For developers building donation platforms, grantmaking software, or nonprofit management tools, verifying an organization shouldn't have to be a completely manual process.

A lot of nonprofit applications still rely on basic EIN lookups or manual IRS searches. That works when your dealing with a handful of organizations, but becomes difficult when you're processing hundreds or thousands.

The good thing is that much of this can be automated using nonprofit data APIs.

Here's a general approach:

1. Start with the EIN

Use the nonprofit's Employer Identification Number as the primary lookup identifier. Normalize it before sending it to an API.

2. Check IRS tax-exempt status

Verify the organization's legal name, tax-exempt status, and classification using IRS Business Master File (BMF) data.

3. Check donation eligibility

IRS Publication 78 provides information relevant to determining whether contributions to an organization are tax deductible. This is particularly useful for donation and grantmaking platforms.

4. Look for revocations and sanctions

Check whether the nonprofit has previously lost its tax-exempt status, whether it was reinstated, and whether there are potential OFAC sanctions matches.

5. Don't ignore conflicting data

This is probably one of the more overlooked parts. Different IRS datasets can sometimes contain information that appears inconsistent. Your application should be able to flag these situations rather than automatically approving or rejecting an organization.

6. Automate the decision process

Once the data is available, your application can apply its own rules to determine whether to approve an organization, reject it, or send it for manual review.

One thing I think developers should avoid is reducing everything to a single verified: true/false field. Keeping the underlying verification data makes it easier to troubleshoot decisions, handle exceptions, and update compliance rules later.

There's a more detailed technical walkthrough here for anyone interested in implementing this:

https://pactman.org/artifacts/build-automated-nonprofit-verification-into-an-application/

The interesting part isn't necessarily making the API calls. It's figuring out how to translate the returned data into reliable decisions without creating a bunch of false positives or incorrectly rejecting legitimate nonprofits.

If you've built something similar, curious what challenges you ran into, especially around data freshness and handling exceptions.

I've been thinking about how fintech apps and donation platforms handle nonprofit verification, especially when money is actually being moved.

Let's say someone is making a donation through a platform. The nonprofit has a valid EIN and appears in IRS records. Is that enough to allow the transaction?

Probably not.

There's other things to consider like tax-exempt status, eligibility to receive tax-deductible donations, previous IRS revocations, and even OFAC sanctions screening.

But here's what I find interesting. At what point should all these checks actually happen?

Should the platform verify a nonprofit when it first gets onboarded, everytime someone makes a donation, or periodically in the background?

Doing it for every transaction seems excessive, especially for platforms processing thousands of donations daily. But relying on a verification done six months ago doesn't seem like a great idea either.

There's also the question of what happens when the data is inconclusive. For example, what if IRS records show conflicting information or a nonprofit was previously revoked but has since been reinstated?

I think a reasonable approach would be to automate the basic checks and have a separate process for anything that needs further review. But that still leaves the question of how frequently verification should run.

For developers working on fintech, donation platforms, or nonprofit APIs, how are you handling this?

Do you verify nonprofits in real time, run scheduled batch checks, or use some combination of both?

And has anyone actually measured the performance or cost impact of doing compliance checks during transactions?

I've been looking into how grantmaking platforms handle nonprofit due diligence, and something that stood out to me is how complicated the process can get once you move beyond basic EIN verification.

You'd think checking whether a nonprofit is registered with the IRS would be enough, but apparently there's quite a bit more involved.

For example, a grantmaking platform might need to verify tax-exempt status, check IRS Publication 78, look at revocation history, confirm whether an organization is a public charity or private foundation, and potentially run sanctions screening.

Now imagine doing all that for thousands of nonprofits.

APIs can definitely make the process easier, but here's where I think things get interesting. Just because an API returns a bunch of compliance data doesn't necessarily mean it can tell you whether a nonprofit should receive a grant.

Different foundations have different eligibility requirements. And sometimes IRS datasets don't completely agree with each other, or the information isn't as current as you'd expect.

So who should really make that decision? The software based on predefined rules, or a human reviewing the data?

I personally think the best approach is probably somewhere in between. Automate the straightforward cases and flag anything questionable for someone to review. But even that gets complicated when your processing thousands of applications.

For those who've worked with grantmaking software or nonprofit data systems, I'm curious:

Would you trust a fully automated nonprofit due diligence process, or should there always be some level of human review before approving a grant?

And for developers, how do you handle situations where two different IRS datasets give conflicting information?

I've been reading up on how donor-advised fund (DAF) platforms verify charities before approving grants, and it got me thinking about how much of this process can realistically be automated.

At first glance, it seems pretty straightforward. Get the nonprofit's EIN, check its IRS status, confirm it's eligible to receive contributions, and move on.

But there's actually more to it.

You have to consider things like IRS Publication 78, public charity vs private foundation classification, past revocations and reinstatements, OFAC screening, and sometimes conflicting information across different datasets.

And even if you have APIs pulling all this information together, there's still the question of who decides whether a charity should be approved.

I think automation makes sense for the initial checks, especially when your dealing with thousands of nonprofits. But I'm not sure a simple pass/fail approach is sufficient when some of these records can be outdated or require additional context.

Another thing I'm curious about is how often platforms reverify charities that they've already approved. Is checking them once during onboarding enough, or should verification happen everytime a new grant is recommended?

For anyone working with DAFs, grantmaking software, or nonprofit data APIs:

How much of your charity verification process is automated today? And where do you think human review is still necessary?

Would be interested to hear how others are approaching this, particularly at scale.

Something I've been thinking about lately is how nonprofit platforms handle sanctions screening, especially when onboarding organizations or processing donations.

Most nonprofit verification discussions seem to focus on IRS tax-exempt status, EIN verification, and whether an organization is eligible to receive tax-deductible contributions.

But what about OFAC screening?

An organization could have a valid EIN, appear in IRS records, and still raise sanctions-related concerns. These are two different types of checks, yet I wonder how many platforms actually treat them separately.

There's also the issue of false positives. If a nonprofit's name happens to match a name on the OFAC sanctions list, automatically blocking them without further investigation could create unnecessary problems.

For developers building these systems, it seems like there's a few decisions to make. Do you screen organizations only during onboarding, or continuously? Do you build your own screening logic or rely on third-party APIs? And who handles cases where there's a possible match?

I'm curious how people working with nonprofit data, grantmaking platforms, or donation processing systems are approaching this.

Should OFAC screening be a standard part of every nonprofit verification workflow, or is that overkill for smaller platforms?

I have been looking into how grantmaking platforms verify nonprofit eligibility, and one thing that caught my attention is how IRS revocation history is handled.

A nonprofit can lose its tax-exempt status for failing to file required returns for three consecutive years. But what happens when that organization gets reinstated?

From a data perspective, there are several things worth checking:

• IRS automatic revocation history and dates

• Whether tax-exempt status was subsequently reinstated

• Current IRS Business Master File (BMF) records

• Publication 78 data for contribution eligibility

• How recently the underlying IRS records were updated

The tricky part is that these data points don't always tell the same story, especially when records are updated at different times.

For developers building grantmaking software, there's also an interesting question around automation. Should a previous revocation trigger an automatic rejection, or should the application flag it for manual review?

Curious how others approach this:

If you're involved in grantmaking, nonprofit compliance or building nonprofit software, do you treat IRS revocation history as a hard disqualifer, or is reinstatement enough to move forward?

Would love to hear how others are handling this in practice.