Kyle Mack

CEO of @MiddeskHQ

[Kyle Mack @KyleTMack]

Yesterday we posted what @MiddeskHQ found when we cross-referenced the @DOGE_HHS dataset against our business identity infrastructure.

300K+ views later, people are asking: what does this fraud actually look like up close? 🧵

[Kyle Mack @KyleTMack]

HHS just open-sourced the largest Medicaid dataset in history. About $1T in claims data, free for anyone to analyze via @DOGE_HHS. Everyone's looking at what was billed. At @MiddeskHQ, we're looking at who's behind the billing.

Here's what we found 🧵

[Kyle Mack @KyleTMack]

Middesk just hit 70,000 all-time businesses registered for state tax accounts.

Q1 2026 is +180% Y/Y.

[Kyle Mack @KyleTMack]

Behind every great yes is the new @MiddeskHQ
https://t.co/zwWmSzmnU6

[Kyle Mack @KyleTMack]

Had a great chat with @ReggieCYoung about all things business identity, AI, and the future of @MiddeskHQ

[Middesk @MiddeskHQ]

AI models aren't the constraint for business identity. Business data is.

Middesk CEO Kyle Mack joined Reggie Young on Fintech Layer Cake (presented by Lithic) to talk about why automation in fintech depends on the quality of the identity layer beneath it.

Middesk works with roughly 400 government agencies across the U.S. to bring primary-source business data into onboarding and risk workflows. Without complete, reliable data, it's difficult to distinguish a fraudulent entity from a legitimate company that incorporated three days ago. And impossible to automate decisions with confidence.

That layer has to be right before anything else can scale.

https://t.co/RvYK0F05tV

[Kyle Mack @KyleTMack]

@typesfast What do you think will be the ways fraud will happen through the broader refund process?

[Kyle Mack @KyleTMack]

@imthemusic This is only one narrowly defined fraud pattern related to paying out to blacklisted providers

[Kyle Mack @KyleTMack]

@DOGE_HHS @MiddeskHQ 300K+ views later, people are asking: what does this fraud actually look like up close?

https://t.co/6TQTqtSwx6

[Kyle Mack @KyleTMack]

Yesterday we posted what @MiddeskHQ found when we cross-referenced the @DOGE_HHS dataset against our business identity infrastructure.

300K+ views later, people are asking: what does this fraud actually look like up close? 🧵

[Kyle Mack @KyleTMack]

HHS just open-sourced the largest Medicaid dataset in history. About $1T in claims data, free for anyone to analyze via @DOGE_HHS. Everyone's looking at what was billed. At @MiddeskHQ, we're looking at who's behind the billing.

Here's what we found 🧵

[Kyle Mack @KyleTMack]

@dpatil @bgurley Couldn’t agree more

[Kyle Mack @KyleTMack]

At @MiddeskHQ we do this every day for banks and fintechs. The same tools can protect public programs.

DM me or reach out at [email protected]

[Kyle Mack @KyleTMack]

Yesterday we posted what @MiddeskHQ found when we cross-referenced the @DOGE_HHS dataset against our business identity infrastructure.

300K+ views later, people are asking: what does this fraud actually look like up close? 🧵

[Kyle Mack @KyleTMack]

HHS just open-sourced the largest Medicaid dataset in history. About $1T in claims data, free for anyone to analyze via @DOGE_HHS. Everyone's looking at what was billed. At @MiddeskHQ, we're looking at who's behind the billing.

Here's what we found 🧵

[Kyle Mack @KyleTMack]

This is one cluster.

Across our analysis, we found networks of providers totaling $1.7B in Medicaid payouts linked through shared addresses, officers, and formation patterns.

The truth is mapping these connections is difficult, it requires broad data access, tooling, and strong analytical research.

[Kyle Mack @KyleTMack]

@inferencepoint @DOGE_HHS @MiddeskHQ 100% the dive you did is great. Excited to keep following the rabbit hole!

[Kyle Mack @KyleTMack]

@bgurley Engineers in HHS like @zachterrell57, @nixonandrewg spoke with Axios on making the data public, obviously @elonmusk amplifying and helping to make this possible.

[Kyle Mack @KyleTMack]

@zachterrell57 Excited to dig in! @bgurley this might be your guy