Every Name Is Common

Didn’t You Promise 52 posts?

When we last met, the summer solstice had not happened. No presidential candidate had been shot. I too, have been busy period since that May 14 post. In the spirit of getting something, anything, back into the Opensouceresearchsphere, I’m going to address a frequent gripe: every name is common.

We Found No Material, Adverse Public Records

That statement leads many memorandums I write. But squaring that with the time I bill to get to the point can be a bumpy road.

Because the bulk of research work – which accounts for most of my hours, is not visible in the memos, and the bulk of what is visible often shows up simply as “name match only,” findings included because you cannot, not include them, you just do not know. Because every name is common. And some names are more common than that. The time factor in most cases goes to figuring out why it’s not your guy or your gal, or in the alternative, putting a lot of things on paper that you know hardly matter. Try doing public record research in India. Billions of people, all with the same name. Or a recent matter that took up a lot of my July, researching Hispanic names in Southern California. These cases required hours of searching and yielded few results.

I will digress momentarily and point out that one of the fundamental frustrations with doing background research is that you tend to be judged by your findings. If you research someone who’s led an exemplary, low profile life and end up with nothing to report, a client may consider your work to be meh. Get an extravagant fraudster and you’re Sherlock freakin’ Holmes. Addressing the common name problem is unavoidable. Within the morass of Kumars and Mohammads and McSorelys, you must find the right record. To avoid being meh,

There are several ways to deal with common names. You may need to try multiple methods to answer the “is this my guy” question. To jump to the conclusion, more often than we would like, we find ourselves needing to report results even when we may doubt elements of what we are saying because we cannot resolve the common name. In recent research the subject was currently married to someone, we’ll say, Smith, and previously someone with the subject’s name was divorced from someone also named Smith. Highly unlikely to be the same person, right, but there is nothing in that beyond the realm, Occam’s Razor, and such, that someone could be married to two different people with the same surname. Until we get there, here’s what we do to fight the common name problem:

•        All the Name

•        Someone Else’s Name (or Location)

•        It’s Not Just the Name

•        Yeah, Big Data

•        Name Match Only

All the Name

The first way we try to distinguish between first and last names is to use what’s in between. Most people have middle names and with middle names there are middle initials. Moreover, in many cultures someone’s name includes their mother’s and their father’s names. This should be enough. Except it often isn't.

Many public records do not include a middle name or middle initial, at least not in the search indices. Those full mother/father names, that’s not how it’s presented in public records. For instance, I recently did some research on several high profile Middle-Eastern people. I had full names, bin this and bindt that (son of, daughter of), but in my online databases, most of those full names were not included. Also, databases sometimes screw things up. A Spanish or Portuguese name should look like this: Juan Maria Garcia Garza. Where Garza is the last name, right? It may be the last name listed in the string of names, but it’s not actually the surname or legal name which, in this situation, is Garcia. Online public records may show this as Juan Garcia or Juan Garza. Having the full name, including mother’s and father’s names or a middle name can help. It is the best and easiest way to run through common names. It works. Except when it doesn’t because of the way online databases work.

Someone Else’s Name (or Location)

Another easy way to cross-reference potential findings is to match them against known information. This happens frequently in that we usually focus our searches on the places a person has lived or worked. We live in a mobile country. This is true in the sense that Americans are more willing to leave their ancestral homes and try someplace new, and it is true that most Americans have cars and can get around. It means that again, Occam’s Razor, that the lawsuit we found that was filed 2,000 miles from your home could still pertain to you. We just assume in most matters that we will limit our research to where the person lived or worked. A public record search of federal court records (PACER) may produce many results, but if we limit the search to the area where the research subject lives, there may be few or no results.  For state litigation, we are going to mostly start and finish in the county where the person lives; sometimes we add, and works, and sometimes, we use the whole metropolitan region—e.g, for Chicago, one may include Cook and Lake and DuPage in their scope, but with common names, each expanse throws in more names. To avoid common names, limit your places.

Another way to sort out findings, is to use other family members’ names. This presupposes we have the husband’s or wife’s or kid’s name. If we do, it can often help eliminate false positive records. Like if both parents were sued. Sometimes we can learn these other names: who’s reported living at an address, what names are on a deed or mortgage, various minor news pieces like wedding announcements and obituaries, and social media. Once we identify these relationships, we have other ways to cross-reference potential findings and eliminate the false positives. Again, having a spouse’s name can be of limited benefit if the record you are staring at if it does not include a spouse. Just because you are married doesn’t mean you will both be in the same public records.

It's Not Just the Name

For all the name match only stuff we include, the vast majority of findings are omitted because, at the end of the day, they do not matter. Traffic tickets, citations for drooping gutters, high school track and field scores, your opinion on the finale of the Big Bang Theory – none of it matters. Findings can be narrowed down by eliminating the fluff and irrelevant, the minor and inconsequential.

We use keywords, negative news screens and other ways to eliminate matters that match the subject’s name but are not vital. And there are other search strategies. I am especially fond of starting with broad lists of media, and then limiting them to specific newspapers or magazines that I think are more likely to contain relevant facts. A couple of other tricks used: limiting your search term (name) to headline or headline plus lead paragraph or limiting your results to articles with a certain minimum word count. These are ways to get to articles that deal with important things rather than just mentioning a name in passing. Again, it doesn't always work—and I’m already on record that I’m negative on negative news.

Yeah, Big Data

The very nature of online searching is Big Data. Do any of you young-ins know how things used to be found when we had no computers? Opening and closing big grantor/grantee books. Deciphering the calligraphy in old docket sheets. Navigating microfiche readers. Those, my friends, were a hell of a skillset. It’s like rowing a canoe, you turn the reader to the right to get to the left of the page. It is amazing, now, how many records can be searched in nanoseconds. But that’s not what I am getting – there are other ways that Big Data helps with the common name conundrum.

Take the matter I mentioned above, Hispanic names in California. I could access county civil and criminal litigation websites, or I could access the same data via a subscription database. What I sacrificed in per name cost, I more than made up in how quickly I could run through the results. With Big Data it is easier to see things: middle names, middle initials, type of case, and other items that help eliminate false positives.

Another way Big Data works is in searching for sanctions/politically exposed persons/watchlists. There are many ways to get this information for free, accessing government websites and free sources. Often, however, those results are a mess. When I do sanctions searching via Big Data, it sorts all this out for me, by country, type of record, full name, etc. My Big Data tools will sort or categorize findings in ways that make it easier to run through, and much easier to see which of the Jessica Cohens is my Jessica Cohen.

Name Match Only

I’ve already mentioned this – name match only. And you will sometimes see this stated in memorandums. Name Match Only means we’re throwing up our hands, we tried to see if these records were or were not the right person, but we could not. Look at all the reasons noted above. The public record did not include a middle name. It did not include the mother’s name and the father’s name. Did not know the spouse name. All the best efforts to weed out the other names did not work. Because almost all names are common, when there is doubt on a name, include it. Likely with additional time and resources, for instance, obtaining and reviewing litigation records, can determine if the name is more than a match only.  Yet, in most cases, that extra time and cost is not budgeted.  We stop with name match only, and start up when directed.

Do not be fooled by the name given to you to research. There is likely to be more than one person who uses it. They may be a kleptomaniac, an aggressive filer of lawsuits, a well-known scientist, a supporter of charities. Our primary job as researchers is not to find those things. It’s knowing which of the things we find, we can report.

If this turns out to be my last blog post in 2024, I am glad it’s addressing the common name problem!