Making Data Work for Your Geospatial Challenges

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Anyone who has looked at flood data knows that FEMA is not the answer. It's not that FEMA doesn't try to accurately map what they believe to be floodable areas. It's that FEMA is politically driven. As a city, you do not have to participate in FEMA mapping. Why would you not participate? Well, properties in flood zones tend to have lower valuations, and lower valuations tend to generate lower taxes. Even as an individual you can exempt yourself with a LOMA. A LOMA establishes a property's location in relation to the Special Flood Hazard Area (SFHA).  LOMAs are usually issued because a property has been inadvertently mapped as being in the floodplain, but it is actually on natural high ground above the base flood elevation.

Melissa customer wanted to sell flood insurance to prospects that were in flood zones but not likely to flood and not in flood zones but likely to flood. They had three primary targets, properties in 100 year flood zones that were not likely to flood, properties in a 500 year flood zone that were not likely to flood, and properties in FEMA minimal risk zones that were likely to flood. The customer realized that they needed a way to understand the current FEMA designation for the target properties but also have an independent flood likelihood evaluation of the property. For them, we created a sample set of customers utilizing HazardHub risk data that looked something like this:

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Then they created a targeted list by selecting B and C score prospects from the 100 year and 500 year flood plains and D and F score prospects from the minimal risk flood zones. This scoring and these selections are available nationwide and provided the customer with the ability to selectively target the types of customers that they were interested in from a risk exposure perspective. While this example discusses flood, this works for any natural hazard where properties are exposed, both personal property as well as commercial property.

If you have data challenges to solve, perhaps the Melissa team can offer the location intelligence solution needed. Melissa supports geospatial professionals in the goal of mapping innovation in location-based services, analytics and decision-making powered by location intelligence. We provide a wealth of location data enrichments including global geocoding to derive latitude and longitude from an address, and geo-enriched data for IP addresses. We offer other types of specialized data including U.S. property and mortgage data (type and number of buildings on a parcel, property age, construction, sales value, and more), demographics (household income, marital status, residence data, credit information, and more), and risk and natural hazard information (wind, water, ground and wildfire) that can be linked to location data to reveal relationships and trends. Our data feeds easily into popular data visualization and analytics platforms for ease of use and up-to-the minute accuracy. When you need to solve global challenges with geospatial technology, turn to Melissa - your single, trusted source for authoritative reference data. 

Written for Directions Magazine - Header Image via @directionsmag

 

The Seven Things Most Insurers Don't Know About Hazard Data

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Unless you spend everyday thinking about, creating, and updating hazard data there's probably a lot you don't know about it.  We were asked what we thought the seven top things that insurers didn't know about hazard data were.  Here's our list:

 

1)     There's a cost to not using the data.  Most hazard data is sourced on a per transaction basis or on annual license based on anticipated transaction volume.  In other words, data costs money.  But that cost is minimal compared to taking on unnecessary risk or underpricing a policy.  Very few underwriting departments examine the cost of making a bad decision to save a dollar or two on hazard data.  The thought process is almost always, what's this report going to cost?  Not, what's the cost of writing a bad policy?

 

2)     Hazard data changes.  The hazard data that you used last year to quote and bind a policy may not be the same this year.  Hazard databases are evolutionary not static.  Some change annually, some quarterly, and some even monthly.  Events that happen or didn't happen this month, quarter, year, will affect the update of the hazard data and can change the scores of your models.  For example, a wildfire will take a high risk area to a low risk area literally overnight.  It may take three to five years or longer for the vegetation to grow back to make it a high scoring area again.

 

3)     The more hazard data the better.  Typically companies only look at the hazard variables they are interested in.  And that makes perfect sense.  It's also a little short sighted.  By acquiring hazard data beyond the basic needs an insurance company can start building up a history of scores associated with a property and find out if other variables are predictive for them and understanding the likelihood of a claim being filed.    

 

4)     Hazard data should be used to educate the consumer.  You may recall the Farmer's commercials that showed how to prevent claims.  They were humorously teaching consumers how to mitigate risk.  Underwriting and Claims should be leading the charge on mitigation.  Hazard data reports provided to the consumer can go a long way in educating the policyholder and reducing claims.  As odd as this may seem, very few consumers know their true hazard exposure.

 

5)     Exposure analysis and underwriting should work hand in hand.  Some people look at the big picture, some people look at the small picture.  Macro versus micro.  But the underlying hazard data to look at both pictures should be the same.  When you have differences in data, or versions of data, different departments can be sending different messages throughout the company.

 

6)     Using hazard data does not have to be hard.  Through the use of LandVision Insurance and APIs, as well as geospatial files, data delivery can be tailored to meet your specific needs.  Companies can also vary their hazard data delivery by department, with some departments receiving geospatial files, some reports, and some the API.  The easier it is to us hazard data the more likely it is to get used.

 

7)     The more you use hazard data the better your loss ratios become.  Knowledge is power according to Sir Francis Bacon.  And the more you know, the more you know when it comes to accurately writing, quoting and binding a policy.  Greater knowledge at the beginning of the policy process equates to better policies written which correlates to better loss ratios.

 

If you came up with some other answers, please send them our way.  Clearly there are more than seven things that aren't typically known about hazard data, but we stopped at seven.  But without getting into all of the technical details of sourcing, update schedules, modeling techniques, etc., the key point is, if you have access to hazard data, use it.  If you don't have access, get it.  And that the more data you have the better your decisions will be.

 

Oracle Validated Integration Offers Oracle Customers Rapid Access to Clean, Accurate Address and Contact Data


Rancho Santa Margarita, CALIF - November 14, 2017 - Melissa, a leading provider of global contact data quality and identity verification solutions, today expanded its partnership with Runner Technologies (Runner EDQ), a leading provider of integrated address verification solutions and Platinum level member of the Oracle Partner Network (OPN). Together the two firms deliver CLEAN_Address®, an Oracle-validated, integrated address verification solution for PeopleSoft Enterprise, JD Edwards EnterpriseOne, and E-Business Suite platforms.


To achieve Oracle Validated Integration, Oracle partners are required to meet a stringent set of requirements that are based on the needs and priorities of Oracle customers. With this integration, CLEAN_Address® can meet the needs of users to capture and maintain accurate address and contact data in a broad spectrum of markets, including healthcare, finance, travel and transportation, government, and higher education. Joint customers include Simon & Shuster, McKesson Corp., and the U.S. Small Business Administration.


"Today's data-driven enterprise demands enterprise-grade data quality infrastructure - this is essential for successful master data management, data integration, and data aggregation," said Bud Walker, vice president, enterprise sales and strategy, Melissa. "The collaboration between Melissa and Runner EDQ meets this need with the integration of postal address verification, email verification, and other data enhancements, ideal for systems such as human capital management, student information, financial, vendor management systems, and CRM platforms."


"Achieving Oracle Validated Integration gives all our customers the confidence that integration between Clean_Address and Oracle is functionally sound and performs as tested," said Greg Marinello, CEO, Runner EDQ. "By combining efforts and capitalizing on Melissa's global toolset and authoritative reference datasets, we're empowering Oracle users to directly engage with data quality, enabling them to cleanse, update, and enrich their existing and incoming data throughout the data lifecycle."


CLEAN_Address also includes an API for custom development, allowing users to create business rules that enforce individual data quality standards. Click here for detailed information on CLEAN_Address or to access a free trial. To connect with members of Melissa's global intelligence team, visit www.melissa.com or call 1-800-MELISSA.

Tis the Season... to Check the 2017 Mail-By Dates

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The season of giving (and, of course, checking the 2017 mail-by dates) is here! To have your gifts and holiday cards delivered by Dec. 25, we recommend you mail by the dates below*:

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*Dates are based on USPS.COM Holiday Shipping Dates

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Exact numbers for the devastation caused by Hurricane Harvey won't be in for quite some time, but the storm surge damage estimates are nothing short of extraordinary. Melissa's partner, HazardHub, the only third-generation provider of property-level hazard risk databases spanning the most dangerous perils in the continental United States, released a shocking calculation before the 2017 storm ever reached Texas. 

 

HazardHub predicted that risk from the storm, depending on the hurricane's Category ranking, could be double that of its competitor's predictions. HazardHub warned that 367,000 homes could be affected if Harvey became a Category 4, and that $77 billion of property was at risk. A report from the balance, after Hurricane Harvey hit, stated that total repairs will cost $180 billion and that 203,000 homes were damaged, thus far. Competitive estimates only indicated $30 billion in property damage and between $48 and $75 billion in total losses. 

 

Although estimates for the number of homes at risk were higher than actuality, HazardHub aired on the side of caution and in the spirit of being best prepared for storm surge risk - as the company believes that everyone who owns a home or business should know the risk around their property and prepare as best they can.   

 

Bob Frady, CEO of HazardHub, described it best, "For too many years, people have been caught by surprise by storm surges that caused massive damage to homes and businesses. We want to help eliminate that surprise by providing cutting-edge tools that help to estimate risk from - and allow people to prepare more fully for - storm surge."

 

HazardHub converts very large, very complicated data sets of long-run hazard event data into risk grades for natural disasters such as flood, wildfire, lightning, hurricanes, tornadoes, earthquakes and more, for every home in the contiguous U.S. You can check an address or latitude/longitude point, one at a time or in batch, to get detailed hazard reports on an entire mailing list, customer file, area or region.

 

Use HazardHub To:

 

·         See detailed reports of natural hazard risks in any area

·         Reach out to potential customers who could use targeted insurance services

·         Pinpoint exact hazards at the address level for granular risk assessment

·         Give the most personalized insurance quotes by knowing which services your customers need

·         Determine which customers can best use services for mitigation, new roofs or storm preparedness

 

Comprehensive, accurate risk data analysis of home and business natural hazard risks help people better prepare for natural disasters, understand what services they need or don't need, and enables individuals to make better real-world decisions.

 

To learn more, check out HazardHub Web Service at www.melissa.com.  

Acquire Property Owner Info in Under a Minute

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Looking for instant insight? Curious about a property or property owner? Get all the data you need, on any property you want - in less than a minute! The TopConnector App provides unlimited and unrestricted access to data for more than 120 million properties and its owners. And, it's all in one place - your hand.

 

Individuals in real estate, insurance, home renovation and a plethora of other businesses offering services to property owners, are staying one step ahead of their competition with the TopConnector App. Imagine how you or your company would benefit from easy access to information on virtually any property in the US... 

 

Ok, get your head out of the clouds...

 

This is How It Works:

 

1.      In the neighborhood: Tap the "My Location" button to center the map on your current location. Touch any house you see on the map to get the property and owner info - touch the next house, then the next, and so on.

 

2.      In the office: Enter an address in the "Search Field" and tap the "Search" button. The app will show the address location on the map. Start touching properties on the map to get property & owner data.

 

3.      In-the-dark on an address: Center the map manually on the area you want to research. Start touching properties on the map to see all the details.

 

4.      In picture-taking mode: From the location, take a picture of whatever you'd like - a house, tree or your shoes! Then use the "Find Photo's Location" feature to show the photo's location on the map. Start touching properties you see on the map to get particulars. 

 

TopConnector can be downloaded in the App Store at http://topconnector.us or for more information, visit topconnectorapp.com.

 

 

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By Kevin Ubay-Ubay, Sales Engineer


Personator World is Melissa's new powerful cloud based web service that gives a simple and single way to clean and verify your contact data globally. This service leverages our experience in name, phone, address & email validation to provide comprehensive contact checking and combines high quality identity level verification.

Some examples of where our new web service can be applied:

·         Age verification

·         Name-address verification

·         Anti-fraud applications

·         Online shopping cart & eCommerce platforms

·         FinTech/Banking

Trusted Reference Data

Personator World uses a number of trusted reference data to verify the identity of an individual. These types of data sources include:

·         Citizen/national databases

·         Credit agency/bureau

·         Utility and telecom sources

·         Driver's licenses

·         Electoral rolls

Personator World can then determine if the identity has been found and matched against those datasources. An example JSON response from the web service may contain something like this:

{

    "DatasourceName": "CREDIT-2",

    "Results": "KV03,KV04",

    "Messages": [

        {

            "ResultCode": "KV03",

            "Description": "First/given/forename matched"

        },

        {

            "ResultCode": "KV04",

            "Description": "Last/surname matched"

        }

    ]

},

{

    "DatasourceName": "CONSUMER-1",

    "Results": "KV01,KV14,KV13,KV12,KV10",

    "Messages": [

        {

            "ResultCode": "KV01",

            "Description": "Address matched"

        },

        {

            "ResultCode": "KV14",

            "Description": "Premise/house number matched"

        },

        {

            "ResultCode": "KV13",

            "Description": "Thoroughfare matched"

        },

        {

            "ResultCode": "KV12",

            "Description": "Locality matched"

        },

        {

            "ResultCode": "KV10",

            "Description": "Postal code matched"

        }

    ]

}


As you can see here, datasources where a match has been found are listed as well as what components of the input had been matched.

Using Personator World

To give a walkthrough of this service, when you send your contact data that you want checked and verified, Personator World starts off by standardizing and validating your data. At this stage, the service will parse and standardize your data as well as check for the following:

·         Name is in a valid format

·         Address is deliverable

·         Email address exists

·         Phone number is callable

Input

Output

Full name: John doe

Phone: 8458692102

Email: john.doe@yaoo.com

Address Line 1: 1 unicorn

City: norwich

Administrative Area:

Postal Code: NR33AB

Country: GB

Full name: John Doe

Phone: +44 8458692102

Email: john.doe@yahoo.com

Address line 1: 1 Unicorn Rd

City: Norwich

Administrative Area: Norfolk

Postal Code: NR3 3AB

Country: GB

 

In the diagram above, you can see the service standardizing (changing casings, abbreviations, etc.) and making corrections to our input data - add in missing street suffixes, add in phone country dialing code, add in missing state/province/administrative area info, correcting typographical email domain errors (yaoo.com -> yahoo.com), etc.

Next, Personator World takes your data and compares it against trusted reference data in order to verify that individual's information.

In this verification stage, the web service takes the standardized name, phone, email and address from the previous checking stage and performs ID verification. Additional input such as the individuals national ID provisioned by the country's government and date of birth can be verified as well, depending on the country.

 

Input

Output

Full name: John Doe

National ID: HJDO840230HVZRRL05

Date of birth: 2/30/1984

Phone: +56-222-226-8000

Email: john.doe@yahoo.com

Address Line 1: Paseo De Los Conquistadores 2000

City: Guadalupe

Administrative Area: NL

Postal Code: 67170

Country: MX

Results:

 

·         KV01 - Address matched

·         KV02 - National ID matched

·         KV03 - First name matched

·         KV04 - Last name matched

·         KV05 - Phone number matched

·         KV06 - Email matched

·         KV07 - Date of birth matched

 

 

 

The diagram above shows how the service will return back result codes indicating which pieces of information got matched. By observing what kinds of result codes are returned, you can determine how reliable your data is.

Availability

Personator World is currently available as a web service hosted on our servers - meaning you won't have to worry about updates and maintenance. Information about the countries supported and the level of coverage can be found by visiting our online wiki at wiki.melissadata.com. Sample code and documentation is also provided to help you get jump started on building applications and services incorporating our ID verification technology.

 

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Hindsight is, as we know, 20/20.  But, what if, when it came to natural hazards and their impact on your portfolio, you didn't have to rely on hindsight?  What if, you could understand your risks and exposures before an event occurred and write and plan appropriately?  Wouldn't that be better than hoping and praying that nothing bad happened?

 

Of course it would.  And we all know that bad things still happen no matter how much hoping and praying goes on.  That said, how many companies truly use hazard data in their everyday processes?  Sadly, very few.  For a large number of insurers either cost or technology or both keeps them from making use of some of the most effective data for pricing a policy, validating claims, and knowing your true exposure.

 

This probably isn't news that extensive property level data can enable a great level of risk awareness and location intelligence which in turn reduces risk for insurers while improving customer communications and service.  But what is news is that this risk information is now more available and easily accessible than ever before, enabling more insurers to take advantage of it.

 

When geospatial data first arrived it was primarily used by the big insurers who could afford to have a geographic information systems (GIS) team to develop or purchase the information, integrate the information, build decision models and monitor usage.  But thanks to technologies companies can now get a hazard answer for just about any hazard easily through Data as a Service (DaaS).

 

With DaaS you get answers to the hazard questions you have, when you have them.  For example, at time of quote: What is this policy exposed to?  How far is it from a fire station?  What's the distance to coast?  What rating territory or premium tax area is it in?  Knowing this information at the time of quote saves the embarrassment of an initial rate quote moving dramatically upwards if this information is only used at time of binding. 

 

Do you understand the hazards your portfolio is truly exposed to?  For example, knowing if properties are in a flood, surge, fire or earthquake zone can support a detailed plan for prospects your business can reach.  You may not insure for flood or earthquake, but knowing the risk exposure to those hazards and your ability to educate your prospective customer on them can separate you from your competitors.  While this has always been the case for the use of hazard data, the difference today is that hazard data is now a real-time asset, helping insurers make internal risk assessments as part of policy services.

 

Hazard information also allows you to better educate your customers.  Most customers do not know what hazards their property, personal or commercial, is exposed to.  A neatly formatted on-line report provided by your company using hazard data can help your customers better mitigate for risk.  As an example: This address in New Smyrna Beach, FL.


Flood Risk

Covered by FEMA digital maps. Minimal Risk of Flooding                            B

Fire Protection Class

Unprotected                                                                                                  D

Wildfire Risk

Very High                                                                                                       F

Drought Risk

Abnormally Dry - Increases the risk of wildfire at this location                     C

Earthquake Damage Risk

No Damage                                                                                                   A

Hurricane Damage

High Property Damage                                                                                 D

Superfund Site Risk

> 2,500 Feet from Known Superfund Site                                                     A

Brownfield Site Risk

> 500 Feet from Known Brownfield Site                                                       A

Florida Sinkhole Risk

Limited Sinkhole Risk                                                                                    B

Straight Line Wind Risk

Very High                                                                                                      D

2" Hail Risk

Very High                                                                                                      D

Tornado Risk

High                                                                                                              C

Lightning Risk

Very High                                                                                                      D

Special Wind Regions

NOAA Hurricane Prone Wind Region: Risk varies with location                   C

Florida Wind Born Debris Zone

130 MPH Wind Speed Zone                                                                          F

Thunderstorms

Very High                                                                                                      D


In looking at this property an insurer can instantly tell that there will be insurability issues.  If this property were already a part of your book of business there are any number of steps you would want to encourage this policyholder to take.  Unprotected for fire - fire extinguishers.  High risk of wildfire - mitigation steps.  Wind borne debris - shutters.  Impacts of convective storms and hurricanes - roof ties, mitigation information, lightning rods.  On the plus side there's no storm surge risk and minimal risk from flood.  Knowing that it's unlikely that there will be a water claim.

By accessing geographic risk-sets in real-time, natural hazard data such as wind, fire, water, or earthquake risk can be easily associated with specific properties.  Ideally, this is coupled with property and mortgage data enhancements, as well as location intelligence toolsets, such as address verification, geocoding and reverse geocoding, and IP location.  Based on multi-sourced datasets, insurers have real-time access to hundreds of different metrics on individual residential or commercial properties.  Hazard data scores are based on information from sources such as FEMA, NOAA, USGS, and state and local governments, and include risk of flooding, wildfire, lightning strikes, straight-line winds, hurricanes, tornadoes, earthquakes, and more.  It's a higher level of insight for insurance professionals, making decisions based on end-to-end property intelligence supported by precise risk scores made easier through hazard DaaS.

 

 

 

Tips & Tricks for Global MatchUp Matching Strategies

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by Tim Sidor, Data Quality Analyst

In the past we've discussed implementing different matching strategies based on how you would like your records grouped. For example. By "Address"? or by "Name and Address". The former would match 'John' and 'Mary Smith' at the same household, whereas the latter would identify them as unique entities.


For Global processing, even after determining and selecting a general strategy, 'Address' for example, it might still require knowing the expected address formats of the source data that needs to be compared and thus reevaluate the logic.

 

At first glance, a 'Global Address' matchcode might appear to be a safe accurate matching strategy...

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But knowing that some countries don't have a reliable Postal Code, which is usually the component MatchUp uses for efficient 'neighborhooding' (also known as 'grouping' or 'clustering'), how can we accurately match these records? Simply removing the Postal Code component would incorrectly match similar addresses that were in different parts of the country.

 

US & Canada users are so used to using the reliable Postal Code that we rarely use City (Locality). But for processing countries without Postal Codes, or databases with multiple countries, adding a Locality can bring back accuracy and efficient clustering.

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Configuring this matchcode to allow 'blank matching' on the Postal Code will accurately match records for most worldwide addresses and is a default distributed matchcode.

 

However, many countries distinguish addresses by also using a different hierarchy structure which may include a combination of Dependent Locality, Administrative Area and or Sub Administrative area. Or the use a Dependent Thoroughfare to distinguish the delivery address. So knowing the primary data types used in a countries standard address can help you decide the proper matchcode components to include in your matchcode.

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How do I know how to construct a good matchcode for specific region processing? Our 'Global Address, Locality' matchcode is a good basic strategy, but using Melissa's resources - such as Global Verification documentation and or actual record processing and parsing can help you determine the necessary components to construct a matchcode to produce accurate results.

Verify National ID like SSN with Personator

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by Michael Johnson, Sales Engineer

Personator has never yielded the crown of contact verification, and that is no accident.  Personator is the quintessential contact verification service.  Nowhere else can you find a fast, no maintenance, easy-to-use web service that will give you a level of certainty your data is not only good, but that it is correct.  One of our newest features in our flagship service to be released is Personators SSN Verify.

Personator SSN Verify adds an integral feature of contact verification to Personator that had not existed before: the ability to verify that an individuals' SSN belongs to them.  Personator now has the ability to take in a social security number and full name and tell you whether or not they match.  We chose not to stop there, but we give you the power to dictate how much of your SSN and name to verify.  Do you want to know if your last name was correct to the SSN? 

What about the correct full name to last 4 SSN?  Our newest option, SSNCascade, allows you to determine whether or not we attempt to drill down and verify multiple segments of your social security number and name combination.

·         Full Name and Social Security

·         Last Name and Social Security

·         Full Name Last 4 Social Security

·         Last Name Last 4 Social Security

We don't just stop at verifying the social security number and name; we crank up the volume to 11.  If the SSN and Name are verified as a match, we allow you to have the option to append information regarding that person.  We are able to append the address, phone, email, demographic data, and census data all regarding the matched individual.

Personator is our flagship product which leads the industry of contact verification. You will be hard pressed to find another single product with so much potential.  We continually add new features and sources to our arsenal, which not only gives the end user more options but will give them more confidence that their data is correct.

 

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