3 Ways AI is Making Wealth Screening More Effective

The nonprofit sector is embracing artificial intelligence. In fact, more than 80% of respondents in a recent report from Blackbaud Institute shared that they’re currently using some form of AI. While much of the focus is on generative AI tools like Copilot and ChatGPT, AI’s impact for nonprofits goes far beyond content creation.

One area seeing significant transformation is wealth screening—a critical part of prospect research that has long helped nonprofits identify major gift opportunities. Despite its proven impact, wealth screening has historical limitations that AI is well positioned to help organizations overcome.

Below, we‘ll explore three powerful ways that AI can modernize wealth screening to make it more efficient, insightful, and actionable for fundraising and research teams.

AI Automates Wealth Screening for Nonprofits

There are plenty of guides that cover “when to conduct a wealth screening.” Depending on your organization, the answer has probably been:

• The start or end of fiscal year
• Prior to kicking off a large campaign
• Immediately following a large event

Or, if your answer was “when we have the time and budget to do so,” that’s also fair. Determining when to leverage a one-time screening service, compiling submission files from multiple internal databases, and importing the results are burdensome challenges for many organizations.

The ideal strategy has always been continuous wealth screening of new and existing prospects. This approach allows your team to be both proactive and reactive: identifying major gift potential in newer supporters while also spotting changes in capacity or relevant wealth characteristics among existing donors.

Fortunately, modern artificial intelligence tools can automate periodic screenings of entire databases as well as trigger-based screening of individuals (think: change in address, new giving activity, etc.). AI also brings behind-the-scenes process improvements that allow vendors to handle larger volumes of data than previously possible.

AI Adds Context to Wealth Data

In order to be truly useful, wealth and asset data needs the context from an individual’s broader background and relationship to your organization. Context gives weight to bits of information that may otherwise seem unimportant or unsurprising. There are many types of context that affect how wealth data should be viewed, including:

• Past giving
• Life stage
• Current engagement track
• Behavioral traits

Imagine a nonprofit prospect researcher reviewing a donor’s profile. The data shows the donor owns a piece of real estate. On its own, this information might simply confirm the donor’s current giving capacity. But let’s add context:

AI-powered wealth screening tools uncover that the property is far from their primary address and was inherited, not purchased. Further, its previous owner was a past supporter of the researcher’s organization. These details signal a higher likelihood that the donor may be open to planned or blended giving options, such as donating the property through a bequest or establishing a charitable trust.

With this context, the conversation shifts. Instead of affirming the donor’s ability to give today, the nonprofit can engage the donor in a discussion about legacy giving—unlocking opportunities for a transformative gift that benefits both the donor and the organization.

I think this is an especially apt example given that aging baby boomers will soon transfer an estimated $45 trillion to younger generations and nonprofits—the largest transfer of wealth in American history.

The great thing about living in this age of rapidly-created data is that context is abundant. The challenge is that it can be too much of a good thing: there’s so much context that humans can’t effectively sift through it—at least not without some help. Artificial intelligence tools offer that helping hand by constantly assessing wealth data against large volumes of contextual data, guiding human staff members to what’s most relevant and why it deserves attention.

AI Improves Identity Resolution

Identity resolution refers to the process of accurately and reliably matching constituent data across multiple data sources. It’s a foundational component of any wealth screening service. Inconsistent data, multiple surnames or nicknames, formatting, and even typos are just some of the factors that make identity resolution difficult to do well at scale.

Wealth screening services typically consolidate different types of data from various data vendors, so consistent matching across all sources is critical to ensure you are getting a balanced and accurate view of your constituents’ wealth.

In the context of major giving, we know that individuals often take measures to hide wealth. As little as 20% of wealth is publicly identifiable, making effective identity resolution even more critical.

AI has the potential to revolutionize this process by automating and improving the accuracy of data matching through modernized machine learning algorithms. These tools provide quicker identification and resolution of inconsistencies. Notably, identity resolution can even fill in the blanks with just an email address or phone number, whereas a physical mailing address has long been a requirement for traditional wealth screening services.

Embracing AI for Smarter Prospect Research and Fundraising

As AI continues to evolve, its impact on wealth screening will only grow. It’s already making prospect research more efficient, insightful, and actionable. By embracing automation, contextual insights, and improved identity resolution, you can unlock new opportunities for major gifts and the long-term donor relationships that fuel your mission.