses are complex, and mastering them is a science. Many companies exist purely to provide address matching, verification, standardization, or cleansing services to insurers who need a reliable address database for their book of business. However, many insurers still experience geospatial fatigue from trying to keep up with all of the data science needed to accurately reflect real-world address relationships, and many struggle with data redundancies across their different departments. While different countries have their own addressing systems, each with their own unique characteristics and associated challenges, insurers in the US often encounter the following complexities as they manage underwriting, claims, and analytics across their portfolios.

Address aliases

One frequent confusion with US address data is the presence of alias addresses. This refers to when multiple addresses exist for one specific location. While they may appear to be different address strings in a database or book of business, they actually relate back to the same property. If this is not represented in an address database, different departments could be performing analytics incorrectly.

Address aliases exist for a few reasons. A common example is when a county road also has a street name. In the case below, 471 NJ-124, Chatham, NJ 07928, and 471 Main Street, Chatham, NJ 07928 are the same address. State Route 124 may have other street names in different municipalities, but it is also known as Main Street for the stretch where this property is located.

Similarly, some properties could have multiple acceptable city names. This can happen when it is part of a zip code belonging predominantly to one municipality, but actually sits in the neighboring municipality.

Another reason for address aliases is the existence of vanity addresses. Vanity addresses are sometimes used to name a street after an influential person, significant event, or nearby landmark. Vanity cities also exist; a common example is Hollywood, California, which is actually a vanity address for part of Los Angeles.

A single address can also have multiple zip codes. This is often the case if an address is in a large complex or multi-story building that contains multiple secondary addresses. Many skyscrapers in Manhattan, New York have their own zip codes for this reason, even though they are located inside traditional zip codes.

While one street name, city name, or zip code is always preferred by the USPS, geocoders and property databases should be able to recognize all options so insurers can properly analyze their portfolios.

Address abbreviations

The integrity of address databases can also be impacted by abbreviations. While humans reading an address containing abbreviations can interpret that it is indicating the same location as its longer official address, databases often cannot. This can result in multiple address records for a single location, potentially skewing insurance quotes and claims.

Secondary addresses

There are situations where multiple addresses exist at the same location. This is common in apartment complexes, office suites, or campus-style places. In these cases, each individual unit has the same primary address, such as 123 Main Street, followed by a unique secondary address, like Apartment 10E. Distinguishing between primary and secondary addresses in a database is critical to make sure their relationship is reflected in any insurance analytics, but also that any unique characteristics of individual units are retained.

Multiple buildings

A single address can also represent multiple buildings, and this relationship should also be represented in address data. For example, a residential property may have a house, detached garage, and barn all on the same parcel. While the three buildings do not have separate addresses, an insurance address database needs to account for all of them in order to provide insight for underwriting, claims management, and other analytics.

With all of these complexities, it’s no surprise that many insurance companies experience challenges with address data. Developing a single system of record for each property that reflects all of these relationships is critical to the integrity of underwriting, claims management, and other key workflows, but extremely difficult to do. As a result, today’s P&C carriers are often experiencing geospatial fatigue as they work to manage their databases and eliminate data redundancies across their various departments.

Addressing the Issue

Implications of bad address data

The many complexities of address data mean there are also a variety of ways incorrect data can impact analytics, especially in insurance. Because an address serves as the foundation of property intelligence data, poor-quality addresses used as an input for insurance analytics can lead to inaccurate results and negative impacts to the bottom line.

Additionally, the advanced data science required to manage data redundancies and inaccuracies can leave many organizations feeling like there is no solution to the problem, despite all of the effort expended trying new geospatial technologies.

Underwriting with low-quality address data

Bad address data can undermine insurance workflows right from the beginning of an insurer’s relationship with a property. When a prospective customer approaches a P&C insurer for a policy quote, they are looking to optimize for the most coverage at the lowest price, and will ultimately choose a provider that offers them the best deal.

To provide a quote, underwriters analyze the property in question to determine its risk profile. But regardless of how experienced the underwriter is or how good the data about the surrounding area is, the integrity of the quotation process can still be undercut by poor address data.

For example, if a prospective customer rents one of many commercial buildings on a single parcel of land, but the address data used by the underwriter does not account for independent buildings, the insurer may provide a quote for the entire parcel that is much higher than what the customer should pay.

Similarly, if the underwriter does not specify which building on that parcel of land is being insured, they may overlook the fact that the exact structure in question is within a flood zone, leading to an underpriced policy and opening the insurance company up to risk.

It’s important to note that when data redundancies exist, some underwriters will analyze duplicate address records to see which will provide a quote at a price the customer is willing to pay, regardless of its accuracy in profiling risk. Without a singular source of truth for underwriting departments to leverage, it is impossible to know which duplicate record is the correct one.

In any of these example scenarios, and in countless other situations where addresses provide critical insight, insurers risk over- or underpricing policies when they rely on poor-quality address information. Inaccurate policies derived from bad address data not only open up insurers to liability and risk but also lead to increased churn as customers turn to the competition to avoid overpriced policies.

Managing claims with disparate data sources

Even after an insurer has established a relationship with a customer, poor-quality address data can have a negative impact. If a customer needs to submit a claim, insurers must again begin with an address to analyze and manage the entire process.

P&C insurers typically have different teams that handle underwriting and claims workflows, and without a company-wide source of truth, sometimes these teams use disparate datasets. When it comes to processing claims, this can be problematic. If a customer submits a claim for their home using an abbreviated or alias address string, or even with a recently updated street name, the claims department may not respond to the claim with the same data used to underwrite the original policy. Additionally, they may investigate the claim by looking at information about the surrounding area of the wrong address and make a judgment call based on data that does not reflect the true situation.

Without a source of truth for property intelligence and a gold standard of addresses, different departments within a P&C insurance company may rely on differing information. Managing claims with address data that is low quality or inconsistent with data used in the underwriting process can lead to inaccurate analysis, incorrect payouts, unhappy customers, and wrongly assumed risk by the insurer.

Performing property analytics without a solid foundation

P&C insurance companies perform a wide variety of property analytics using address data beyond underwriting and claims management. Using bad address information in these workflows has similar implications for the integrity of property intelligence and resulting business decisions.

As insurers develop plans for growing their book of business, minimizing their risk, and optimizing their reinsurance strategy, they rely on detailed property analytics to reveal critical information. For example, insurers frequently analyze all properties within their book of business to quantify how much risk they are open to and how that evolves over time in a dynamically changing world. If the address data used as the foundation of this analysis is stale, incorrect, contains duplicates, or does not locate properties in the correct place, any resulting conclusions cannot be trusted to inform business strategies. Without a reliable address database, insurers could be grossly over- or underestimating their risk portfolios.

Inaccurate property analytics resulting from poor-quality address data can also weaken reinsurance strategies. Many P&C insurance companies take their own insurance policies out on properties in their book of business to minimize their own risk. Each insurer develops its own strategy for choosing which properties to reinsure and usually does so by deriving property intelligence from address data. Bad address information as an input to this analysis means reinsurance strategies are not built on a strong foundation, and insurers are vulnerable to more risk than they realize.

These implications of bad address data not only impact an insurer’s bottom line but also contribute to a feeling of overall geospatial fatigue as carriers try to keep up with the latest trends in technology claiming to solve for data redundancies. The reality is, that many address solutions on the market do not alleviate the challenges of data redundancies and inaccuracies, and most carriers are still looking for a single source of truth to rely on.

Building a Solid Foundation

How to achieve the gold standard in property data

Any of the previously mentioned address complexities and possible impacts to underwriting, claims, and analytics can be remedied by a single source of truth for property intelligence. A unified address database leveraged across departments within an insurance company prevents inconsistent risk calculations and costly errors in decision-making. But even if an insurer uses the same database across its underwriting, claims, and analytics departments, incorrect address information can undermine the foundation of property intelligence. The best way to manage address data and subsequent property analytics is by assigning a unique and persistent identifier to each address that can be used for master data management.

Much, in the same way, an employee identification number (EIN) or social security number (SSN) is a unique and persistent identifier for an individual regardless of name, address, or other personal data changes, an address identifier serves to link multiple records of the same address together and simplify analysis. Unique and persistent identifiers are critical tools for address data management as they represent all of the possible forms of an address and remain constant as addresses and properties change. This allows insurers to maintain one source of truth for each property in their database, and eliminate duplicate or stale records.

Ecopia’s Building-Based Geocoding solution provides unique and persistent identifiers for each of the 270M+ primary and secondary US addresses included in the database. But while some other data providers deliver a similar offering, what makes Ecopia unique is the inclusion of additional unique identifiers for building footprints, parcels, and multi-unit buildings. This ecosystem of unique identifiers provides an unparalleled level of property intelligence that insurers rely on as their source of truth for underwriting, claims management, and strategic analytics.

Ecopia’s network of unique and persistent identifiers enables insurance carriers to visualize and analyze key spatial relationships in their portfolio. In the illustration above, a single parcel of land contains two building structures: a two-family home and a garage. The geocoded addresses and building footprints representing this property will contain all of the related IDs to indicate the intricacies of this relationship. With Building-Based Geocoding and the spatial relationships represented through a trusted network of unique and persistent identifiers, insurers can be confident that their various departments are analyzing each property with a comprehensive, accurate, and up-to-date view of its risk profile, and driving strategic decision-making based on a unified source of truth.

This gold standard of property data not only leads to more accurate analysis, lower risk to the company, and higher customer satisfaction but also more streamlined master data management that can save valuable time and resources across departments.

The advantage of Ecopia's Building-Based Geocoding

Addresses can be complicated, and insurers often struggle to maintain a reliable database of geocoded address strings they can leverage for underwriting, claims, and property analytics. Without a source of truth for address locations and spatial relationships, P&C insurers open their businesses up to additional risk, increased customer churn, and operational inefficiencies.

Through master data management and a detailed network of unique identifiers, insurers can achieve the gold standard of property intelligence and avoid the fatigue that comes with geospatial data redundancies. Unique and persistent identifiers enable insurers to locate a specific property with confidence and maintain an up-to-date record of its risk profile, eliminating duplicate records and the inaccurate analytics generated by poor quality address data.

When applied across an entire organization, a unified, accurate, comprehensive, and up-to-date address database can streamline workflows and prevent costly mistakes.

The level of detail provided by Ecopia’s Building-Based Geocoding solution allows P&C insurers to understand a property at the level of granularity needed for accurate underwriting, claims management, or internal analytics. Whether an insurer needs to see what other buildings are located on the same parcel, what secondary addresses exist within the same building footprint, or how close a property is to potential hazards, Ecopia’s Building-Based Geocoding provides the gold standard source of truth for confident, strategic decision-making.

To learn more about Ecopia’s Building-Based Geocoding for master data management in insurance, get in touch with our team today.