Machine Learning Applications in Non-Profit Organizations: Enhancing Strategy, Efficiency, and Impact
Author: Dr. Anna Neya Kazanskaia
Publisher: NEYA Global Publishing
Article | NEYA Global Journal of Non-Profit Studies
Year: 2025
ORCID: https://orcid.org/0009-0009-5669-1676
DOI: https://doi.org/10.64357/neya-gjnps-adv-dt-an-04
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About the Article
Machine learning (ML) empowers non-profit organizations to transform complex datasets into actionable insights, automating routine tasks, identifying patterns among donors and volunteers, and supporting predictive forecasting. This article examines supervised and unsupervised learning methods—including decision trees, neural networks, clustering, anomaly detection, and dimensionality reduction—and demonstrates applications in donor engagement, fraud detection, and service optimization. Illustrative cases highlight measurable benefits such as improved donor retention, operational efficiency, and enhanced decision-making clarity. Adoption challenges, including data quality issues, limited infrastructure, and staff capacity gaps, are addressed with practical strategies: pilot projects, cloud-based and open-source tools, and staff training. By embedding ML into operational processes, non-profits can transition from reactive to adaptive practices, ensuring more effective resource allocation, stronger accountability, and greater social impact.
Key Topics
- Machine learning in non-profit organizations
- Supervised and unsupervised learning methods
- Anomaly detection and donor segmentation
- Operational efficiency and resource optimization
- Predictive modeling and forecasting
- Data-driven strategic decision-making
Suggested Citation
Kazanskaia, A. N. (2025). Machine Learning Applications in Non-Profit Organizations: Enhancing Strategy, Efficiency, and Impact. NEYA Global Journal of Non-Profit Studies. Neya Global Publishing. https://doi.org/10.64357/neya-gjnps-adv-dt-an-04
References
| Brynjolfsson, E., & McElheran, K. (2016). The rapid adoption of data-driven decision-making. American Economic Review, 106(5), 133-139. https://doi.org/10.1257/aer.p20161016 | ||||
| Chambers, R. (2014). Rural development: Putting the last first. Routledge. https://doi.org/10.4324/9781315835815 | ||||
| Ebrahim, A. (2003). Accountability in practice: Mechanisms for NGOs. World Development, 31(5), 813-829. https://doi.org/10.1016/S0305-750X(03)00014-7 | ||||
| Floridi, L., & Taddeo, M. (2016). What is data ethics? Philosophical Transactions of the Royal Society A: Mathematical, https://doi.org/10.1098/rsta.2016.0360 | ||||
| Kazanskaia, A. N. (2025). Advanced Data Analytics Techniques for Social Impact. NEYA Global Publishing. https://doi.org/10.64357/advanced-data-analytics-social-impact-2025 | ||||
| Kazanskaia, A. N. (2025). Data Analysis: Turning Information into Insight. NEYA Global Publishing. https://doi.org/10.64357/data-analysis-insight-2025 | ||||
| Kazanskaia, A. N. (2025). Quantitative Research Methods. NEYA Global Publishing. https://doi.org/10.64357/quantitative-research-methods-2025 | ||||
| Kazanskaia, A. N. (2025). Visualizing Impact: Data Communication Strategies. NEYA Global Publishing. | ||||
| Kazanskaia, A. N. (2025). Predictive Analytics in Non-Profit Organizations: Applications, Models, and Strategic Implications. NEYA Global Journal of Non-Profit Studies. https://doi.org/10.64357/neya-gjnps-adv-dt-an-03 | ||||
| Kazanskaia, A. N. (2025). Teaching Paper: Machine Learning Resource List - Building Non-Profit Capacity in Data-Driven Innovation. NEYA Global Journal of Non-Profit Studies (Teaching Paper). https://doi.org/10.64357/neya-gjnps-adv-dt-an-tp-02 | ||||
| Physical and Engineering Sciences, 374(2083), 20160360. | ||||
| James, G., Witten, D., Hastie, T., & Tibshirani, R. (2021). An introduction to statistical learning with applications in R (2nd ed.). Springer. https://doi.org/10.32614/CRAN.package.ISLR2 | ||||
| Patton, M. Q. (2015). Developmental evaluation: Applying complexity concepts to enhance innovation and use. Guilford Press. | ||||
| Salamon, L. M. (2014). The resilient sector revisited: The new challenge to nonprofit America. Brookings Institution Press. https://doi.org/10.5040/9780815750949 | ||||
