Grants-sorting tool opens up new insights for funders
Posted on 01 May 2025
By Matthew Schulz, journalist, SmartyGrants
A pioneering automatic grants classification tool will give funders across Australasia the power to reveal funding patterns and gaps.
After several years of development and testing by data scientists and developers, the artificial intelligence-powered tool is now integrated into the SmartyGrants platform, which channels more than $1 billion in grants each year.
SmartyGrants executive director Kathy Richardson said the new tool, being rolled out as part of a suite of new analytics functionality, would allow Australian and New Zealand grantmakers to get instant insights about their funding patterns.
“Auto-classification enables SmartyGrants users to instantly categorise past and current grants according to our CLASSIE (Classification of Social Sector Initiatives and Entities) taxonomy,” Richardson said.
“It’s the latest effort towards fulfilling our grantmaking ‘manifesto’, which seeks to maximise the value of grants, minimise waste, build the professionalism of practitioners, and ensure grants are driven and underpinned by outcomes and ethics.”
How does the automatic grants classification tool work?
The algorithmic system works by swiftly “reading” grant applications and assigning subject and population labels defined by CLASSIE, the social sector "dictionary”.
Developed by SmartyGrants’ parent company, Our Community, CLASSIE allows classification of the social sector by organisation, subject, population, activity, and transaction type. The auto-classification tool calls on the subject and population sections of the taxonomy.
CLASSIE has been adopted by a number of not-for-profit support bodies, including the Australian Charities and Not-for-Profit Commission’s online charity search portal.
The taxonomy, which covers both Australian and New Zealand contexts, has been available to SmartyGrants users for several years, but until now users were required to assign CLASSIE labels manually when filling in, assessing or managing forms.
The keyword-matching algorithm removes the need for human classification.
New tool gives more power to grantmakers
Our Community chief data scientist Dr Paola Oliva-Altamirano said grantmakers could use the new auto-classification tool to categorise all current and past grant records, or specific programs or rounds.
Results are available instantly via labels applied to each classified application form, while collective results are shown on the grantmaker’s dashboard and via exportable reports, she said.
Grantmakers are able to examine aggregated views of:
- Applications by subject
- Applications by beneficiary
- Total funding by subject
- Total funding by beneficiary
- Funding split by subject
- Funding split by beneficiary.
Dr Oliva-Altamirano said the system had been set up to give grantmakers extensive control, including deciding what sections of CLASSIE to use, and how granular results should be.
“We put the power in the hands of the grantmaker because only they know what view of the data they need,” Dr Oliva-Altamirano said.
“For example, you may be a general grantmaker who is interested in applying the entire subject classification to see how much money you’re putting towards the arts versus sports versus the environment.
“Or you might be an arts-only grantmaker, who only wants to apply the arts portion of the taxonomy, but you want a lot of granularity – you want to know what proportion of your budget went to ballet versus contemporary dance versus hip hop.
“Context is everything. Only you know what view will be most meaningful for the people who view your reports (and maybe that view will change on a daily basis, depending on the audience for the results). We’ve turned control over to you. You can reclassify your data however you like and as many times as you like.”
Users can also decide how many labels to apply.
“Some grantmakers will want to see only one label for subject and one for beneficiaries per application,” Dr Oliva-Altamirano said. “That allows you to produce a nice pie chart; however, forcing the tool to make just one selection makes it really hard to get an accurate result. The algorithm can’t know what is the “right” result or the most important result for you.”
System tweaked to improve ethical dimensions
Dr Oliva-Altamirano said the data science team was aware of the ethical dimensions of using algorithms, and the tool’s design reflected that.
“The problem with machine-learning algorithms is that they can learn from and then reinforce the biases that exist in the world,” says Dr Oliva-Altamirano. “In the early days of development, we were seeing some results coming through that reinforced harmful racial stereotypes, but it was very difficult to control the bot.”
Dr Oliva-Altamirano said the team switched to using a keyword-matching algorithm in part because it allowed the data science team to correct for biases.
“We rely on our users to tell us when they see something unfair in the results so we can keep improving the algorithm for future users.”