Most donor scoring checks quietly pull from current database records instead of historical snapshots, letting future giving leak into past predictions. When models are evaluated with data they have already seen, accuracy metrics can look artificially inflated while real conversion rates fall flat. A true validation requires rebuilding inputs strictly as of a cutoff date to spot leaks before gift officers invest time in misleading portfolios.
Can a small, budget-conscious prospect development shop build its own secure AI tool? In the latest issue of Connections, Nikolai Shchelkov, MSIS, MBA, shares how he designed and tested a secure, offline agentic AI prototype at Eastern New Mexico University. Running entirely on a single laptop with zero budget, this tool allows researchers to ask natural-language questions of their donor giving history and get immediate, calculated results without risking sensitive constituent data.
Is AI in fundraising a powerful tool or a privacy risk? A casual Apra Exchange Zoom discussion turned into a serious Apra-PA roundtable on AI in prospect development. Learn why data privacy and policy gaps could be exposing your organization.