Hmmm, I generate and process csv files in large quantities for work.  I could call this practicing data analysis.

No, really, I'm always looking for ways to make finding outliers in raw data, this may legitimately be a good source of practice data.

So I put in my zip code (town of 12000) and I didn't recognize a single name. So I put in the zip code of the town I delivered the mail to (pop 150) and I didn't recognize any names. I live in a mostly red area (about 98% red) and the majority of the donations were Act Blue. Anyway the donations for the town I delivered the mail in isn't accurate. I can name names (and addresses) of everyone in the town and I didn't recognize a single name

That is the type of stuff we are looking for.

I typed in my home town (where I grew up) and my ex husband donated 3 times to winred and my aunt donated 5 times to Actblue. (She's a liberal). I recognized the names at least. Most were winred (which I have never heard of).

3 and 5 are probably normal real people. We're looking for data outliers.

- person listed who didn't live there
- tons of donations specifically earmarked
- changing amounts of donations (for example, a reoccurring $10 monthly donation to a general fund isn't that strange)

You're going to find tons and tons of normal people doing normal things. That's fine. We're looking for abnormal.

Question for you Whiskers, I put in my area code and noticed quite a large number of donations by one name in particular, so I added the name as a filter.  It's showing 2056 returns for the year to date, and every single one I've looked at say they're earmarked for one fund or another.  Oddly enough, the name is that of a lady who used to be a Judge here in town but not sure if she still is or not.  And I'm not sure if it's even the same lady.  

One thing that really stands out to me is the fact that there are multiple donations per day, every single day.

Here, see if this link works for you.

I imported the data dumps for 2023-2024 into a SQL database and ran a couple quick queries. Each row in the screenshots below has the number of donations for a unique person as best as I can determine.
A sample of the top ActBlue donors by number of donations:
Attached File

A sample of the top WinRed donors:
Attached File


DISCLAIMERS
* The exported data seems to truncate or round down donations so it's hard to sum up a total dollar amount.
* The donations records in the export file doesn't match up with the web portal and it's not really even close. Not sure why at the moment.
* I'm not a professional data scientist or campaign finance expert or anything more than an internet rando.

Whether or not there's anything here, you be the judge.

Methodology below for those who are interested:
Click To View Spoiler

Edit to clean up some things

Looks like at least in NM I can buy the voter rolls, to include addresses?  Might be more accurate than cross checking property records since a lot rent.

This is a job made for AI.

That is a very good one.

WILSON, JACKIEACTBLUETXNOT EMPLOYED07/31/2024$3.00
WHITE, NOMAACTBLUETXNOT EMPLOYED07/31/2024$10.00
WHITE, NOMAACTBLUETXNOT EMPLOYED07/31/2024$1.00
SMITH, JUNEACTBLUETXNOT EMPLOYED07/31/2024$5.00
SMITH, DONNAACTBLUETXNOT EMPLOYED07/31/2024$4.00
SMITH, DONNAACTBLUETXNOT EMPLOYED07/31/2024$25.00
SMITH, DEBACTBLUETXNOT EMPLOYED07/31/2024$7.00
ROANE, PATRICIAACTBLUETXNOT EMPLOYED07/31/2024$4.00
ROANE, PATRICIAACTBLUETXNOT EMPLOYED07/31/2024$25.00
RICHARDSON, VIRGINIA ACTBLUETXNOT EMPLOYED07/31/2024$5.00
MALOY, TERESAACTBLUETXNOT EMPLOYED07/31/2024$25.00
MALLORY, JACKIEACTBLUETXNOT EMPLOYED07/31/2024$2.00
MALLORY, JACKIEACTBLUETXNOT EMPLOYED07/31/2024$5.00
LILLY, CHERIACTBLUETXNOT EMPLOYED07/31/2024$10.00
LILLY, CHERIACTBLUETXNOT EMPLOYED07/31/2024$25.00
LILLY, CHERIACTBLUETXNOT EMPLOYED07/31/2024$25.00
LEE, JOEACTBLUETXNOT EMPLOYED07/31/2024$9.89
LEE, JOEACTBLUETXNOT EMPLOYED07/31/2024$2.00
HOLDER, KARENACTBLUETXNOT EMPLOYED07/31/2024$5.00

delete.....wrong squirrel

That's probably a real person with money.

Think like a criminal. You're trying launder money. Think how the IRS looks at structuring. Basically we're looking at this from an IRS perspective. How would you donate to obscure donations?

You could probably find some first year comp sci kids to do this in AI in like an easy afternoon with a free copy of sql server.

Are you saying that's a good question, or making the observation that's a good example?  Because I've found at least 2 more, and one of them comes back as living in a different zip code on the other side of town when you Google his name.  You want me to share the links?

Humans are better. AI is programmed, humans can spot things only a human would know.

A good example.

This isn't even my brainchild, but I think it has merit. I don't have a very detailed plan yet, I just want to see if others are seeing similar patterns. Keep it to yourself for now, but make a note of it. I need to figure out data collection that doesn't hurt member's privacy amongst a litany of other things.

For now, we're just trying to see irregularities

OK, put me down for having found several.

Yeah, I downloaded the CSV file from my query ....that sheet is more than a little busy. I'm gonna need more computing power to filter and sort this hot mess. I picked an odd one at random, checked their listed employer which is literally 1500 miles away, and they are NOT on the faculty list. Interesting.

This guy "GRISWOLD, EDSON" from DENVER,CO 80231  donated 14,844 times in 2023-2024 and over $240,000.

Pretty good for an unemployed guy.

AI is overrated.  You still have to know how to train it. I play with privately trained models, and most of the strange behavior of our sins are identified by either an analyst asking about strange patterns, or the programmers staring at outputs till we're cross eyed.

The 14k times is more interesting than the fact he listed unemployed.

People give money to things they like, but most people give a large amount (like a thousand dollars to Harris), or donate on a schedule, or hardly ever do.

14k donations is weird

The example I gave above looked to be making 8-10 individual donations per day, every day.  Not strange at all.....

Exactly

OK....I have a guy in a particular zip code, unemployed, and has made 371 individual contributions of sub $10, often multiple times a day. Something smells....


@WhiskersTheCat Is this the kind of data that I should be looking at? The link works.

I recognize SEVERAL individuals on my zip code. Zero surprises. One individual is listed as both retired and unemployed. I'm guessing it's how the person self-reports.

Top 100 contributors to ACTBLUE and HARRIS by number of donations in 2023-2024

cnt = number of donations
tot = Total $

Total PACS 11834
{"pac":"C00401224","nam":"ACTBLUE","cnt":6851904,"tot":977978170,"doners":684332,"avg_donation":143,"rec_per_doner":10}
{"pac":"C00744946","nam":"HARRIS VICTORY FUND","cnt":290806,"tot":352280660,"doners":134607,"avg_donation":1211,"rec_per_doner":2}

People 750103
{"nam":"URBANOWICZ, WENDY","cit":"VANCOUVER","st":"WA","zip":"98668","cnt":13304,"tot":62618}
{"nam":"KESKITALO, CANDACE","cit":"SPRING PARK","st":"MN","zip":"55384","cnt":11684,"tot":63280}
{"nam":"GRISWOLD, EDSON","cit":"DENVER","st":"CO","zip":"80231","cnt":10610,"tot":140070}
{"nam":"DEARMAN, MELINDA","cit":"SAN ANTONIO","st":"TX","zip":"78261","cnt":8311,"tot":35750}
{"nam":"LYONS, CHRISTA LYONS","cit":"ALEXANDRIA","st":"VA","zip":"22314","cnt":8193,"tot":58660}
{"nam":"KIM, SUN HAE","cit":"FLUSHING","st":"NY","zip":"11367","cnt":6782,"tot":13993}
{"nam":"FARR, GERALD","cit":"SAN MARCOS","st":"TX","zip":"78666","cnt":6742,"tot":76899}
{"nam":"MATENOS, AGNES","cit":"PEABODY","st":"MA","zip":"01960","cnt":6128,"tot":35194}
{"nam":"NICHOLLS, ROSALIE","cit":"AUSTIN","st":"TX","zip":"78748","cnt":6086,"tot":65608}
{"nam":"MONDER, STEVEN","cit":"CINCINNATI","st":"OH","zip":"45215","cnt":6078,"tot":114200}
{"nam":"JOHNSON, MARK","cit":"JACKSON","st":"OH","zip":"45640","cnt":5806,"tot":62309}
{"nam":"SCOTT, THOMAS","cit":"ANCHORAGE","st":"AK","zip":"99504","cnt":5586,"tot":44102}
{"nam":"ROAN DENNIS, CAROL ANN","cit":"OAK HARBOR","st":"WA","zip":"98277","cnt":5530,"tot":18494}
{"nam":"NEWMAN, HAROLD","cit":"AVENTURA","st":"FL","zip":"33180","cnt":5328,"tot":63029}
{"nam":"KILPATRIC, GARY","cit":"SANTA FE","st":"NM","zip":"87505","cnt":5293,"tot":35833}
{"nam":"BALDWIN, JEANNE","cit":"MONROEVILLE","st":"NJ","zip":"08343","cnt":5257,"tot":78027}
{"nam":"CAFFREY, DOROTHY","cit":"ELMHURST","st":"IL","zip":"60126","cnt":5027,"tot":41667}
{"nam":"BOYNTON, ELIZABETH","cit":"SCARBOROUGH","st":"ME","zip":"04074","cnt":4982,"tot":27417}
{"nam":"WEISBART, JAN","cit":"LOS ANGELES","st":"CA","zip":"90035","cnt":4912,"tot":42655}
{"nam":"KAM, DINNY","cit":"OCEANSIDE","st":"CA","zip":"92057","cnt":4910,"tot":18660}
{"nam":"MASON, GWYNETH","cit":"OKLAHOMA CITY","st":"OK","zip":"73132","cnt":4908,"tot":20229}
{"nam":"KOSMINSKY, JANE","cit":"NYC","st":"NY","zip":"10023","cnt":4817,"tot":32659}
{"nam":"KING, ALLAN","cit":"PRINCETON","st":"NJ","zip":"08540","cnt":4766,"tot":44400}
{"nam":"MORGAN, JOANNE","cit":"RAVENNA","st":"OH","zip":"44266","cnt":4757,"tot":17022}
{"nam":"WOOLARD, LINDA","cit":"MILAN","st":"MI","zip":"48160","cnt":4748,"tot":27618}
{"nam":"SALATA, SUSAN L.","cit":"BRIDGEPORT","st":"CT","zip":"06604","cnt":4736,"tot":39837}
{"nam":"MAAKE, FISIITETEFA","cit":"PEARL CITY","st":"HI","zip":"96782","cnt":4734,"tot":37916}
{"nam":"HAY, RICHARD","cit":"OAK PARK","st":"IL","zip":"60304","cnt":4702,"tot":11159}
{"nam":"EVIDON, EVE","cit":"EDINA","st":"MN","zip":"55436","cnt":4642,"tot":31853}
{"nam":"KRATZMANN, MONIKA","cit":"BOSTON","st":"MA","zip":"02110","cnt":4516,"tot":14054}
{"nam":"GRAMZA, MARIANNE","cit":"WAKE FOREST","st":"NC","zip":"27587","cnt":4504,"tot":19628}
{"nam":"KIPPUR, SUSAN","cit":"NEW YORK","st":"NY","zip":"10023","cnt":4483,"tot":41313}
{"nam":"BILLINGS, GREGORY","cit":"WASHINGTON","st":"DC","zip":"20011","cnt":4448,"tot":12224}
{"nam":"BURKE, MICHAEL","cit":"REDONDO BEACH","st":"CA","zip":"90278","cnt":4440,"tot":32241}
{"nam":"WELDEN, AMY","cit":"ASHLAND`","st":"OR","zip":"97520","cnt":4436,"tot":22616}
{"nam":"DAVENPORT, ANNE I","cit":"PHILADELPHIA","st":"PA","zip":"19147","cnt":4397,"tot":39107}
{"nam":"WESTERLUND, TRINA","cit":"BELLEVUE","st":"WA","zip":"98004","cnt":4311,"tot":3028}
{"nam":"GOERNER, JON","cit":"DALLAS","st":"TX","zip":"75205","cnt":4300,"tot":13236}
{"nam":"DALY, CAROL","cit":"SUNNYVALE","st":"CA","zip":"94087","cnt":4223,"tot":23090}
{"nam":"UHLER, ROGER","cit":"HOLDERNESS","st":"NH","zip":"03245","cnt":4168,"tot":29277}
{"nam":"SCHWEITZER, MICHELLE","cit":"SEATTLE","st":"WA","zip":"98108","cnt":4052,"tot":26628}
{"nam":"SMITH, KAREN","cit":"BIXBY","st":"OK","zip":"74008","cnt":4051,"tot":69068}
{"nam":"KRIEGER, KARL","cit":"OXFORD","st":"MD","zip":"21654","cnt":3880,"tot":70509}
{"nam":"RODARTE, MARYKAY","cit":"PHELAN","st":"CA","zip":"92371","cnt":3879,"tot":4761}
{"nam":"PUSCHECK, ROBERT E","cit":"YPSILANTI","st":"MI","zip":"48198","cnt":3873,"tot":12026}
{"nam":"ARNOLD, PHILLIP","cit":"HONOLULU","st":"HI","zip":"96926","cnt":3866,"tot":17237}
{"nam":"FALES, WILLIAM","cit":"AMES","st":"IA","zip":"50014","cnt":3793,"tot":9747}
{"nam":"AROSE, ROBERT","cit":"SPRING LAKE","st":"NJ","zip":"07762","cnt":3684,"tot":37704}
{"nam":"DAVIS-ALPERT, LAUREL","cit":"DENVER","st":"CO","zip":"80224","cnt":3655,"tot":10334}
{"nam":"JOOST, DAWN","cit":"TAMPA","st":"FL","zip":"33647","cnt":3624,"tot":35220}
{"nam":"ALVES, DIANE","cit":"PORTLAND","st":"OR","zip":"97215","cnt":3591,"tot":35534}
{"nam":"FORSTER, BARBARA","cit":"WAYZATA","st":"MN","zip":"55391","cnt":3587,"tot":176375}
{"nam":"CHASIN, MARK","cit":"WEST ORANGE","st":"NJ","zip":"07052","cnt":3536,"tot":24150}
{"nam":"ADAM, MONIQUE","cit":"OAKLAND","st":"CA","zip":"94611","cnt":3483,"tot":-955}
{"nam":"BAKER, GEOFFREY","cit":"SALEM","st":"OR","zip":"97302","cnt":3421,"tot":24716}
{"nam":"WEINRACH, ESTHER","cit":"NEWTOWN SQUARE","st":"PA","zip":"19073","cnt":3412,"tot":18730}
{"nam":"YOUNG, ADRIENNE","cit":"SEWICKLEY","st":"PA","zip":"15143","cnt":3368,"tot":30499}
{"nam":"ELLIOTT, MICHELE","cit":"SEWELL","st":"NJ","zip":"08080","cnt":3358,"tot":16836}
{"nam":"TREICHLER, PAULA","cit":"CHAMPAIGN","st":"IL","zip":"61820","cnt":3339,"tot":30224}
{"nam":"NANDI, JEAN B.","cit":"BERKELEY","st":"CA","zip":"94703","cnt":3326,"tot":8878}
{"nam":"MATTESON, DONALD","cit":"PULLMAN","st":"WA","zip":"99163","cnt":3325,"tot":6665}
{"nam":"BARNETT, MILES","cit":"HIGH RIDGE","st":"MO","zip":"63049","cnt":3287,"tot":22960}
{"nam":"WENZEL, DONNA","cit":"INDIANAPOLIS","st":"IN","zip":"46268","cnt":3281,"tot":33541}
{"nam":"SCHLESSINGER, LESLIE","cit":"PHILADELPHIA","st":"PA","zip":"19118","cnt":3248,"tot":10091}
{"nam":"COOK, HEIDI","cit":"BELLEVUE","st":"WA","zip":"98004","cnt":3244,"tot":21387}
{"nam":"CLOSE, JESSIE","cit":"BOZEMAN","st":"MT","zip":"59715","cnt":3240,"tot":5391}
{"nam":"TEBRAKE, NORMA","cit":"SUN CITY","st":"AZ","zip":"85351","cnt":3239,"tot":-336}
{"nam":"JOHNSTON PHD MBA, PROF. LLOYD","cit":"PINCKNEY","st":"MI","zip":"48169","cnt":3231,"tot":25954}
{"nam":"DUNHAM, WOLCOTT","cit":"PINE PLAINS","st":"NY","zip":"12567","cnt":3157,"tot":33898}
{"nam":"JEMIN, MARY","cit":"SILVER CITY","st":"NM","zip":"88061","cnt":3145,"tot":6664}
{"nam":"JOHNSON, LANA","cit":"MADISON","st":"SD","zip":"57042","cnt":3122,"tot":14801}
{"nam":"HAHN, FLETCHER","cit":"ALBUQUERQUE","st":"NM","zip":"87110","cnt":3114,"tot":49867}
{"nam":"TOOL, KATHY","cit":"CHAMPAIGN","st":"IL","zip":"61821","cnt":3105,"tot":28884}
{"nam":"OTTO, ROBERT","cit":"GREENSBORO","st":"NC","zip":"27407","cnt":3058,"tot":68210}
{"nam":"FEINKIND, CARLA","cit":"HINSDALE","st":"IL","zip":"60521","cnt":3052,"tot":45465}
{"nam":"HOROWITZ, DAVID","cit":"PORTLAND","st":"OR","zip":"97219","cnt":3028,"tot":33510}
{"nam":"BRENNAN, MARY L","cit":"MILLBURY","st":"MA","zip":"01527","cnt":3015,"tot":21890}
{"nam":"KING, MARY","cit":"FUQUAY VARINA","st":"NC","zip":"27526","cnt":3000,"tot":26289}
{"nam":"BUBENIK, PATRICIA","cit":"PALO ALTO","st":"CA","zip":"94301","cnt":2982,"tot":14938}
{"nam":"KATZ, RONALD","cit":"SAINT HELENA","st":"CA","zip":"94574","cnt":2961,"tot":15810}
{"nam":"MARTIN, FLOYD","cit":"SIMI VALLEY","st":"CA","zip":"93065","cnt":2917,"tot":4158}
{"nam":"FAIGIN, ANNE","cit":"VAN NUYS, CA","st":"CA","zip":"91406","cnt":2912,"tot":9320}
{"nam":"SMITH, STEVEN","cit":"MARSHFIELD","st":"MO","zip":"76501","cnt":2904,"tot":37755}
{"nam":"SHALLER, VIRGINIA","cit":"WEST PALM BEACH","st":"FL","zip":"33401","cnt":2864,"tot":37000}
{"nam":"OLSON, CATHERINE","cit":"SPRING VALLEY","st":"CA","zip":"91978","cnt":2834,"tot":31523}
{"nam":"GARDE, GAURI","cit":"RIVERSIDE","st":"CA","zip":"92506","cnt":2815,"tot":9951}
{"nam":"PENNINGTON, PHYLLIS A","cit":"MUNSTER","st":"IN","zip":"46321","cnt":2807,"tot":18288}
{"nam":"ZOSKI, GLENN","cit":"GREAT FALLS","st":"VA","zip":"22066","cnt":2785,"tot":13351}
{"nam":"HUGUENIN, ALVA","cit":"RANCHO MURIETA","st":"CA","zip":"95683","cnt":2784,"tot":23038}
{"nam":"GAULKE, JEAN","cit":"CHESTERTON","st":"IN","zip":"46304","cnt":2772,"tot":21175}
{"nam":"SMITH, CHARLES","cit":"OLYMPIA","st":"WA","zip":"98501","cnt":2747,"tot":10595}
{"nam":"BITONDO, JAMES","cit":"SEATTLE","st":"WA","zip":"98178","cnt":2743,"tot":39158}
{"nam":"GODBOLD, GARY","cit":"FORT WORTH","st":"TX","zip":"76116","cnt":2741,"tot":37481}
{"nam":"MARSH, ROXANNE","cit":"CONCORD","st":"NH","zip":"03301","cnt":2740,"tot":8384}
{"nam":"BALES, DORLAN","cit":"WICHITA","st":"KS","zip":"67204","cnt":2729,"tot":-9722}
{"nam":"WHEAT, MARY","cit":"ASTORIA","st":"NY","zip":"11102","cnt":2700,"tot":24717}

Check the earmark notes for the names.  There are lots of different notes there, many different, even for the same name and same date. Not sure what it means, just pointing it out.


ETA:

Attached File



Attached File

Also, to play devil's advocate, I'll say it'd be wise to check for right-leaning orgs too, especially if you've donated, to see if there's a baseline.

Off the cuff, the actblue data is way suspicious but I also don't have details about how they aggregate things, etc.  

Now I'm off again to finish watching Shogun....

That's not what we're doing. We're not trying to doxx people.

Democrats exist and they're allowed to hold their beliefs. We are looking for fraudulent money raising/laundering.

GROUP BY Name, COUNT(*), SUM(Amount)
HAVING COUNT(*) > 2

People can hold various beliefs, that's totally fine.

Money laundering to fund centralized activities underground is not fine.

Edit: the most simple thing you could do, would be to search yourself. Obviously you'd know if you made donations.

Yeah that's generally what I did; see above.



Yeah it's hard to say anything definitively without knowing a lot more how each org solicits donations and so on.
I think there's some interesting things you could do with the OTHER_ID field from the data dump, but I don't fully understand it.

22307 (Alexandria, south of the Beltway) has some exact examples of the pattern.

Am I wrong or do we basically just need to pull a download for our ZIP Code, then run a pivot table to summarize individual donors by the number of donations they make, and then look at the top 10% by donation quantity?

I just found a weird one in my county.

One retired lady, who looks to be in her 70s, has donated 1,067 times 2023-2024.

It makes you wonder if perhaps it's not the people, but some kind of automated tool to take money and "donate" it under the guise of random people.  The API to move the money is one thing, but a script to grab random people and update things like job title, etc. is not a heavy lift at all.

It's making me think about a lot of things.

That said, it's unwise to assume it's definitely fraud until more information is had.  Right now it seems suuuuuuper fishy, though.

Not to pick on the elderly but I wonder if they aren’t also more prone to getting tricked into auto-recurring donations

Another good point.

Something else to consider, and mind you this is off-the-cuff and I don't have the information/portal in front of me, is the possibility of the donation system/API itself rolling up all donations within a period via certain portal to a particular date.  If that's unclear, I'll give a scenario:

Say John Doe donates to Act Blue.  He has a favorite coffee shop or some kind of rewards card he uses frequently that has a "roll up" program where a charge will be rolled up and the difference, often rounded up, will be donated.

Say John Doe goes to the coffee shop 20-25 times in a month or something, buys his coffee or whatever.  The system that captures the donations may only make a submission on a certain date and may not transmit the actual donation date.  So if coffee shop X sees John 20 times in a month on different dates, but their system only communicates with Actblue or whatever other system only once a month, like on the 30th, then those multiple smaller donations could be rolled up to the same day.

BUT - this has potential pitfalls, and again, I'm not in front of the data.   If there are, say, more small transactions than there are days in the month than that theory is dubious.  

There's more to this but I'm getting sidetracked but understanding these systems is crucial since sometimes seemingly suspicious behavior can be explained by perfectly rational explanations.  Whether that truly applies here or not, I don't know -- I haven't done a deep-dive into this, just making points to consider for analysis.

I have a lot on my plate today; I'll take a look later if time permits.

Yeah, twice a day, almost every day for almost two years is pretty damned fishy.

That is exactly what I think it is. I think it's money laundering software

186 in my zip code. 54 by the same person in weird small amounts under $10 ea.

That person's FB has posts from family members saying his profile was hacked. Interesting.

There's a guy in my zip code with over 800 donations (most $1-$5, largest $25) in 2023 and a woman with over 700 in 2023.

Weird.

I know

I was just posting something I'd found about someone I know.

How do we bulk download the data?
Nvm, view is better on PC.  Found what I need

I realize what this thread needs. It needs more people reporting a guy who is unemployed for having a lot of unreported income. The IRS has a tip line for reporting tax cheats. If a college student or unemployed person are anyone is donating stupid amounts to Act blue the odds are they have unreported income. The IRS loves unreported income.

While that might be fun it wouldn't help the cause. Because if the IRS found that money was being laundered through the unwitting person for the Democrats they'd just close the case and bury the whole thing.

As was I. I have no intention of doxxing anyone. Simply pointing out that I recognize several names in my town who I know are leftists, and am finding nothing suspicious.

I see lots of repeat donor small donations to winred in my zip code and some for actblue.
So both teams are scamming on donations?

Nothing suspicious is a totally okay result.

That's why I'm curious if people are seeing it or not. Normal people who are Democrats are fine, they get to believe and donate to whatever they want.