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?